CN117631532A - High-safety stable control method for vehicle-mounted magnetic suspension flywheel battery system based on interference factors - Google Patents
High-safety stable control method for vehicle-mounted magnetic suspension flywheel battery system based on interference factors Download PDFInfo
- Publication number
- CN117631532A CN117631532A CN202311368150.3A CN202311368150A CN117631532A CN 117631532 A CN117631532 A CN 117631532A CN 202311368150 A CN202311368150 A CN 202311368150A CN 117631532 A CN117631532 A CN 117631532A
- Authority
- CN
- China
- Prior art keywords
- interference
- working condition
- factor
- vehicle
- fuzzy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000725 suspension Substances 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000006073 displacement reaction Methods 0.000 claims abstract description 41
- 238000001914 filtration Methods 0.000 claims abstract description 13
- 230000009466 transformation Effects 0.000 claims abstract description 8
- 230000003321 amplification Effects 0.000 claims abstract description 3
- 238000003199 nucleic acid amplification method Methods 0.000 claims abstract description 3
- 230000001133 acceleration Effects 0.000 claims description 107
- 230000008859 change Effects 0.000 claims description 45
- 238000012937 correction Methods 0.000 claims description 22
- 230000004927 fusion Effects 0.000 claims description 21
- 239000011159 matrix material Substances 0.000 claims description 16
- 238000005096 rolling process Methods 0.000 claims description 10
- 238000004088 simulation Methods 0.000 claims description 9
- 101710163391 ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase Proteins 0.000 claims description 7
- 230000005484 gravity Effects 0.000 claims description 5
- 238000012885 constant function Methods 0.000 claims description 4
- 238000011156 evaluation Methods 0.000 claims description 4
- 238000005339 levitation Methods 0.000 claims description 4
- 230000000903 blocking effect Effects 0.000 claims description 3
- 238000013016 damping Methods 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 2
- 230000000875 corresponding effect Effects 0.000 description 18
- 230000006870 function Effects 0.000 description 16
- 238000010606 normalization Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 239000010985 leather Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/16—Mechanical energy storage, e.g. flywheels or pressurised fluids
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention provides a high-safety stable control method of a vehicle-mounted magnetic suspension flywheel battery system based on an interference factor, which comprises the steps of obtaining a difference between a displacement reference signal and an actual displacement signal when a safety working condition is interfered, inputting the difference into a fuzzy active disturbance rejection control module of a Butterworth filter to obtain a control current; the control current comprises radial control current, torsion control current and axial control current, the radial control current and the torsion control current are subjected to Clark inverse transformation and axial control current are respectively compensated after power amplification, so that three-phase control currents of a radial coil and a torsion coil and control currents of an axial coil are obtained, the control currents control corresponding coils on one hand, and on the other hand, a multi-information expansion Kalman filtering prediction module is input to output an actual displacement signal; when dangerous working conditions interfere, the output signals of the working condition identification and safety judgment module are cut off. The invention can improve the stability, robustness and anti-interference capability of the system.
Description
Technical Field
The invention belongs to the technical field of vehicle-mounted magnetic suspension batteries of new energy automobiles, and particularly relates to a control method of a vehicle-mounted magnetic suspension flywheel battery system.
Background
The vehicle-mounted magnetic suspension flywheel battery is a novel electromechanical integrated device based on the magnetic suspension bearing technology, breaks through the limitation of the traditional chemical battery, and has the advantages of high power density, high energy conversion rate, no pollution, long service life and the like. The magnetic suspension flywheel battery is used as an auxiliary, and is matched with the electric automobile to form a composite power supply system, so that the energy utilization efficiency can be obviously improved, the power performance of the electric automobile is improved, and the original vehicle-mounted power battery is protected.
Safety control aspect: at present, the research on the vehicle-mounted magnetic suspension flywheel battery only aims at the stability of the vehicle-mounted magnetic suspension flywheel battery under the safe working condition, under the dangerous working condition, the vehicle-mounted magnetic suspension flywheel battery system is impacted or even the outer shell of the vehicle-mounted magnetic suspension flywheel battery system is damaged due to the external impact or other extreme conditions, so that the normal operation of the flywheel is influenced, the flywheel running at high speed is damaged, the life of a driver is threatened to a great extent, the outer shell is damaged, and the effect of the protection measures of the flywheel through the outer shell is greatly reduced. Therefore, a method for judging the current operation condition of the magnetic suspension flywheel battery system is needed, and the flywheel battery is safely controlled under the condition that dangerous conditions are found.
Stability control aspect: the gyro effect of the flywheel rotor is obviously enhanced when the electric automobile runs under complex road conditions, the coupling between the degrees of freedom is also obvious, and in the process of the form of the electric automobile, the normal operation of a control system is interfered by external noise, various fine noises generated by the complexity of the mechanical structure of a sensor and the like, and a system with good performance is required by combining an observer, a filter and a control algorithm. The current common method is to control the magnetic suspension flywheel battery system by using a PID (Proportional, integral, differential) control system, and the adaptability is poor under the actual working condition of PID control due to nonlinearity and uncertainty when the magnetic suspension flywheel battery operates; han Jingqing teaches that the developed active disturbance rejection algorithm can better improve the stability performance, but the self-adaptability of the nonlinear error feedback link is poor, and the adjustment difficulty is high; the extended kalman filter algorithm (EKF) is one of the widely used filtering methods, and for a common EKF, only a single innovation is to estimate the prediction of a certain moment after linearization of the system by only adopting the state of the previous moment, and the data of all the previous moments are lost, which can cause a large prediction error.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the high-safety stable control method for the vehicle-mounted magnetic suspension flywheel battery system based on the interference factor, which not only can enable the vehicle-mounted magnetic suspension flywheel battery system to perform emergency treatment under dangerous working conditions, but also can improve the stability, the robustness and the anti-interference capability of the system.
The present invention achieves the above technical object by the following means.
A high-safety stable control method of a vehicle-mounted magnetic suspension flywheel battery system based on interference factors comprises the following steps:
the working condition recognition and safety judgment module judges the current working condition interference:
when the interference of the safe working condition is judged, the five-degree-of-freedom displacement reference signals are respectively differenced with the actual displacement signals, and the five-degree-of-freedom displacement reference signals are input into a fuzzy active disturbance rejection control module of the fusion Butterworth filter to obtain control current; the control current comprises radial control current, torsion control current and axial control current, the radial control current and the torsion control current are subjected to Clark inverse transformation and axial control current are subjected to power amplification and then are respectively compensated to obtain three-phase control current signals of a radial coil, three-phase control current signals of a torsion coil and axial control current signals of an axial coil, the control current signals control corresponding coils on one hand, and on the other hand, the control current signals are input into a multi-innovation extended Kalman filtering prediction module to output actual displacement signals and are subjected to difference solving with five-degree-of-freedom displacement reference signals;
And when the dangerous working condition interference is judged, cutting off the output signal of the working condition identification and safety judgment module.
Further, the working condition recognition and safety judgment module judges the current working condition interference by establishing a working condition recognition and safety judgment mechanism:
1) When the vehicle turns on one's side
Based on a vehicle roll dynamics differential equation considering an interference factor variable function, defining a rollover evaluation index LTR of the whole vehicle quality, wherein when the absolute LTR is <1, the current working condition interference is safe working condition interference, and when the absolute LTR is >1, the vehicle is rollover, and the current working condition interference is dangerous working condition interference;
the LTR is as follows:
wherein: m is m s A is the sprung mass of the vehicle, a ys Lateral acceleration, h, of sprung mass r For the distance of the sprung mass center of gravity to the roll center,for damping the roll of the suspension, < - > j->For side tilt angle +.>Is the roll angle speedDegree (f)>G is gravity acceleration, h is suspension roll stiffness g The mass center height is the mass center height, B is the wider the wheel track, and m is the mass of the whole vehicle; the interference factor variable function P (k) is:C 1 to the |A y Constant function of S y Is not influenced by normal lateral acceleration under the condition of road conditions and the environment, V car For the current acceleration of the vehicle, R is the turning radius, |A y The I is lateral acceleration, and the beta is a lateral ramp angle of the road;
2) When the vehicle is in rear-end collision or slipping
When the longitudinal acceleration A is detected x Or lateral acceleration |A y And when the I exceeds the threshold value, judging the current working condition interference as dangerous working condition interference, otherwise, judging the current working condition interference as safe working condition interference.
