CN115199645B - High-stability low-power consumption flywheel battery magnetic suspension support control system based on vehicle working condition factors - Google Patents
High-stability low-power consumption flywheel battery magnetic suspension support control system based on vehicle working condition factors Download PDFInfo
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Abstract
The invention provides a high-stability low-power consumption magnetic suspension control system based on vehicle working condition factors, wherein a position sensor detects the displacement of the mass center of a flywheel rotor along a coordinate axis and the deflection angle of the flywheel rotor around the coordinate axis, a zero-power consumption control module obtains the displacement/deflection angle adjustment quantity of the flywheel rotor at a balance position according to the displacement and the deflection angle and radial/axial control current, a BP neural network module obtains the displacement/deflection angle pre-measurement and a telescopic factor according to the displacement and the deflection angle and radial/axial control current, the displacement/deflection angle pre-measurement is respectively and linearly added with the displacement/deflection angle adjustment quantity to obtain the adjustment quantity, and a vehicle working condition processing module obtains the control current of a driving working condition and a driving road condition according to the telescopic factor and the adjustment quantity and outputs the three-phase current of a five-degree-of-freedom vehicle magnetic suspension flywheel battery system through conversion. The invention also gives consideration to the high stability and low energy consumption of the flywheel battery magnetic suspension supporting system.
Description
Technical Field
The invention belongs to the technical field of control of vehicle-mounted magnetic suspension flywheel batteries, and particularly relates to a high-stability low-power-consumption magnetic suspension control system based on vehicle working condition factors.
Background
The magnetic suspension bearing utilizes magnetic force to suspend the rotor in space without mechanical contact with the stator, thereby greatly reducing mechanical loss and greatly improving the rotating speed of the rotor; the flywheel battery is used as a novel device for storing energy by using a physical mode, and has the characteristics of high energy conversion efficiency, high energy storage density and the like. Therefore, the two technologies are used in the electric automobile by combining the advantages of the two technologies, and the energy utilization rate of the automobile can be obviously improved. It is worth noting that in the flywheel battery magnetic suspension supporting system composed of the magnetic suspension bearings, the speed of the control system for processing external influencing factors can directly influence the stability of the flywheel battery system.
Because the flywheel battery system is obviously influenced by the working conditions of the vehicle such as acceleration and deceleration, braking, ascending and descending slopes, turning and the like, the flywheel tends to cause frequent eccentricity, so that the control coil current of the magnetic bearing can change more frequently along with the change of the working conditions of the vehicle in order to enable the flywheel to adapt to the influence of the external working conditions as soon as possible and return to the balance position. And under different road conditions, the dynamic performance of the flywheel is greatly different even if the flywheel is caused by the running condition of the same vehicle. Therefore, the stability of the whole flywheel battery system can be deeply influenced along with the increase of the complexity of the running condition and road condition of the external vehicle, so that the design of a high-stability flywheel battery magnetic suspension support control system related to the vehicle working condition factors (the running condition and the road condition of the vehicle) is urgently needed to ensure the high-stability running of the system. The main current magnetic suspension supporting system stability control strategies are as follows: PID control, fuzzy control, sliding mode control, neural network control, etc. Traditional control algorithms such as PID control are simple and easy to adjust, but are difficult to meet the real-time adjustment and control requirements of a complex control system. Intelligent algorithms such as sliding mode control and neural network control have self-adjusting capability to adapt to complex and changeable environments, but the application range is limited due to the fact that the parameter setting depends on experience and the convergence speed is low.
Although the existing control system can finally realize the stability of the magnetic suspension supporting system, the problem of instability caused by large change amplitude of the regulating current and long regulating period in the regulating process is not considered. In order to ameliorate the disadvantages of the control system, it is desirable to ensure that there is still a smooth, rapid control current output under a variety of conditions. On the one hand, a pre-response mechanism is needed to solve the problem that the current parameter changes frequently and variably due to the complex working condition, namely, a control current database corresponding to the corresponding working condition is established. The flywheel is ensured to be quickly and stably realized by a cooperative processing mode of a database and a corresponding control algorithm; on the other hand, if a single algorithm is adopted, the problem of frequent change of complex working conditions is difficult to adapt, so that if the advantages of the algorithms are brought into play through organic fusion of the algorithms, the whole control system can perform timely tracking prediction according to the change of the real-time working conditions, and the control current is quickly adjusted to cope with the disturbance of various working conditions, so that the quick stability of the system is necessary.