Still further, the operating mode interference is:
when longitudinal acceleration A x Normal change and lateral acceleration A y When normal change occurs, judging the working condition interference as working condition interference 1 and working condition interference 13; when longitudinal acceleration A x Normal change and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 3 and working condition interference 15; when longitudinal acceleration A x Normal change, lateral acceleration A y Normal change and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 7 and working condition interference 11; when longitudinal acceleration A x Is greatly changed and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 2, working condition interference 4, working condition interference 14 and working condition interference 16; when longitudinal acceleration A x Normal change and lateral acceleration A y When a large change occurs, judging the working condition interference as working condition interference 5 and working condition interference 9; when longitudinal acceleration A x Is greatly changed and the lateral acceleration A y Normal change and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 6, working condition interference 8, working condition interference 10 and working condition interference 12;
wherein: the working condition interference 1 is formed by combining a turning interference factor and a following interference factor, the working condition interference 2 is formed by combining a rolling interference factor and a following interference factor, the working condition interference 3 is formed by combining a gradient interference factor and a following interference factor, the working condition interference 4 is formed by combining a bumping interference factor and a following interference factor, the working condition interference 5 is formed by combining a turning interference factor and an obstructing interference factor, the working condition interference 6 is formed by combining a rolling interference factor and an obstructing interference factor, the working condition interference 7 is formed by combining a gradient interference factor and an obstructing interference factor, the working condition interference 8 is formed by combining a bumping interference factor and an obstructing interference factor, the working condition interference 9 is formed by combining a turning interference factor and a lane changing interference factor, the working condition interference 10 is formed by combining a rolling interference factor and a lane changing interference factor, the working condition interference 11 is formed by combining a bumping interference factor and a lane changing interference factor, the working condition interference 13 is formed by combining a rolling interference factor and an indicating interference factor, the working condition interference 14 is formed by combining a bumping interference factor and an indicating interference factor, the working condition interference 15 is formed by combining a gradient interference factor and an indicating interference factor, and the working condition interference 16 is formed by combining a bumping interference factor and an indicating interference factor.
Further, when the longitudinal acceleration A x Normal changes are included in the range of-0.3 g to 0g, large changes are included in the range of-0.5 g to-0.3 g, and dangerous changes are included in the range of more than-0.5 g; when the lateral acceleration A y Normal changes are included in the range of-0.2 g to 0.2g, large changes are included in the range of-0.45 g to-0.2 g and in the range of 0.2g to 0.45g, and dangerous changes are included in the range of less than-0.45 g and more than 0.45 g; where g refers to gravitational acceleration.
Further, the fusion Butterworth filter fuzzy active disturbance rejection control module specifically comprises:
1) The fuzzy control is introduced into ADRC, and the nonlinear state error feedback control rate is added into an error integration link:
wherein: e, e θ1 (k) Is a tracking signal of a given displacement error at time k, e θ2 (k) Is a differential signal of a given displacement error at time k, e z1 (k) A state tracking signal, e, which is the actual error at time k z2 (k) A differential signal, e, which is the actual error at time k z3 (k) Tracking signal for system disturbance at k moment beta 0 、β 1 And beta 2 The adjusting parameter of the nonlinear state feedback control rate is obtained by a fuzzy controller, alpha 0 、α 1 、α 2 Nonlinear factor, delta is the filter factor of the extended state observer, u (k) is the actual output voltage, fal () is a nonlinear function, e 0 (k) Is an integral signal, b is disturbance compensation;
e 1 (k)、e 2 (k) As a control input of the fuzzy controller, the output thereof is { beta } 0 、β 1 、β 2 Correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 }；
2) Introduction of interference factors into fuzzy reasoning
Classifying noise interference factors NIF, road interference factors RDF and traffic interference factors TDF, determining different interference intensities, and comparing beta with the interference intensity 0 、β 1 、β 2 Correcting to obtain a correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 -a }; then establish the correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 A fuzzy control rule table.
Still further, the fusion Butterworth filter fuzzy active disturbance rejection control module gives a displacement error e x As input to a tracking differentiator, a tracking signal e of a given displacement error is output θ1 Differential signal e for a given displacement error θ2 State tracking signal e of actual error with output of extended state observer z1 Differential signal e of actual error z2 Difference, error e of output rotation speed 1 Differential signal e of rotational speed 2 Respectively used as the input of nonlinear state error feedback control rate and fuzzy reasoning, and also for e 1 Integrating to obtain e 0 Inputting a nonlinear state error feedback control rate; after fuzzy reasoning is processed, delta beta is obtained 0 、Δβ 1 、Δβ 2 Also, as an input of the nonlinear state error feedback control rate, the output of the nonlinear state error feedback control rate sets the control amount u 0 (k) Tracking signal e perturbed from output system of extended state observer z3 Multiplying by 1/b, performing difference to obtain actual output voltage u (k), and inputting to controlled object to obtain control current i x * The method comprises the steps of carrying out a first treatment on the surface of the u (k) multiplied by b is input to the extended state observer, and the current i is controlled x * The signal is input to a Butterworth filter for filtering, and the processed signal is also input to an extended state observer.
Still further:
(1) The noise interference factor NIF is divided into three levels, which are respectively expressed as high, medium and low, and the fuzzy set on the corresponding domain of each level is expressed as:
high: noise interference factor NIF e [74, 90), the fuzzy set over this universe is expressed as:
in (a): noise interference factor NIF e 40,74), the fuzzy set over this universe is expressed as:
low: noise interference factor NIF e 0,40), the fuzzy set over this universe is expressed as:
(2) The road interference factor RDF is divided into three levels which are respectively expressed as high, medium and low, and the fuzzy set on the corresponding domain of each level is expressed as:
high: road interference factor RDF e (6, 8), the fuzzy set on this domain is expressed as:
in (a): road interference factor RDF e (3, 6), the fuzzy set on this domain is expressed as:
low: road interference factor RDF ε (0, 3), the fuzzy set over this universe is represented as:
(3) The traffic disturbance factor TDF is divided into five classes, which are respectively expressed as very high, medium, low and very low, and the fuzzy set on the corresponding domain of each class is expressed as:
very high: traffic disturbance factor TDF ε (8, 10), the fuzzy set over this domain is expressed as:
high: traffic disturbance factor TDF e (6, 8), the fuzzy set over this domain is expressed as:
in (a): traffic disturbance factor TDF e (4, 6), the fuzzy set over this domain is expressed as:
low: traffic disturbance factor TDF e (2, 4), the fuzzy set over this domain is expressed as:
very low: traffic disturbance factor TDF e [0,2], the fuzzy set on this domain is expressed as:
further, the multi-innovation extended kalman filter prediction module constructs a prediction equation set of the MIEKF according to a prediction part, an update part, a correction coefficient and a multi-innovation theory of the extended kalman filter:
wherein:is the prior value of the state moment of k moment being an array, u k-1 For the output variable at time k-1 of the system, m (k-p+1) represents the innovation at time k-p+1, I k-p+1 Time is a measurement of current at time k-p+1, y k-p+1 The predicted value of the current at the moment k-p+1, p is the innovation length, L k Is a Kalman gain matrix, < >>x k R is a state variable at the moment of a system k k For the covariance of the observed error, E is the identity matrix, P k|k Is a state error covariance matrix of update correction values,/>Is P k|k-1 Estimated value of P k|k-1 Is the state error covariance matrix at time k,>is an estimate of the correction value at time k.
Further, determining different vehicle type longitudinal acceleration thresholds by simulation; the lateral acceleration threshold is according to the formulaA determination is made, wherein: a, a y Is the actual threshold, a y * Is a dangerous threshold, B is the tread of the automobile, h g Is the centroid height and β is the lateral ramp angle of the road.
Further, the turning interference factor, the heave interference factor, the gradient interference factor and the bump interference factor belong to road surface interference factors, the following interference factor, the blocking interference factor and the lane changing interference factor belong to vehicle interference factors, the vehicle interference factor and the indication interference factor form traffic interference factors, and the traffic interference factors and the road surface interference factors form working condition interference factors.
The beneficial effects of the invention are as follows:
(1) The invention considers the complexity of the road in real life, the randomness of traffic and the interference of noise factors on the magnetic suspension flywheel battery, provides the interference factors, and subdivides the interference factors: a working condition interference factor and a noise interference factor; the working condition interference factors are divided into road surface interference factors and traffic interference factors; road surface interference factors are divided into turning interference factors, heave interference factors, gradient interference factors and bump interference factors; traffic interference factors are classified into a vehicle interference factor and an indication interference factor; the vehicle interference factors are divided into following interference factors, obstructing interference factors and lane change interference factors; the working condition interference factor set table is established, the current working condition can be judged at any time, the complexity of a control system is greatly reduced, and the stability of the magnetic suspension flywheel battery system is improved. And the noise interference factor, the road surface interference factor and the traffic interference factor are used as optimization factors and are introduced into fuzzy pushing of fuzzy active disturbance rejection control, and the reasoning rule is optimized, so that the vehicle-mounted magnetic suspension flywheel battery system which normally operates under complex working conditions and various interferences can be better controlled.
(2) The invention recognizes the current running working condition of the vehicle carrying the magnetic suspension flywheel battery system, and designs a working condition recognition and safety judgment module. The working condition identification and safety judgment module identifies the current working condition through the interference factor integrated table, if the identification is successful, the data of the integrated table is adopted to be output to the next link, if the identification is failed, the current longitudinal acceleration, the lateral acceleration, the current vehicle speed, the transverse slope angle and other running data are collected as the position working condition, an interference factor variable function is established, and the interference factor variable function is introduced into a rolling dynamics differential equation of the vehicle to calculate, so that whether the current working condition is a dangerous working condition is judged. The module uses the known road condition interference factor set table, does not need to calculate through a complex module, greatly reduces the complexity of the system, provides a processing scheme for coping with unknown working conditions according to actual complex conditions, improves the practicability and self-adaption capability of the module, can identify most complex working conditions by combining the two, integrates and outputs parameters, and greatly improves the reliability of the system.
(3) The invention provides a safety working condition judging module which considers whether the current running working condition of a vehicle carrying a magnetic suspension flywheel battery system is safe or not, and judges through the working condition identification and the data output by the safety judging module. Considering various vehicle types and the barycenter heights and wheel tracks thereof, setting the maximum acceleration threshold value (the set value is 90% of the maximum acceleration, and 10% margin is reserved for the system to react) by calculation, when the longitudinal acceleration A is detected x Or lateral acceleration |A y And when the I exceeds the threshold value, judging the current working condition interference as dangerous working condition interference, otherwise, judging the current working condition interference as safe working condition interference. The module can judge the safety of most working conditions, and greatly improves the safety and reliability of the magnetic suspension flywheel system.
(4) The invention combines the working condition identification and safety judgment module, the fusion Butterworth filter fuzzy active disturbance rejection control module, the current normalization compensation module, the multi-innovation extended Kalman filtering prediction module, the rotating speed observation and judgment module and the working condition disturbance observation module through multiplexing of current signals, so that the invention forms an operation state control with high stability, high disturbance rejection, high robustness, and high robustness, which can well solve the influence of external noise and internal noise on data, simplifies the complexity of the magnetic suspension flywheel battery system, strengthens the self-adaptability of the magnetic suspension flywheel battery system, and can keep the high stability, the high disturbance rejection and the high robustness under the diversity of vehicles, the complexity of road conditions and the randomness of traffic in real life. The deviation signal output by the working condition identification and safety judgment module is compounded with the signal output by the multi-innovation extended Kalman filter prediction module, the signals are used as input signals of the fuzzy active disturbance rejection control module and the working condition disturbance observation module, the working condition disturbance information is output by the working condition disturbance observation module, the output signal of the fuzzy active disturbance rejection control module is used as the input signal of the CLARK inverse transformation module, the output signal is input into the current normalization compensation module through the Hall current sensor, the current normalization compensation module compensates the disturbance generated when the signal passes through the Hall current sensor, the output signal of the current normalization compensation module is input into a corresponding coil for control, and the output signal of the current normalization compensation module is used as the input signal of the multi-innovation extended Kalman filter prediction module, so that all the modules are prevented from being piled in a control system, the complexity and the practicability of the control system are increased, and the real-time response capability of the control system is reduced.
(5) The control method refers to the Butterworth filter, processes the sensor signal, aims at the complexity of the mechanical structure in the sensor, avoids various fine noises in the working process, and improves the stability of the output signal by applying the strong inhibition capability of the Butterworth filter to peak noises.
(6) The Butterworth filter used in the control method is fused with fuzzy active disturbance rejection control, so that a large amount of shot noise, thermal noise, gaussian noise doped in the external environment and a large amount of vibration noise generated by a complex structure of the magnetic suspension flywheel battery system are greatly reduced, interference is caused to detected data, optimal estimation is carried out on signals, and the position control precision of the system is improved.
(7) The control method uses the multi-innovation theory to improve the extended Kalman filtering, greatly reduces the complexity of the road and the randomness of traffic, and improves the accuracy of the algorithm under the condition that the filtering accuracy is poor and the robustness is poor due to the fact that the nonlinear control system presents strong nonlinearity when the magnetic levitation flywheel system operates under the complex working conditions.
Drawings
FIG. 1 is a block diagram of a high-safety stable control method of a vehicle-mounted magnetic suspension flywheel battery system based on interference factors;
Fig. 2 is a schematic structural diagram of the fuzzy active disturbance rejection control module of the butterworth filter;
FIG. 3 is a detailed view of the fuzzy active disturbance rejection control module of the fused Butterworth filter according to the present invention;
FIG. 4 is a flow chart of dangerous condition determination and processing according to the present invention;
in the figure: the system comprises a 1-working condition identification and safety judgment module, a 2-safety working condition switching module, a 3-fusion Butterworth filter fuzzy active disturbance rejection control module, a 31-first fusion Butterworth filter fuzzy active disturbance rejection control module, a 32-second fusion Butterworth filter fuzzy active disturbance rejection control module, a 33-third fusion Butterworth filter fuzzy active disturbance rejection control module, a 34-fourth fusion Butterworth filter fuzzy active disturbance rejection control module, a 35-fifth fusion Butterworth filter fuzzy active disturbance rejection control module, a 311-tracking differentiator, a 312-fuzzy inference, a 313-nonlinear state error feedback control rate, a 314-Butterworth filter, a 315-expanded state observer, a 41-first Clark inverse transformation module, a 42-second Clark inverse transformation module, a 43-switching power amplifier, a 5-Hall current sensor, a 6-current normalization compensation module, a 7-multiple update expansion Kalman filter prediction module, an 8-controlled object, a 9-rotating speed and judgment module and a 10-working condition observation module.
Detailed Description
The invention will be further described with reference to the drawings and the specific embodiments, but the scope of the invention is not limited thereto.
As shown in FIG. 1, the high-safety and stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factors is realized based on the following modules: the system comprises a working condition identification and safety judgment module 1, a safety working condition switching module 2, a fusion Butterworth filter fuzzy active disturbance rejection control module 3, a current normalization compensation module 6, a multi-innovation extended Kalman filter prediction module 7, a rotating speed observation and judgment module 9 and a working condition disturbance observation module 10.