Furthermore, for flywheel batteries, the guarantee of high stability tends to come at the expense of a large control loss, the magnitude of which will determine the efficiency and performance of the overall system. The support loss of the magnetic bearing control system among standby losses is the most critical for the vehicle-mounted flywheel battery. Therefore, when controlling the stable suspension of the flywheel, the control loss needs to be reduced as much as possible, that is, the problem of loss of the flywheel battery system needs to be considered while the stability of the flywheel is ensured. At present, the zero-power consumption control algorithm is taken as a classical low-energy consumption control algorithm, and the control current can be converged to zero by adjusting an air gap, so that the loss can be reduced as much as possible when the flywheel is stable. Therefore, in order to reduce the power consumption and not influence the stability of the system, the zero-power control algorithm is effectively combined with the stable control strategy, so that the control effect of low power consumption and high stability can be achieved, and the problem of loss caused by sudden jump of control current due to complex factors of vehicle working conditions is particularly required to be solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a high-stability low-power consumption flywheel battery magnetic suspension supporting control system based on vehicle working condition factors, and simultaneously, the high stability and the low energy consumption of the flywheel battery magnetic suspension supporting system are considered.
The present invention achieves the above technical object by the following means.
A high-stability low-power consumption magnetic suspension control system based on vehicle working condition factors comprises:
the position sensor is used for detecting displacement of the mass center of the flywheel rotor along the x, y and z axes and deflection angles of the flywheel rotor around the x and y axes;
the zero power consumption control module obtains the displacement adjustment quantity and the deflection angle adjustment quantity of the flywheel rotor at the balance position according to the displacement quantity and the deflection angle;
the BP neural network module obtains a displacement pre-measurement value, a deflection angle pre-measurement value and a telescoping factor alpha according to the displacement amount, the deflection angle, the radial control current and the axial control current 1 、α 2 ；
The displacement pre-measurement and deflection angle pre-measurement are respectively added with the displacement adjustment quantity and the deflection angle adjustment quantity in a linear manner to obtain adjustment quantity;
vehicle working condition processing module according to the expansion factor alpha 1 、α 2 And the regulating variable is used for obtaining the control current of the driving working condition and the control current of the driving road condition, and outputting and controlling the three-phase current of the five-degree-of-freedom vehicle-mounted magnetic suspension flywheel battery system through conversion.
In the above technical scheme, the vehicle working condition processing module comprises a running working condition control switching module, a running working condition variable domain theory fuzzy PID controller, a running road condition control switching module and a running road condition variable domain theory fuzzy PID controller;
the driving condition control switching module and the driving condition control switching module comprise a condition control mode selector and a condition database, wherein the condition control mode selector is used for controlling the driving condition according to the expansion factor alpha 1 /α 2 And determining the expansion factor of the input driving condition variable domain theory fuzzy PID controller/driving road condition variable domain theory fuzzy PID controller
The driving condition variable domain theory fuzzy PID controller and the driving road condition variable domain theory fuzzy PID controller are based on the expansion factor And the component of the regulating quantity is used for acquiring the control current of the driving working condition and the control current of the driving road condition.
In the above technical solution, the scaling factorThe determination process of (1) is as follows:
when the real-time working condition of the vehicle is in a certain interval of the working condition database, the expansion factor is used for expanding the working condition databaseEqual to the scaling factor alpha 1 /α 2 When the real-time working condition of the vehicle changes from one section to another section in the working condition database, the expansion factor is +.>Equal to the scaling factor in the operating mode database.
In the above technical scheme, the driving condition variable domain theory fuzzy PID controller and the driving road condition variable domain theory fuzzy PID controller both comprise a PID controller and a fuzzy controller, the fuzzy controller comprises a fuzzifier, an inference machine and a fuzzy eliminator, the PID controller and the fuzzy controller input errors and error change rates generated by adjustment quantity components, the fuzzifier fuzzifies the input, the inference machine fuzzifies the input, obtains PID parameter adjustment quantity according to a fuzzy control rule, and then transmits the PID parameter adjustment quantity to the PID controller through the fuzzy eliminator, so as to output control current.