The safe working condition switching module 2 judges the operation mode by the working condition identification and safety judging module 1 and the rotating speed observing and judging module 9, if the safe working condition is interfered, information is transmitted to the following module, if the safe working condition is interfered, the output signal of the working condition identification and safety judging module 1 is cut off, and the working condition identification and safety judging module 1 is also used for judging whether the rotating speed observing and judging module 9 is operated or not.
When the dangerous working condition interference is judged, the safe working condition switching module 2 carries out reverse processing on the reference signal, meanwhile, the rotation speed observation and judgment module 9 starts to work, and when the rotation speed is 0rps, a command is sent out, so that the safe working condition switching module 1 cuts off the reference signal; if it is determined that the safe working condition is interfered, the safe working condition switching module 1 normally outputs the reference signal and the rotation speed observing and determining module 9 stops working, the safe working condition switching module 1 transmits the safe working condition information and the current road condition information to the working condition interference observing module 10, observes and records the current information, determines through experience of an experimenter, calculates the probability of occurrence of dangerous working conditions of the corresponding working condition interference (the number of times of occurrence of dangerous working conditions under the working condition/the number of occurrence of working condition interference), and performs safe prediction work for the subsequent magnetic suspension flywheel battery.
For the radial control part, a radial displacement reference signal (x * 、y * ) Error (e) from radial displacement prediction values (x, y) x 、e y ) Input into a corresponding fuzzy active disturbance rejection control module 3 of the fusion Butterworth filter to obtain a radial control current signal { i } x * 、i y * Radially controlling the current { i } x * 、i y * The three-phase radial control current signal { i } is obtained by a first Clark inverse transformation module 41 tA 、i tB 、i tC And { i } is detected by the hall current-sensor 5 tA 、i tB 、i tC }。
For the torsion control portion, a displacement reference signal (θx * 、θy * ) Error (e) from displacement prediction value (θx≡θy+) of torsion θx 、e θy ) Inputting the signals into a fuzzy active disturbance rejection control module of a corresponding fusion Butterworth filter to obtain torsion control current signals { i } θx * 、i θy * The torsion control current { i } θx * 、i θy * The three-phase torsion current signal { i } is obtained by passing through a second Clark inverse transformation module 42 tθA 、i tθB 、i tθC And { i } is detected by the hall current-sensor 5 tθA 、i tθB 、i tθC }。
For the axial control part, the axial displacement reference signal z * Error e from axial displacement prediction value z ≡, and z inputting the current into a fuzzy active disturbance rejection control module of a corresponding fusion Butterworth filter to obtain an axial control current i z * The output of the axial control current signal i for the axial coil is obtained via a switching power amplifier 43 tz 。
{i tA 、i tB 、i tC }、{i tθA 、i tθB 、i tθC }、i tz Is input to the current normalization compensation module 6 for the following operations:
three-phase control current signal for radial coil:
i A ＝i tA +i tonifying device ，i B ＝i tB +i Tonifying device ，i C ＝i tC +i Tonifying device
Three-phase control current signal for torsion coil:
i θA ＝i tθA +i tonifying device ，i θB ＝i tθB +i Tonifying device ，i θC ＝i tθC +i Tonifying device
Axial control current signal for axial coil:
i Z ＝i tZ +i tonifying device
Signal i of current A 、i B 、i C 、i θA 、i θB 、i θC 、i z As multiple informationThe input of the extended Kalman filtering prediction module 7 outputs actual displacement signals x, y, theta x, theta y and z, and then integrates with the reference displacement signals output by the working condition identification and safety judgment module 1 to serve as the input of the fusion Butterworth filter fuzzy active disturbance rejection control module 3, so that a closed vehicle-mounted magnetic suspension flywheel battery control method is formed.
As shown in fig. 2, the input signals of the first butterworth filter fuzzy active disturbance rejection control module 31, the first butterworth filter fuzzy active disturbance rejection control module 32, the first butterworth filter fuzzy active disturbance rejection control module 33, the first butterworth filter fuzzy active disturbance rejection control module 34 and the first butterworth filter fuzzy active disturbance rejection control module 35 respectively correspond to the radial displacement error e of the magnetic levitation flywheel battery x Error of radial displacement e y Error of torsional displacement e θx Error of torsional displacement e θy Axial displacement error e z 。
Taking the first Butterworth-fused fuzzy active disturbance rejection control module 31 as an example, as shown in FIG. 3, the radial displacement error e is calculated x As an input to the tracking differentiator 311, its output is e θ1 、e θ2 And output e of extended state observer 315 z1 、e z2 Difference, output e 1 、e 2 As inputs to nonlinear state error feedback control rate 313 and fuzzy inference 312, respectively, and for e 1 Integrating to obtain e 0 Input to nonlinear state error feedback control rate 313; fuzzy inference 312 is processed to derive Δβ 0 、Δβ 1 、Δβ 2 As an input of the nonlinear state error feedback control rate 313, it outputs a set control amount u 0 (k) And the output e of the extended state observer 315 z3 Multiplying by 1/b, performing subtraction to obtain actual output voltage u (k), and inputting to controlled object 8 to obtain radial control current i x * And u (k) multiplied by b is input to the extended state observer 315; radial control current i x * Is input to Butterworth filter 314 for filtering, and the processed signal is input to extended state observer 315 to form a signalA closed fusion Butterworth filter fuzzy active disturbance rejection control method.
As shown in fig. 4, when the vehicle-mounted magnetic suspension flywheel battery system starts to operate, acceleration information of a current vehicle is collected, a current working condition is judged through a change of acceleration and a working condition interference factor integration table, after the judgment is successful, safety judgment is carried out on current working condition interference through a rollover evaluation index LTR and an acceleration threshold value table, if the current working condition interference is the safety working condition interference, data are transmitted to a subsequent system, if the current working condition interference is the dangerous working condition interference, shutdown processing is carried out, if the dangerous working condition is processed, the current working condition is returned to the safe working condition again, normal operation of the magnetic suspension flywheel battery system is manually restored, and if the dangerous working condition is not processed, shutdown processing is carried out.
The working condition recognition and safety judgment module 1, the fusion Butterworth filter fuzzy active disturbance rejection control module 3 and the multi-innovation extended Kalman filter prediction module 7 are specifically described below.
1. Working condition recognition and safety judgment module 1 establishes working condition recognition and safety judgment mechanism of vehicle-mounted magnetic suspension flywheel battery system
(1) When the vehicle turns on one's side, the working condition recognition and the safety judgment are carried out by the following modes
Analyzing states of different types of vehicles under four road conditions of turning, rolling, ramp, jolting and traffic conditions of lane changing, blocking, following and signal lamps by utilizing five-degree-of-freedom model machine, dynamic experiment, ADAMS and CARSIM joint simulation, defining interference factors, establishing working condition interference factor set table as shown in table 1, carrying out combined analysis of road conditions and traffic conditions, and observing longitudinal acceleration A x Lateral acceleration A y Vertical acceleration A z And the change of the parameters.