In the above technical solution, the fuzzy controller further inputs a scaling factorFor controlling expansion and contraction of the input field of discussion.
In the above technical scheme, the component of the adjustment quantity is to divide the adjustment quantity into driving condition current adjustment and driving road condition current adjustment.
In the above technical scheme, the vehicle working condition processing module further comprises a current processing module, and the current processing module is used for performing linear conversion on the control current of the driving working condition and the control current of the driving road condition.
In the above technical solution, the linear conversion mode is:
i * ＝∑m p1 i p1
i ** ＝∑m p2 i p2
wherein: i.e p1 Control current, i for driving conditions p2 For controlling current of driving road condition, m p1 Is i p1 Corresponding linear conversion coefficient, m p2 Is i p2 Corresponding linear conversion coefficients, p=x, y, z, θ x 、θ y ，x、y、z、θ x 、θ y To adjust the quantity i * For controlling current, i, of alpha-axis diameter/axial direction under two-phase coordinates ** The current is controlled for the beta axis diameter/axial direction in two-phase coordinates.
The technical scheme further comprises that:
and the genetic algorithm module is used for obtaining optimal neural network parameters based on the displacement and the deflection angle and transmitting the optimal neural network parameters to the BP neural network module.
The technical scheme further comprises that:
the Clark inverse transformation module comprises a front radial Clark inverse transformation module, an axial Clark inverse transformation module) and a rear radial Clark inverse transformation module which are respectively used for transforming the control current of the driving working condition and the control current of the driving road condition.
The beneficial effects of the invention are as follows:
(1) The invention designs a vehicle working condition processing module by considering the influence of the working condition (comprising the running working condition and the running road condition) of the vehicle, wherein the module is respectively provided with two corresponding switching modules, two domain-variable fuzzy PID controllers and a current processing module aiming at the running working condition and the running road condition of the vehicle; the switching module is used for selecting different working modes for the variable domain theory fuzzy PID controller; the two variable domain theory fuzzy PID controllers are used for respectively processing the influence of the driving working condition and the driving road condition; the current processing module is used for carrying out linear conversion on the output currents of the two variable domain theory fuzzy PID controllers to obtain final control current; the vehicle working condition processing module can pointedly process the influence of different working conditions, and can also be used for joint debugging, so that the real-time response speed of the system is greatly improved, and the stability of the flywheel battery system is improved.
(2) The genetic algorithm module, the neural network module, the vehicle working condition processing module and the zero power consumption control module are organically combined together instead of simply piled up, so that the whole control system can still ensure the high-stability and low-energy consumption operation of the whole system under complex and changeable vehicle working conditions; after the vehicle working condition processing module is built under the action of the output signals of the neural network module and the zero power consumption control module, the vehicle working condition processing module feeds back the processed output signals to the neural network module, so that the neural network module is built under the combined action of the vehicle working condition processing module and the genetic algorithm module, the modules of the whole system are closely combined together, the system response speed is accelerated, the control precision is improved, and the problems of poor system stability and high energy consumption caused by system delay response caused by stacking of various algorithms are effectively avoided.
(3) The invention creatively provides a control current database classified according to the working condition factors of vehicles, which is used for classifying and analyzing the influence of the working condition factors of the vehicles on a flywheel rotor under the support of a magnetic suspension system; when the running condition and the running road condition of the vehicle are detected, the magnitude of the control current is controlled according to a set database interval by giving a telescopic factor, so that the control current can reach the vicinity of an optimal range quickly, then the control current is switched and generated by adjusting a variable domain fuzzy PID controller, and the variable domain fuzzy PID controller adjusts according to flywheel rotor position information based on the control current; thus, the flywheel can reach a stable state with the minimum variation amplitude and the fastest speed; the system greatly reduces the variation amplitude of the control current, greatly reduces the loss caused by the control current, reduces the stabilization time of the magnetic suspension system and increases the stability of the system.