TABLE 1 Integrated form of operating mode interference factor
Since the vehicle number of each vehicle is known, the vehicle is driven out according to different vehicles and different road conditions on which the vehicle is drivenThe possibility of the dangerous working conditions is different, and the analysis according to the joint simulation data shows that the lateral acceleration change under the turning road condition is larger; longitudinal acceleration and vertical acceleration under rough and bumpy road conditions are greatly changed; the vertical acceleration of the road condition of the ramp varies greatly; the longitudinal acceleration is greatly changed under the condition of traffic signal lamp or traffic police indication; the longitudinal acceleration and the lateral acceleration of the vehicle are greatly changed under the traffic conditions of being blocked by other vehicles and changing lanes; thus, it is possible to make reference to the current longitudinal acceleration A x Lateral acceleration A y Vertical acceleration A z To judge what working condition the current vehicle is in, to prepare data for subsequent safe working condition judgment, specifically as follows:
according to simulation data, it can be known that under daily road conditions and traffic conditions, the vertical acceleration A z The change in (2) has little effect on the safety of the vehicle and is not divided here. Will accelerate longitudinally A x Lateral acceleration A y Dividing into sections when the longitudinal acceleration A x Normal changes are included in-0.3 g to-0 g, large changes are included in-0.5 g to-0.3 g, and dangerous changes are included in the range of more than-0.5 g; when the lateral acceleration A y Normal changes are included in-0.2 g to-0.2 g, large changes are included in-0.45 g to-0.2 g and 0.2g to 0.45g, dangerous changes are included in less than-0.45 g and more than 0.45g, and g refers to gravitational acceleration. When longitudinal acceleration A x Normal change and lateral acceleration A y When the normal change occurs, the working condition interference can be judged to be working condition interference 1 and working condition interference 13; when longitudinal acceleration A x Normal change and vertical acceleration A z When the change occurs, the working condition interference can be judged to be working condition interference 3 and working condition interference 15; when longitudinal acceleration A x Normal change, lateral acceleration A y Normal change and vertical acceleration A z When the change occurs, the working condition interference can be judged to be the working condition interference 7 and the working condition interference 11; when longitudinal acceleration A x Is greatly changed and vertical acceleration A z When the change occurs, the working condition interference can be judged as working condition interference 2, working condition interference 4, working condition interference 14 and working condition interference 16The method comprises the steps of carrying out a first treatment on the surface of the When longitudinal acceleration A x Normal change and lateral acceleration A y When a large change occurs, the working condition interference can be judged as working condition interference 5 and working condition interference 9; when longitudinal acceleration A x Is greatly changed and the lateral acceleration A y Normal change and vertical acceleration A z When the change occurs, the working condition interference can be judged to be the working condition interference 6, the working condition interference 8, the working condition interference 10 and the working condition interference 12.
The roll dynamics differential equation of the vehicle is established as follows:
wherein: i x To moment of inertia about the x-axis, h g Is the height of the mass center, m s In the form of the sprung mass of the vehicle,for suspension roll stiffness>For damping the roll of the suspension, < - > j->For side tilt angle +.>For roll angle speed, ++>Roll acceleration, h r A is the distance from the center of gravity of the sprung mass to the roll center ys Is the lateral acceleration of the sprung mass.
From simulation, the current acceleration V of the vehicle car The larger the turning radius R, the larger the lateral acceleration |a y The larger the i, the higher the centroid height h of the vehicle g The larger the lateral ramp angle beta of the road is, the larger the disturbance factor variable function P (k) is; the wider the track B, the smaller the disturbance factor variable function P (k); variable function of variation of longitudinal acceleration and vertical acceleration to disturbance factorP (k) is not large and is therefore not considered. To sum up, the disturbance factor variable function P (k) and the current acceleration V of the vehicle car Turning radius R, lateral acceleration |a y Centroid height h of vehicle g The lateral ramp angle beta of the road is positively correlated and inversely correlated with the wider the tread is, the more the track width is. The interference factor variable function is as follows:
wherein P (k) is an interference factor variable function; c (C) 1 To the |A y The constant function of the I is different along with the different parameters of the vehicle and the current interference factors, and an empirical table or formula of the constant function can be obtained through experiment and simulation accumulation; s is S y Is normal lateral acceleration without being affected by road conditions and the environment.
The optimized roll dynamics differential equation of the vehicle is as follows:
after the corresponding disturbance factor variable function is added, the running environment of the vehicle-mounted flywheel battery system can be accurately judged according to different road conditions and traffic conditions, and safety accidents caused under actual dangerous working conditions are avoided.
The rollover evaluation index LTR defining the quality of the whole vehicle is as follows:
wherein: m is the mass of the whole vehicle.
When the absolute LTR is smaller than 1, the current working condition interference is safe working condition interference, and when the absolute LTR is larger than 1, the vehicle turns over, and the current working condition interference is dangerous working condition interference.
(2) When the vehicle is in rear-end collision or slipping, the working condition identification and the safety judgment are carried out by the following modes
According to industry using prototypes, ADAMS VIEW and CARSIMThe condition interference factor is combined with the working condition in the table to carry out simulation analysis, the dangerous working condition is judged and calculated by the arrangement data, and the lateral acceleration |A is obtained y Threshold of i.
As shown in table 2, when the longitudinal acceleration a is detected x Or lateral acceleration |A y And when the I exceeds the threshold value, judging the current working condition as dangerous working condition interference, and controlling the vehicle-mounted magnetic suspension flywheel battery to stop working.
TABLE 2 longitudinal acceleration thresholds for different types of vehicles
The risk threshold in table 2 is determined from the simulation.
TABLE 3 lateral acceleration thresholds for different types of vehicles
| Vehicle type | Centroid height/m | Track/m | Actual threshold/g | Dangerous threshold/g |
| A-type vehicle | 0.54 | 1.415 | 1.311 | 1.179 |
| B-type vehicle | 0.54 | 1.480 | 1.370 | 1.233 |
| C-shaped vehicle | 0.54 | 1.675 | 1.534 | 1.380 |
| Van-type recreational vehicle | 0.70 | 1.640 | 1.171 | 1.054 |
| D-type vehicle | 0.52 | 1.550 | 1.490 | 1.341 |
| E-type vehicle | 0.53 | 1.600 | 1.509 | 1.358 |
| Small SUV | 0.72 | 1.575 | 1.094 | 0.984 |
| Van type truck | 0.71 | 1.500 | 1.056 | 0.951 |
| Large van | 0.71 | 1.550 | 1.092 | 0.982 |
| Sports car | 0.375 | 1.650 | 2.200 | 1.980 |
| Racing car | 0.375 | 1.650 | 2.200 | 1.980 |
| F-shaped vehicle | 0.590 | 1.605 | 1.360 | 1.224 |
| Leather card | 0.665 | 1.550 | 1.165 | 1.049 |
| Large SUV | 0.781 | 1.725 | 1.104 | 0.994 |
The risk threshold in table 3 is determined according to the following formula:
in which a is y Is the actual threshold, a y * Is a hazard threshold; b is the track of the automobile, and the unit is m; h is a g Is the centroid height in m; beta is the lateral ramp angle of the road in rad.
If the condition is judged to be the safe condition interference, five-degree-of-freedom displacement signals acquired by a displacement sensor arranged in the condition identification and safety judgment module 1 are respectively connected in series to the fuzzy auto-disturbance rejection control module 3 of the fusion Butterworth filter, the judgment result of the current condition is input to the condition interference observation module 10, the condition interference factor is empirically summarized in the table through systematic continuous operation, the probability of dangerous condition is judged, if the probability of dangerous condition occurrence caused by the condition interference obtained through the empirical summary is large, when the condition interference is detected, the energy stored in the magnetic suspension flywheel battery is released, and the situation that the energy of the magnetic suspension flywheel battery is not released due to too short time is avoided when the dangerous condition occurs is avoided.
2. Establishing a fuzzy active disturbance rejection control module 3 fusing Butterworth filter
Step one: establishing a second-order ADRC mathematical model
The second order ADRC expression is:
Tracking Differentiator (TD):
wherein: e, e x Is given displacement error, e θ1 (k) Is a tracking signal of a given displacement error at time k, e θ2 (k) Is a differential signal of a displacement error given at the moment k, T is an integral step length, alpha is a nonlinear factor, and h 0 Fhan () is the optimal control function, which is the filter factor of TD.
Extended State Observer (ESO):
wherein: i.e x * For actually outputting the feedback value, e (k) is the current error signal, e z1 (k) A state tracking signal, e, which is the actual error at time k z2 (k) A differential signal, e, which is the actual error at time k z3 (k) Is the tracking signal of the disturbance of the system at the moment k, alpha 01 、α 02 Is a nonlinear factor; beta 01 、β 02 ，β 03 The output error correction gain is a system adjustable parameter; delta is the filter factor of ESO, u (k) is the actual output voltage, and fal () is a nonlinear function.
Nonlinear state error feedback control rate (NLSEF):
wherein: alpha 1 、α 2 Is a nonlinear factor e 1 (k)、e 2 (k) Differential signals of the error and the rotation speed, beta 1 Corresponding to the proportion coefficient beta 2 Corresponding to the differential coefficient, u (k) is the voltage compensation control quantity, u 0 (k) To set the control amount, b is disturbance compensation.
Step two: fuzzy control introduction to ADRC
Because the parameter change of ADRC control has a larger influence on the control performance of the system, in order to improve the position control precision of the whole magnetic suspension flywheel battery system and the tracking precision of the system, the integral anti-interference capability is enhanced, and the robustness is improved, the fuzzy control is introduced into the ADRC, the NLSEF is added into an error integrating link, and the NLSEF is rewritten as follows:
Wherein beta is 0 、β 1 And beta 2 The adjusting parameter of the nonlinear state feedback control rate is obtained through a fuzzy controller. Three parameters are similar to the parameter setting of PID, beta 0 Corresponding to integral coefficient beta 1 Corresponding to the proportion coefficient beta 2 Corresponding to differential coefficient e 0 (k) Is an integral signal, alpha 0 And is a nonlinear factor.
e 1 (k)、e 2 (k) As a control input of the fuzzy controller, the output thereof is { beta } 0 、β 1 、β 2 Correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 }。
Step three: introduction of interference factors into fuzzy reasoning
For a fuzzy active disturbance rejection control system, taking into consideration the influence of external disturbance of a magnetic suspension flywheel battery system caused by complex road conditions and internal disturbance such as resistance, inductance and the like on the system performance when the magnetic suspension flywheel battery system is in operation, introducing a noise disturbance factor NIF, a road disturbance factor RDF and a traffic disturbance factor TDF, and establishing a correction parameter { delta beta ] according to different importance of different disturbance factors in normal operation of the vehicle-mounted magnetic suspension flywheel battery system 0 、Δβ 1 、Δβ 2 A fuzzy control rule table.
Blurring (classifying) noise interference factor NIF, road interference factor RDF and traffic interference factor TDF by lambda N 、λ R 、λ T Membership functions representing the respective interference factors:
(1) The noise interference factor NIF is divided into three levels, which are respectively expressed as high, medium and low, and the fuzzy set on the corresponding domain of each level is expressed as:
High (H): noise interference factor NIF e [74, 90), then the fuzzy set over this universe is expressed as:
in (M): noise interference factor NIF e 40,74), the fuzzy set over this universe is expressed as:
low (L): noise interference factor NIF e 0,40), the fuzzy set over this universe is expressed as:
(2) The road interference factor RDF is divided into three levels which are respectively expressed as high, medium and low, and the fuzzy set on the corresponding domain of each level is expressed as:
high (H): road interference factor RDF e (6, 8), the fuzzy set on this domain is expressed as:
in (M): road interference factor RDF e (3, 6), then the fuzzy set on this domain is expressed as:
low (L): road disturbance factor RDF ε (0, 3), the fuzzy set over this domain is expressed as:
(3) The traffic disturbance factor TDF is divided into five classes, which are respectively expressed as very high, medium, low and very low, and the fuzzy set on the corresponding domain of each class is expressed as:
very High (VH): traffic disturbance factor TDF e (8, 10), the fuzzy set over this domain is expressed as:
high (H): traffic disturbance factor TDF e (6, 8), the fuzzy set over this domain is expressed as:
in (M): traffic disturbance factor TDF e (4, 6), the fuzzy set on this domain is expressed as:
low (L): traffic disturbance factor TDF e (2, 4), the fuzzy set on this domain is expressed as:
Very Low (VL): traffic disturbance factor TDF e [0,2], the fuzzy set on this domain is expressed as:
according to the different levels of the noise interference factor NIF, the road interference factor RDF and the traffic interference factor TDF in the interference of the current working condition, namely the interference intensity (the higher the level is, the greater the interference intensity is), the interference intensity is compared with the beta 0 、β 1 、β 2 Correction is performed (the greater the interference intensity, β 1 、β 2 The larger and beta 1 The rate of change of (c) is greater than beta 2 Is a rate of change of (2); beta 0 The influence of the interference intensity can be ignored), so that the fuzzy control law is more accurate. Rate of change
e 1 、e 2 ，{Δβ 0 、Δβ 1 、Δβ 2 The fuzzy subset of } is defined as:
fuzzy subset of input quantity: e, e 1 、e 2 ＝{NB NM NS ZO PS PM}
Fuzzy subset of output quantity：Δβ 0 、Δβ 1 、Δβ 2 ＝{NB NM NS ZO PS PM}
The language subset is { "negative large (NB)", "Negative Medium (NM)", "Negative Small (NS)", "Zero (ZO)", "Positive Small (PS)", "median (PM)", "positive large (PB)" }
Establishing correction parameters delta beta 0 、Δβ 1 、Δβ 2 Fuzzy control rule table, fuzzy reasoning adopts Mamdani reasoning method, parameter control adjustment test is carried out in laboratory, experience obtained in experimental debugging is summarized, control rule is obtained, fuzzy control rule table of fuzzy control output variable is established, and the following table is shown:
TABLE 4 output quantity control rule List
The output quantity is clarified by a weighted average method.
Aiming at useless noise signals generated by factors such as noise of a sensor and the like when the vehicle-mounted magnetic suspension flywheel battery is in operation, a Butterworth filter is introduced into the fuzzy active disturbance rejection control module 3 of the fusion Butterworth filter, and belongs to one of infinite impulse response filters.
The expression of the amplitude square function of the butterworth filter is:
wherein: i H c (j.OMEGA.) represents amplitude-frequency response, and Ω is angular frequency c A 3dB low pass filter cut-off frequency;
the butterworth low-pass filter has the following order:
wherein: n is the order of the filter, alpha s For minimum attenuation of stop band alpha p Minimum attenuation for passband。
3. Establishing a multiple-innovation extended kalman filter (MIEKF) prediction module 7
Step one, an extended Kalman filter is established:
wherein: x is x k 、u k 、y k The state variable, the output variable and the observation variable at the moment k of the system are respectively; omega k 、v k The system k time is the process noise and the observation noise, which are the white noise with zero mean value and uncorrelated, f () is the relation of the system state value, and h () is the relation of the system input value.
Because the magnetic suspension flywheel battery system is a nonlinear system, an extended Kalman filtering algorithm is needed. F and h are as followsTaylor expansion:
in the middle of The optimal state estimated value at the moment k is obtained by only retaining the first-order partial differentiation of the Taylor series:
1) The prediction part of the extended kalman filter is:
in the method, in the process of the invention,is an estimate of the state quantity at time k, < >>Is an estimated value of the state quantity at time k-1, P k|k-1 Is the covariance matrix of the state error at the moment k, P k-1|k-1 Is the state error covariance matrix at time k-1,/->Q k-1 Representing process noise omega k-1 Covariance of A) k-1 Is a state transition matrix for prediction when a state variable changes from k-1 time to k time.
Correction coefficient:
wherein: l (L) k Is a kalman gain matrix that is used to determine,R k for observing error covariance, ++>Is P k|k-1 Is used for the estimation of the estimated value of (a).
2) The update part of the extended kalman filter is:
wherein: e is an identity matrix, x k|k Is the correction value of the system k moment, P k|k Is a state error covariance matrix of updated correction values.
Step two, adding multiple innovation theory into the extended Kalman filter
Definition m (k) =i k -y k|k-1 For a new message, single message m (k) is extended to a multiple message matrix as follows:
/>
wherein: i k Is a measure of current; y is a predicted value of the current; p is the innovation length.