(4) Aiming at the influence of two different factors on a magnetic suspension system in the working condition of a vehicle, the invention establishes two different controllers (a driving working condition variable domain theory fuzzy PID controller/a driving road condition variable domain theory fuzzy PID controller) according to the expansion factors The component of the regulating quantity is used for acquiring control current of a driving working condition and control current of a driving road condition; the invention decomposes the actual influence into different parts, and generates different control currents through different controllers; therefore, the defect that the control has hysteresis in the case of a single controller is overcome, and the stability of the whole magnetic suspension system can be greatly improved.
(5) In order to solve the problem of control loss increase caused by frequent change of adjusting current in the process of adjusting a magnetic bearing, the invention creatively proposes to combine a neural network module with a vehicle working condition processing module, and obtain displacement and deflection angle pre-measurement and expansion factor alpha by transmitting a position sensor to obtain displacement and deflection angle and control current of the vehicle working condition processing module to a BP neural network module 1 、α 2 The method comprises the steps of carrying out a first treatment on the surface of the Therefore, the change of the current in unit time and the change of the magnetic suspension bearing caused by the control current are controlled in a small range, and the loss of the whole system is greatly reduced.
(6) The control system reduces the control loss of the axial magnetic bearing by introducing unequal air gaps under the control of the zero-power-consumption control module, realizes the low power consumption of the suspension system until zero power consumption, and aims at the defect that the current zero-power-consumption control module reduces the loss but is difficult to consider stability, and the output signal of the module is introduced into the vehicle working condition processing module; the control signal which ensures both power consumption and stability is obtained through the processing of the variable domain theory fuzzy PID controller in the vehicle working condition processing module.
(7) In the system, the flywheel rotor displacement and deflection angle detected by the position sensor are transmitted to the genetic algorithm module, the zero-power control module and the neural network module, and the three modules process the position signals simultaneously, so that the processing speed of the whole system is increased, the response time of the control system is shortened, and the hysteresis influence caused by the processing data of the system is reduced.
Drawings
FIG. 1 is a schematic diagram of a high-stability low-power consumption magnetic levitation control system based on vehicle operating conditions;
FIG. 2 is a coordinate system established with a rotor centroid in accordance with the present invention;
FIG. 3 is a block diagram of the driving condition control switching module according to the present invention;
FIG. 4 is a block diagram of the inside of the variable domain theory fuzzy PID controller for driving conditions according to the invention;
FIG. 5 is a fuzzy diagram of variable domains according to the present invention;
in the figure: 1. the system comprises a position sensor, a zero power consumption control module, a genetic algorithm module, a 4 BP neural network module, a 5 vehicle working condition processing module, a 6 Clark conversion module, a 7 five-degree-of-freedom vehicle-mounted magnetic suspension flywheel battery system, a 51 driving working condition control switching module, a 511 driving working condition control mode selector, a 512 driving working condition database, a 52 driving working condition variable domain fuzzy PID controller, a 521 first fuzzy controller, a 53 current processing module, a 54 driving road condition control switching module, a 55 driving road condition variable domain fuzzy PID controller, a 61 front radial Clark conversion module, a 62 axial Clark conversion module and a 63 rear radial Clark conversion 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-stability low-power consumption flywheel battery magnetic suspension supporting control system based on the vehicle working condition factors comprises a position sensor 1, a zero-power consumption control module 2, a genetic algorithm module 3, a BP neural network module 4, a vehicle working condition processing module 5, a Clark inverse transformation module 6 and a five-degree-of-freedom vehicle-mounted magnetic suspension flywheel battery system 7, wherein the vehicle working condition processing module 5 comprises a driving working condition control switching module 51, a driving working condition variable domain theory fuzzy PID controller 52, a current processing module 53, a driving road condition control switching module 54 and a driving road condition variable domain theory fuzzy PID controller 55, and the Clark inverse transformation module 6 comprises a front radial Clark inverse transformation module 61, an axial Clark inverse transformation module 62 and a rear radial Clark inverse transformation module 63.
In a three-axis Cartesian coordinate system established by taking the flywheel rotor centroid as the origin O, as shown in FIG. 2. The position sensor 1 detects displacement amounts (Deltax, deltay, deltaz) of the mass center of the flywheel rotor along the x, y, and z axes, and deflection angles (Deltatheta) of the flywheel rotor around the x and y axes x 、Δθ y ) And the data are respectively transmitted to the zero power consumption control module 2, the genetic algorithm module 3 and the BP neural network module 4. After the zero power consumption control module 2 converts the displacement and the deflection angle into corresponding interference force (the conversion process is the prior art), the balance position of the flywheel rotor is continuously changed to realize that the control current converges to 0 (the process of realizing the control current convergence is the prior art), and finally the displacement adjustment quantity x of the flywheel rotor at the balance position is output * 、y * 、z * And deflection angle adjustment amountThe displacement and deflection angle are used as initial data of the genetic algorithm module 3, and the optimal neural network parameters including the hidden layer node number l and the connection weight omega (including the connection weight omega between the input layer and the hidden layer neurons) are calculated for the BP neural network module 4 through the selection, crossover, variation and evolution operations of the genetic algorithm ij And connection weights omega between hidden layer and output layer neurons j ′ k ) The method comprises the steps of outputting layer node number n, inputting layer node number m, hidden layer threshold value a and outputting layer threshold value b. According to the parameter data output by the genetic algorithm module 3, the BP neural network module 4 establishes a BP neural network; the BP neural network module 4 then outputs the flywheel rotor displacement and deflection angle output by the position sensor 1 and the control current data (including alpha-axis front radial control current +_ under two-phase coordinates) output by the vehicle working condition processing module 5>Beta-axis anterior radial control current +.>Alpha-axis axial control current +.>Beta-axis axial control current +.>Radial control current after alpha axis->Beta-axis rear radial control electricity->) Processing (predicting and analyzing the change trend of the flywheel rotor and the change trend of the control current) to output displacement predicted quantity x * 、y * 、z * And deflection angle pre-measure +>Scaling factor (alpha) 1 、α 2 ). The predicted quantity x output by the BP neural network module 4 * 、y * 、z * 、θ x *、θ y * And the adjustment amount x output by the zero power consumption control module 2 * 、y * 、z * 、/>Linear addition is carried out to obtain the adjustment quantity x, y, z and theta input into the vehicle working condition processing module 5 x 、θ y Where x=x * +x * 、y＝y * +y * 、z＝z * +z * 、/>
Expansion factor (alpha) 1 、α 2 ) Adjusting the amounts x, y, z and theta x 、θ y As inputs of the vehicle condition processing module 5, the driving condition variable domain theory fuzzy PID controller 52 and the driving condition variable domain theory fuzzy PID controller 55 in the vehicle condition processing module 5 respectively calculate control currents of corresponding driving conditions according to the adjustment amountsAnd control current of driving road conditionWherein: { i x1 ,i y1 ,i z1 The displacement adjustment amount x is represented by } 1 、y 1 、z 1 Is used for the control of the current (driving conditions),indicating the deflection angle adjustment quantity theta x1 、θ y1 Is used for the control of the current (driving conditions); { i x2 ,i y2 ,i z2 The displacement adjustment amount x is represented by } 2 、y 2 、z 2 Regulating current (driving road condition), +.>Adjusting the deflection angle adjustment amount θ x2 、θ y2 Is used for the regulation of the current (driving road conditions). The two control currents are linearly converted by the current processing module 53 to output current +.>And->The linear conversion mode is exemplified by the previous radial control current, and the rest is the same as the previous radial control current: />p＝x、y、z、θ x 、θ y Wherein m is p1 Is i p1 A corresponding linear conversion coefficient; />p＝x、y、z、θ x 、θ y Wherein m is p2 Is i p2 Corresponding linear conversion coefficients.
Finally, after the Clark inverse transformation module 6 performs inverse transformation, the two phases (alpha beta axis) are subjected to the coordinateElectric current Inverse transformation is to control three-phase (uvw axis) currents of the five-degree-of-freedom vehicle-mounted magnetic levitation flywheel battery system 7: front radial control current { i fu ,i fv ,i fw Control current { i } axially aw { and post radial control current { i } ru ,i rv ,i rw -a }; wherein: i.e fu Representing the front radial bearing control current of the u-phase, i fv Representing the v-phase front radial bearing control current, i fw Representing the w-phase front radial bearing control current, i aw Represents the w-phase axial control current, i ru Represents the radial bearing control current after u phase, i rv Representing the v-phase front radial bearing control current, i rw Representing the radial bearing control current after w-phase.