The MIEKF prediction state equation can be obtained according to the multi-information matrix:
wherein:is an estimate of the correction value at time k.
According to the prediction part, the updating part, the correction coefficient and the multi-innovation theory of the extended Kalman filter, simultaneous equations form a prediction equation set of the MIEKF, wherein the equation set is as follows:
The current output from the current rectifying compensation module 6 passes through the multi-innovation extended Kalman filtering, so that the interference of external noise is reduced, the precision of the current sensor is improved, and the characteristics and the intensity of the original signal are maintained to a large extent.
The examples are preferred embodiments of the present invention, but the present invention is not limited to the above-described embodiments, and any obvious modifications, substitutions or variations that can be made by one skilled in the art without departing from the spirit of the present invention are within the scope of the present invention.
Claims (10)
1. A high-safety stable control method of a vehicle-mounted magnetic suspension flywheel battery system based on interference factors is characterized by comprising the following steps of:
the working condition recognition and safety judgment module (1) judges the current working condition interference:
when the interference of the safe working condition is judged, the five-degree-of-freedom displacement reference signals are respectively differenced with the actual displacement signals, and are input into a fuzzy active disturbance rejection control module (3) of the fusion Butterworth filter to obtain control current; the control current comprises radial control current, torsion control current and axial control current, the radial control current and the torsion control current are subjected to Clark inverse transformation and axial control current are subjected to power amplification and then are respectively compensated to obtain three-phase control current signals of a radial coil, three-phase control current signals of a torsion coil and axial control current signals of an axial coil, the control current signals control corresponding coils on one hand, and on the other hand, a multi-innovation extended Kalman filtering prediction module (7) is input to output an actual displacement signal and perform difference with a five-degree-of-freedom displacement reference signal;
And when the dangerous working condition interference is judged, cutting off the output signal of the working condition identification and safety judgment module (1).
2. The high-safety and stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factors according to claim 1, wherein the working condition identification and safety judgment module (1) judges the current working condition interference by establishing a working condition identification and safety judgment mechanism:
1) When the vehicle turns on one's side
Based on a vehicle roll dynamics differential equation considering an interference factor variable function, defining a rollover evaluation index LTR of the whole vehicle quality, wherein when the absolute LTR is <1, the current working condition interference is safe working condition interference, and when the absolute LTR is >1, the vehicle is rollover, and the current working condition interference is dangerous working condition interference;
the LTR is as follows:
wherein: m is m s A is the sprung mass of the vehicle, a ys Lateral acceleration, h, of sprung mass r For the distance of the sprung mass center of gravity to the roll center,for damping the roll of the suspension, < - > j->For side tilt angle +.>For roll angle speed, ++>G is gravity acceleration, h is suspension roll stiffness g The mass center height is the mass center height, B is the wider the wheel track, and m is the mass of the whole vehicle; the interference factor variable function P (k) is:C 1 to the |A y Constant function of S y Is not influenced by normal lateral acceleration under the condition of road conditions and the environment, V car For the current acceleration of the vehicle, R is the turning radius, |A y The I is lateral acceleration, and the beta is a lateral ramp angle of the road;
2) When the vehicle is in rear-end collision or slipping
When the longitudinal acceleration A is detected x Or lateral acceleration |A y And when the I exceeds the threshold value, judging the current working condition interference as dangerous working condition interference, otherwise, judging the current working condition interference as safe working condition interference.
3. The high-safety and stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factors as claimed in claim 2, wherein the working condition interference is as follows:
when longitudinal acceleration A x Normal change and lateral acceleration A y When normal change occurs, judging the working condition interference as working condition interference 1 and working condition interference 13; when longitudinal acceleration A x Normal change and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 3 and working condition interference 15; when longitudinal acceleration A x Normal change, lateral acceleration A y Normal change and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 7 and working condition interference 11; when longitudinal acceleration A x Is greatly changed and vertical acceleration A z When the working condition is changed, the interference is determined as the work conditionCondition disturbance 2, condition disturbance 4, condition disturbance 14 and condition disturbance 16; when longitudinal acceleration A x Normal change and lateral acceleration A y When a large change occurs, judging the working condition interference as working condition interference 5 and working condition interference 9; when longitudinal acceleration A x Is greatly changed and the lateral acceleration A y Normal change and vertical acceleration A z When the change occurs, judging the working condition interference as working condition interference 6, working condition interference 8, working condition interference 10 and working condition interference 12;
wherein: the working condition interference 1 is formed by combining a turning interference factor and a following interference factor, the working condition interference 2 is formed by combining a rolling interference factor and a following interference factor, the working condition interference 3 is formed by combining a gradient interference factor and a following interference factor, the working condition interference 4 is formed by combining a bumping interference factor and a following interference factor, the working condition interference 5 is formed by combining a turning interference factor and an obstructing interference factor, the working condition interference 6 is formed by combining a rolling interference factor and an obstructing interference factor, the working condition interference 7 is formed by combining a gradient interference factor and an obstructing interference factor, the working condition interference 8 is formed by combining a bumping interference factor and an obstructing interference factor, the working condition interference 9 is formed by combining a turning interference factor and a lane changing interference factor, the working condition interference 10 is formed by combining a rolling interference factor and a lane changing interference factor, the working condition interference 11 is formed by combining a bumping interference factor and a lane changing interference factor, the working condition interference 13 is formed by combining a rolling interference factor and an indicating interference factor, the working condition interference 14 is formed by combining a bumping interference factor and an indicating interference factor, the working condition interference 15 is formed by combining a gradient interference factor and an indicating interference factor, and the working condition interference 16 is formed by combining a bumping interference factor and an indicating interference factor.
4. The high-safety and stable control method for the vehicle-mounted magnetic levitation flywheel battery system based on the interference factor as claimed in claim 3, wherein when the longitudinal acceleration A is x Normal changes are included in the range of-0.3 g to 0g, large changes are included in the range of-0.5 g to-0.3 g, and dangerous changes are included in the range of more than-0.5 g; when the lateral acceleration A y At-0.2 gNormal changes in the range of 0.2g, large changes in the range of-0.45 g to-0.2 g and in the range of 0.2g to 0.45g, dangerous changes in the range of less than-0.45 g and greater than 0.45 g; where g refers to gravitational acceleration.
5. The high-safety and stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factors according to claim 1, wherein the fusion Butterworth filter fuzzy active disturbance rejection control module (3) is specifically:
1) The fuzzy control is introduced into ADRC, and the nonlinear state error feedback control rate is added into an error integration link:
wherein: e, e θ1 (k) Is a tracking signal of a given displacement error at time k, e θ2 (k) Is a differential signal of a given displacement error at time k, e z1 (k) A state tracking signal, e, which is the actual error at time k z2 (k) A differential signal, e, which is the actual error at time k z3 (k) Tracking signal for system disturbance at k moment beta 0 、β 1 And beta 2 The adjusting parameter of the nonlinear state feedback control rate is obtained by a fuzzy controller, alpha 0 、α 1 、α 2 Nonlinear factor, delta is the filter factor of the extended state observer, u (k) is the actual output voltage, fal () is a nonlinear function, e 0 (k) Is an integral signal, b is disturbance compensation;
e 1 (k)、e 2 (k) As a control input of the fuzzy controller, the output thereof is { beta } 0 、β 1 、β 2 Correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 }；
2) Introduction of interference factors into fuzzy reasoning
Classifying noise interference factors NIF, road interference factors RDF and traffic interference factors TDF, determining different interference intensities, and comparing beta with the interference intensity 0 、β 1 、β 2 Correcting to obtain a correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 -a }; then establish the correction parameter { Δβ } 0 、Δβ 1 、Δβ 2 A fuzzy control rule table.