For the structure of the running condition control switching module 51 as shown in fig. 3, the running condition control switching module 51 includes a running condition control selector 511 and a running condition database 512, and the running condition control selector 511 is used for selecting the expansion factor α output by the BP neural network module 4 1 Or the stretch factor α of the running condition database 512 (in which various running condition data are stored) 1* Thereby outputting the expansion factor applied to the running condition variable domain fuzzy PID controller 52For->Is determined by detecting real-time working conditions (including driving working conditions and driving road conditions) of the vehicle: when the real-time running information of the vehicle meets a certain condition in the running condition database 512 (i.e. is in a certain section of the database), the vehicle is in a certain section of the database>I.e. the expansion and contraction of the output of the BP neural network module 4Factor controls the variable domain fuzzy PID controller 52 to produce a corresponding current; when the real-time working condition of the vehicle changes from one driving working condition to another driving working condition (namely, from one interval to another interval in the database), the vehicle is in the condition of +.>Given by the driving condition control database 512, i.e. +.>The driving condition control switching module 54 has the same composition and principle as the driving condition control switching module 51, and includes a driving condition control selector and a driving condition database, and its function and principle are the same as the driving condition control selector 511 and the driving condition database 51, and will not be described again here.
Expansion factorAnd a displacement adjustment amount x 1 、y 1 、z 1 Deflection angle adjustment amount θ x1 、θ y1 As an input to the running condition variable domain theory fuzzy PID controller 52, the changes caused by the running conditions are controlled; expansion factor->And a displacement adjustment amount x 2 、y 2 、z 2 Deflection angle adjustment amount θ x2 、θ y2 As an input to the traveling road condition variable domain theory fuzzy PID controller 55, the road condition-induced changes are controlled. The function of the driving condition variable domain theory fuzzy PID controller 55 is similar to that of the driving condition variable domain theory fuzzy PID controller 52, and the driving condition variable domain theory fuzzy PID controller 52 is only explained here. The following is expressed by the component x of the displacement adjustment quantity 1 For example, a working condition variable domain theory fuzzy PID controller is specifically described; as shown in fig. 4, the driving condition variable domain theory fuzzy PID controller 52 internally includes a PID controller and a first fuzzy controller 521, and the components of the first fuzzy controller 521 include a fuzzifier, an inference engine and a fuzzifier; the error e and error are as followsThe difference change rate ec is respectively transferred into the PID controller and the first fuzzy controller 521, and the first fuzzy controller 521 also inputs the expansion factor +.>Component x of displacement adjustment 1 The input generation error e and the error change rate ec are obtained by: e=x 1 -x 1 '(x 1 For the input value of the displacement adjustment quantity, x 1 ' is the last input displacement adjustment value), +.>(i.e., the derivative of error e with respect to time).
For a given interval (input field) [ -P, P](P is a constant), the fuzzifier divides the interval insertion value { NB, NM, NS, ZO, PS, PM, PB }, as shown in FIG. 5, the ordinate is the membership size; when the input value is at [ -P, P]When the range is in the range, blurring the input value according to the maximum membership degree, namely, the input value is a certain insertion value; for error e, the value inserted by dividing is { NB ] e ，NM e ，NS e ，ZO e ，PS e ，PM e ，PB e -a }; for the error change rate ec, the value inserted by dividing is { NB ] ec ，NM ec ，NS ec ，ZO ec ，PS ec ，PM ec ，PB ec -a }; the inference engine obtains the PID parameter adjustment quantity delta k according to the fuzzy control rule according to the fuzzy numerical value p 、Δk i 、Δk d The method comprises the steps of carrying out a first treatment on the surface of the With Δk for fuzzy control rule p For example, as shown in the following table, Δk p Is determined jointly by the error e and the blurred value of the error rate of change ec:
by a telescoping factorControlling inputDomain [ -P, P]Expansion and contraction of (i.e. the actual input argument is [ -Pα) 1 * ，Pα 1 * ]The method comprises the steps of carrying out a first treatment on the surface of the When->The range of the time domain is enlarged, namely the domain is expanded; when->The range of the time domain is reduced, namely the domain is contracted; the expansion and contraction of the domain can effectively solve the problem of inaccurate control caused by the fact that the original domain is still used due to the change of the error e and the error change rate ec.
The inference engine will Δk p 、Δk i 、Δk d Is supplied to a blur eliminator, and the blur amount Deltak is passed through the blur eliminator p 、Δk i 、Δk d Conversion to a clear quantity k p 、k i 、k d Then transmitted to the PID controller, and calculated by the PID controller x1 ＝k p *e+k i *∑e+k d * ec, outputting a control current to the controlled object. The method of eliminating the blurring adopts a weighted average method, and k is adopted p Examples:where Σu is the set weighting coefficient.
Considering that the automobile is in the state of different running working conditions such as stable running, starting and accelerating, braking and decelerating, turning, climbing and the like on different road conditions, the equation for establishing the magnetic bearing flywheel battery is as follows:
wherein: m is the flywheel rotor mass; and->Respectively, the mass center balance position of the flywheel rotor deviates from the mass center in the x, y and z axis directions, x f 、x r Is the displacement of the front radial bearing and the rear radial bearing in the x-axis direction, y f 、y r Is the displacement of the front radial bearing and the rear radial bearing in the y-axis direction, z f Displacement in the z-axis direction of the front radial bearing; f (F) fx 、F fy 、F fz Electromagnetic force applied to the front radial bearing in the x axis, the y axis and the z axis; f (F) rx 、F ry 、F rz Electromagnetic force applied to the rear radial bearing in the x axis, the y axis and the z axis;is a coupling term for torque; l (L) f 、l r The distance from the front radial bearing axle center to the center of mass of the rear radial bearing axle center is the distance from the front radial bearing axle center to the center of mass of the rear radial bearing axle center; j (J) x 、J y And J z Moment of inertia of flywheel rotor around x, y and z axes respectively, and satisfy J x ＝J y ＝J z ，/>And->The rotational angles of the flywheel rotor in the xOz plane and yOz plane, respectively.
Through modeling simulation, the change rule of flywheel rotor displacement and the change rule of corresponding control current compensation control when the vehicle is in different working conditions (turning, climbing, steady running, starting acceleration, braking deceleration, longitudinal vibration, transverse vibration and pitching vibration) under different road conditions (A-H grade road surfaces) are simulated, and the adjustment of the flywheel under the actual conditions is decomposed into the adjustment which influences the running working conditions, the adjustment which influences the running road conditions and other factors:
compared with the driving condition and road condition, the vehicle has influence, kappa i The value is smaller when kappa is ignored i In this case, the flywheel adjustment is considered to be generated by the joint adjustment of the running condition current and the running condition current, and the running condition control current is generated by the running condition variable domain theory fuzzy PID controller 52 and the running condition variable domain theory fuzzy PID controller 55 respectivelyAnd driving road condition control current +.>
Aiming at the difference of the running condition and the running road condition of the vehicle, a database is established, wherein each data in the table is the expansion factor of the database when the running condition and the running road condition are located, and the expansion factor of the database is set according to experience and data:
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 (8)
1. High stable low power consumption magnetic suspension control system based on vehicle operating mode factor, its characterized in that includes:
the position sensor (1) is used for detecting displacement of the mass center of the flywheel rotor along the x, y and z axes and deflection angles of the flywheel rotor around the x and y axes;
the zero power consumption control module (2) obtains the displacement adjustment quantity and the deflection angle adjustment quantity of the flywheel rotor at the balance position according to the displacement quantity and the deflection angle;
a BP neural network module (4) for generating a sum of the displacement amountsDeflection angle, radial control current and axial control current to obtain displacement pre-measurement, deflection angle pre-measurement and expansion factor alpha 1 、α 2 ；
The displacement pre-measurement and deflection angle pre-measurement are respectively added with the displacement adjustment quantity and the deflection angle adjustment quantity in a linear manner to obtain adjustment quantity;
a vehicle working condition processing module (5) for processing the working condition according to the expansion factor alpha 1 、α 2 The control current of the driving working condition and the control current of the driving road condition are obtained, and three-phase current of the five-degree-of-freedom vehicle-mounted magnetic suspension flywheel battery system (7) is output through conversion;
the vehicle working condition processing module (5) comprises a running working condition control switching module (51), a running working condition domain-changing theory fuzzy PID controller (52), a running road condition control switching module (54) and a running road condition domain-changing theory fuzzy PID controller (55);
the driving condition control switching module (51) and the driving condition control switching module (54) comprise a condition control mode selector and a condition database, wherein the condition control mode selector is used for controlling the driving condition according to the expansion factor alpha 1 /α 2 And the expansion factors in the working condition database, determining the expansion factors of the input driving working condition variable domain theory fuzzy PID controller (52)/driving road condition variable domain theory fuzzy PID controller (55)
The driving condition variable domain theory fuzzy PID controller (52) and the driving road condition variable domain theory fuzzy PID controller (55) are controlled according to the expansion factorsThe component of the regulating quantity is used for acquiring control current of a driving working condition and control current of a driving road condition;
the telescoping factorThe determination process of (1) is as follows:
when the vehicle isWhen the real-time working condition is in a certain interval of the working condition database, the expansion factorEqual to the scaling factor alpha 1 /α 2 When the real-time working condition of the vehicle changes from one section to another section in the working condition database, the expansion factor is +.>Equal to the scaling factor in the operating mode database.
2. The high-stability low-power consumption magnetic suspension control system according to claim 1, wherein the driving condition variable domain theory fuzzy PID controller (52) and the driving road condition variable domain theory fuzzy PID controller (55) comprise a PID controller and a fuzzy controller, the fuzzy controller comprises a fuzzifier, an inference engine and a fuzzy eliminator, the PID controller and the fuzzy controller input the error and the error change rate generated by the adjustment quantity component, and the fuzzy eliminator fuzzifies the input, the inference engine fuzzifies the control rule to obtain the PID parameter adjustment quantity, and then the PID parameter adjustment quantity is transmitted to the PID controller through the fuzzy eliminator, so as to output the control current.
3. The high stability low power magnetically levitated control system of claim 2, wherein the fuzzy controller further inputs a telescoping factorFor controlling expansion and contraction of the input field of discussion.
4. The high-stability low-power consumption magnetic levitation control system according to claim 1, wherein the component of the adjustment amount is to divide the adjustment amount into a driving condition current adjustment and a driving condition current adjustment.
5. The high-stability low-power-consumption magnetic levitation control system according to claim 1, wherein the vehicle condition processing module (5) further comprises a current processing module (53), and the current processing module (53) is configured to perform linear conversion on a control current of a driving condition and a control current of a driving road condition.
6. The high stability low power consumption magnetic levitation control system of claim 5, wherein the linear conversion is as follows:
i * ＝∑m p1 i p1
i ** ＝∑m p2 i p2
wherein: i.e p1 Control current, i for driving conditions p2 For controlling current of driving road condition, m p1 Is i p1 Corresponding linear conversion coefficient, m p2 Is i p2 Corresponding linear conversion coefficients, p=x, y, z, θ x 、θ y ，x、y、z、θ x 、θ y To adjust the quantity i * For controlling current, i, of alpha-axis diameter/axial direction under two-phase coordinates ** The current is controlled for the beta axis diameter/axial direction in two-phase coordinates.
7. The high stability low power consumption magnetic levitation control system of claim 1, further comprising:
and the genetic algorithm module (3) obtains optimal neural network parameters based on the displacement and the deflection angle and transmits the optimal neural network parameters to the BP neural network module (4).
8. The high stability low power consumption magnetic levitation control system of claim 1, further comprising:
the Clark inverse transformation module (6) comprises a front radial Clark inverse transformation module (61), an axial Clark inverse transformation module (62) and a rear radial Clark inverse transformation module (63) which are respectively used for transforming the control current of the running working condition and the control current of the running road condition.
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