6. The high-safety stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factor according to claim 5, wherein the fusion Butterworth filter fuzzy active disturbance rejection control module (3) is used for giving a displacement error e x As an input to a tracking differentiator (311), a tracking signal e of a given displacement error is output θ1 Differential signal e for a given displacement error θ2 A state tracking signal e which is actually error-corrected with the output of the extended state observer (315) z1 Differential signal e of actual error z2 Difference, error e of output rotation speed 1 Differential signal e of rotational speed 2 As inputs to the nonlinear state error feedback control rate (313) and fuzzy inference (312), respectively, also for e 1 Integrating to obtain e 0 Inputting a nonlinear state error feedback control rate (313); fuzzy reasoning (312) is processed to obtain delta beta 0 、Δβ 1 、Δβ 2 Also as an input to the nonlinear state error feedback control rate (313), it outputs a set control amount u 0 (k) Tracking signal e perturbed by output system of extended state observer (315) z3 Multiplying by 1/b, performing difference to obtain actual output voltage u (k), and inputting to controlled object (8) to obtain control current i x * The method comprises the steps of carrying out a first treatment on the surface of the u (k) multiplied by b is input to the extended state observer (315), controlling the current i x * The signal is input to a Butterworth filter (314) for filtering, and the processed signal is also input to an extended state observer (315).
7. The high-safety and stable control method for the vehicle-mounted magnetic suspension flywheel battery system based on the interference factors as claimed in claim 5 is characterized in that:
(1) The noise interference factor NIF is divided into three levels, which are respectively expressed as high, medium and low, and the fuzzy set on the corresponding domain of each level is expressed as:
high: noise interference factor NIF e [74, 90), the fuzzy set over this universe is expressed as:
In (a): noise interference factor NIF e 40,74), the fuzzy set over this universe is expressed as:
low: noise interference factor NIF e 0,40), the fuzzy set over this universe is expressed as:
(2) The road interference factor RDF is divided into three levels which are respectively expressed as high, medium and low, and the fuzzy set on the corresponding domain of each level is expressed as:
high: road interference factor RDF e (6, 8), the fuzzy set on this domain is expressed as:
in (a): road interference factor RDF e (3, 6), the fuzzy set on this domain is expressed as:
low: road interference factor RDF ε (0, 3), the fuzzy set over this universe is represented as:
(3) The traffic disturbance factor TDF is divided into five classes, which are respectively expressed as very high, medium, low and very low, and the fuzzy set on the corresponding domain of each class is expressed as:
very high: traffic disturbance factor TDF ε (8, 10), the fuzzy set over this domain is expressed as:
high: traffic disturbance factor TDF e (6, 8), the fuzzy set over this domain is expressed as:
in (a): traffic disturbance factor TDF e (4, 6), the fuzzy set over this domain is expressed as:
low: traffic disturbance factor TDF e (2, 4), the fuzzy set over this domain is expressed as:
very low: traffic disturbance factor TDF e [0,2], the fuzzy set on this domain is expressed as:
8. The high-safety and stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factors according to claim 1, wherein the multi-innovation extended kalman filter prediction module (7) constructs a prediction equation set of the MIEKF according to a prediction part, an update part, a correction coefficient and a multi-innovation theory of an extended kalman filter:
wherein:is the prior value of the state moment of k moment being an array, u k-1 For the output variable at time k-1 of the system, m (k-p+1) represents the innovation at time k-p+1, I k-p+1 Time is a measurement of current at time k-p+1, y k-p+1 The predicted value of the current at the moment k-p+1, p is the innovation length, L k Is a Kalman gain matrix, < >>x k R is a state variable at the moment of a system k k For the covariance of the observed error, E is the identity matrix, P k|k Is a state error covariance matrix of updated correction values,is P k|k-1 Estimated value of P k|k-1 Is the state error covariance matrix at time k,>is an estimate of the correction value at time k.
9. The high-safety and stable control method of the vehicle-mounted magnetic suspension flywheel battery system based on the interference factor according to claim 1, wherein different vehicle type longitudinal acceleration thresholds are determined by simulation; the lateral acceleration threshold is according to the formula A determination is made, wherein: a, a y Is the actual threshold, a y * Is a dangerous threshold, B is the tread of the automobile, h g Is the centroid height and β is the lateral ramp angle of the road.
10. The high-safety and stable control method of the vehicle-mounted magnetic levitation flywheel battery system based on the interference factors according to claim 1, wherein the turning interference factors, the heave interference factors, the gradient interference factors and the bump interference factors belong to road interference factors, the following interference factors, the blocking interference factors and the lane change interference factors belong to vehicle interference factors, the vehicle interference factors and the indication interference factors form traffic interference factors, and the traffic interference factors and the road interference factors form working condition interference factors.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311368150.3A CN117631532A (en) | 2023-10-20 | 2023-10-20 | High-safety stable control method for vehicle-mounted magnetic suspension flywheel battery system based on interference factors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311368150.3A CN117631532A (en) | 2023-10-20 | 2023-10-20 | High-safety stable control method for vehicle-mounted magnetic suspension flywheel battery system based on interference factors |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117631532A true CN117631532A (en) | 2024-03-01 |
Family
ID=90026002
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311368150.3A Pending CN117631532A (en) | 2023-10-20 | 2023-10-20 | High-safety stable control method for vehicle-mounted magnetic suspension flywheel battery system based on interference factors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117631532A (en) |
-
2023
- 2023-10-20 CN CN202311368150.3A patent/CN117631532A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107161207B (en) | Intelligent automobile track tracking control system and control method based on active safety | |
| Odhams et al. | Active steering of a tractor–semi-trailer | |
| Lee et al. | Adaptive vehicle traction force control for intelligent vehicle highway systems (IVHSs) | |
| Chen et al. | A real-time rollover threat index for sports utility vehicles | |
| Fathy et al. | Online vehicle mass estimation using recursive least squares and supervisory data extraction | |
| Kumarawadu et al. | Neuroadaptive combined lateral and longitudinal control of highway vehicles using RBF networks | |
| Yoshimura et al. | Steering and suspension system of a full car model using fuzzy reasoning based on single input rule modules | |
| KR101470221B1 (en) | Apparatus for controlling suspension and method thereof | |
| Cai et al. | Implementation and development of a trajectory tracking control system for intelligent vehicle | |
| CN106004870A (en) | Vehicle stability integrated control method based on variable-weight model prediction algorithm | |
| Lee et al. | Synthesis of robust lane keeping systems: Impact of controller and design parameters on system performance | |
| CN111002976B (en) | Intelligent vehicle crosswind-resistant control method based on fuzzy self-adaptive PID control | |
| Bouton et al. | A rollover indicator based on the prediction of the load transfer in presence of sliding: application to an all terrain vehicle | |
| Farag et al. | Tuning of PID track followers for autonomous driving | |
| Guo et al. | Integrated adaptive dynamic surface car-following control for nonholonomic autonomous electric vehicles | |
| Ercan et al. | An adaptive and predictive controller design for lateral control of an autonomous vehicle | |
| Yang et al. | A dynamic rollover prediction index of heavy-duty vehicles with a real-time parameter estimation algorithm using NLMS method | |
| Wenzel et al. | Kalman filter as a virtual sensor: applied to automotive stability systems | |
| Nguyen | Development of a fuzzy algorithm with multiple inputs for the active stabilizer bar to improve vehicle stability when steering | |
| CN117631532A (en) | High-safety stable control method for vehicle-mounted magnetic suspension flywheel battery system based on interference factors | |
| Nguyen et al. | Investigate the relationship between the vehicle roll angle and other factors when steering | |
| Chen et al. | On-board estimation of road adhesion coefficient based on ANFIS and UKF | |
| Linström et al. | A real-time non-linear vehicle preview model | |
| CN114148319B (en) | Rollover early warning and control method based on secondary prediction type zero moment point | |
| Kumar et al. | Modeling and analysis of active full vehicle suspension model optimized using the advanced fuzzy logic controller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |