CN109067264A - A kind of balance car system and its control method - Google Patents
A kind of balance car system and its control method Download PDFInfo
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- 230000008569 process Effects 0.000 abstract description 2
- 238000009415 formwork Methods 0.000 description 16
- 238000001914 filtration Methods 0.000 description 7
- 230000004888 barrier function Effects 0.000 description 5
- 238000004804 winding Methods 0.000 description 4
- 241000208340 Araliaceae Species 0.000 description 3
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 3
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- 235000008434 ginseng Nutrition 0.000 description 3
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- 235000021170 buffet Nutrition 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/04—Arrangements for controlling or regulating the speed or torque of more than one motor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62K—CYCLES; CYCLE FRAMES; CYCLE STEERING DEVICES; RIDER-OPERATED TERMINAL CONTROLS SPECIALLY ADAPTED FOR CYCLES; CYCLE AXLE SUSPENSIONS; CYCLE SIDE-CARS, FORECARS, OR THE LIKE
- B62K11/00—Motorcycles, engine-assisted cycles or motor scooters with one or two wheels
- B62K11/007—Automatic balancing machines with single main ground engaging wheel or coaxial wheels supporting a rider
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/006—Controlling linear motors
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/10—Arrangements for controlling torque ripple, e.g. providing reduced torque ripple
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/34—Modelling or simulation for control purposes
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Mechanical Engineering (AREA)
- Control Of Electric Motors In General (AREA)
Abstract
The invention discloses a kind of balance car system and its control methods to establish the state equation of Extended Kalman filter and the mathematical model of permanent-magnetism linear motor according to the difference equation model and drifting error model of the attitude signal of balance car system；Permanent magnet synchronous motor is driven using second order dynamic Terminal synovial membrane；With particle swarm algorithm to tracking control unit in dual motors system, isochronous controller, backlash control device parameter and switching function optimize simultaneously.The present invention realizes that there are biggish noise and zero-drift errors in the signal that sensor acquires to balance car；Poor robustness；Structure is complicated for brush motor, failure is more, maintenance workload is big, the service life is short, commutation spark is also easy to produce electromagnetic interference, and occurs the solution of the problems such as shake, deviation is excessive, safety is low in balance car operational process.
Description
Technical field
The invention belongs to balance car control technology fields, and in particular to a kind of balance car system and its control method.
Background technique
Two-wheeled balance car is gradually entered into because of characteristics such as its cheap, small in size, light weight, easy to remove and carryings
Among people's lives.Currently, the relevant technologies of balance car are being constantly updated, balance system controls more flexible acumen, man-machine
Interactive system is intended to detailed-oriented and humanized.But in driving method, the balance car brand of mainstream is used currently on the market
It is all mostly brush direct current motor, electric energy enters armature winding driving motor by brush and commutator.Due to brush and change
Presence to device, it is dry that structure is complicated for brush motor, failure is more, maintenance workload is big, the service life is short, commutation spark is also easy to produce electromagnetism
It the characteristics such as disturbs, increases production cost and maintenance cost to a certain extent.That there is cruising abilities is not strong for traditional balance car,
The disadvantages of precision of control system is not high, has a single function.
Although balance car product category currently on the market is various, most of product motor control design to be had not
Foot, traditional control method are easy to cause the generation of balance accident because of the defect of itself.PID control is based on T_S fuzzy control,
Self adaptive control etc. all has following defects that control variable armature voltage indirectly controls car body balance, electronic therefore, it is difficult to guarantee
The dynamic property of vehicle；There are biggish noise and zero-drift errors for the signal of sensor acquisition；Poor robustness is unsuitable for non-
The application of linear system.Sliding formwork control design is simple, control precision is high, and sliding mode has the perturbation of system and external disturbance
There is complete robustness, is the important method for solving nonlinear problem.But common sliding formwork control generally chooses linear switching function, though
It so can guarantee that system is stablized, but theoretically, system mode (or error) converges to balance origin and needs the infinitely great time.By
The inspiration of attractor concept in neural network, Terminal sliding formwork construct diverter surface with nonlinear function, can be in finite time
Converge to equalization point.But the problem of equally existing buffeting buffets the application seriously affected sliding formwork control in engineering and control effect
Fruit.Although the removal of the methods of saturation function is buffeted effectively, control performance can be made to be deteriorated.
In conclusion not comprehensively consider controller each to permanent magnet synchronous motor for existing balance car drive control method
The tracking and monitoring of item performance, and often occur the problems such as shake, deviation is excessive, safety is low during its realization.Therefore
Designing a kind of Novel balance vehicle control method, there is very big Practical Project to be worth.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is that providing a kind of balance car system
System and its control method, second order dynamic Terminal sliding formwork and particle swarm algorithm based on Kalman filtering, in permanent magnet synchronous electric
On the basis of machine, in Finite-time convergence in balance origin, eliminates and buffet, improve the stability and durability of balance car.
The invention adopts the following technical scheme:
A kind of balance car system, including control section, Sensor section, human-computer interaction part, power pack, main circuit portion
Point and epigynous computer section；Control section use microcontroller, respectively with Sensor section, human-computer interaction part, power pack and
The connection of main circuit part；Epigynous computer section is connect with human-computer interaction part, and human-computer interaction part provides electric energy by power pack；
Sensor section includes the gyroscope for acquiring the angular speed of vehicle body offset, and the acceleration for collecting vehicle adds
Speedometer, for acquiring the velocity sensor of actual speed, the rotary angle transmitter of the deflection for acquiring control stick, for examining
The infrared sensor for surveying road conditions, for acquiring the voltage and current sensor of inverter related data；
Human-computer interaction part includes for showing vehicle speed information, information about power, battery temperature information and system running state
The LCD display of feedback is used for transmission the wireless blue tooth that data send instruction to cell phone application and to controller；For logical with PC
The Upper machine communication module of news controls, model selection for power switch, the key of information inquiry, and for prompting vehicle
The LED light and buzzer of warning message；
Power pack includes the charging module and battery for battery charging and the power supply of system components；
Main circuit part includes two permanent magnet synchronous motors and three phase arms of two permanent magnet synchronous motors can be driven inverse
Become device, for realizing the independent control and synthetic operation of motor；
Epigynous computer section is including for carrying out information transmission and send instructions mobile phone by wireless blue tooth and being led to by serial ports
The end PC of letter.
A kind of balance car system control method, according to the difference equation model of the attitude signal of the balance car system and drift
Shift error model establishes the state equation of Extended Kalman filter and the mathematical model of permanent-magnetism linear motor；It is dynamic using second order
State Terminal synovial membrane drives permanent magnet synchronous motor；With particle swarm algorithm to tracking control unit in dual motors system,
Isochronous controller, backlash control device parameter and switching function optimize simultaneously.
Specifically, the state equation of Extended Kalman filter is as follows:
Wherein,For gyroscope output valve,For the true angular velocity of gyroscope measurement, κ is scale error, and ε is drift
Error, ν (n-1) are the white Gaussian noise that mean value is zero, and T is the period.
Further, the difference equation model of the attitude signal of balance car are as follows:
Wherein, ω0For gyroscope initial angle angle value, κ is scale error, and α is drift error, the one of gyroscopic drift error
Rank autoregression AR model is as follows:
Wherein,For Parameters of Autoregressive Models；ν (n) is the measurement white noise that mean value is zero；
The variance of white Gaussian noise is as follows:
Wherein, Ci、CgRespectively inclinometer and gyroscope noise covariance；δ1For the standard deviation of tilt angle；δ2For gyro
Instrument Gaussian noise density criterion is poor；δ3For the standard deviation of noise in gyroscopic drift error AR model.
Specifically, the mechanical motion equation of permanent-magnetism linear motor is as follows:
Wherein, s is mover displacement；V is mover speed；M is mover and its institute's bringing onto load gross mass；BvFor viscous friction because
Number；F∑For total perturbed force.
Further, total perturbed force F∑It is as follows:
F∑=Frip+Fload+Ffric
Wherein, FloadFor load resistance；FripThe equivalent drag generated for end effect；FMFor end effect force oscillation width
Value；τ is pole span；For initial phase electrical angle；FfricFor frictional force；fcFor Coulomb friction coefficient；fsFor static friction coefficient；v
For mover speed；vsFor critical friction velocity.
Specifically, being driven using second order dynamic Terminal synovial membrane to permanent magnet synchronous motor specifically:
Enable x=[x1,x2]T=[s, v]TFor the state variable of system, input control quantity u=iq, state equation is as follows:
Wherein, k1,k2,k3For unknown parameter, kfFor resistance coefficient, BvFor viscous friction factor, F∑For total perturbed force, M is
Mover and its institute's bringing onto load gross mass；
If system tracking error is e=x1 *-x1, wherein x1 *For x1Given value, define system second order it is nonsingular quickly
Terminal sliding mode variable are as follows:
Wherein, 0 < α < 1, β ∈ R+, p, q ∈ N are odd number, λ > p/q, 1 < p/q < 2；
When system mode is close to equalization point, the high-order term of tracking error e (t) levels off to 0, and convergence rate is similar to non-
Unusual terminal sliding mode；When system is far from equalization point, the high-order term of tracking error e (t) plays a major role, and convergence rate is than non-
Unusual terminal sliding mode is faster；System with Sliding Mode Controller meets sliding variable σ and its first derivativeConverge to zero point.
Further, the first derivative of synovial membrane variableIt is as follows:
Synovial membrane control law is as follows:
Wherein: r is position command, and ce is error, and F (t) is the function in order to reach the design of global sliding mode face, F (t)=s
(0) exp (- λ t), λ > 0, s (0) are that initial time is s (t), and sgn (s) is jump function, and B is coefficient of friction；K (t) is switching
Gain, K (t)=max (| E (t) |)+ρ, ρ > 0；When system inputs ss < 0, synovial membrane exists；If system inputs ss > 0, switching increases
Beneficial K (t) should increase；If system inputs ss < 0, handoff gain K (t) should reduce.
Specifically, the performance index function F with particle swarm algorithm setting parameter is as follows:
Wherein, the steady-state error of the smaller expression system of the first item of performance indicator is smaller；Section 2 is smaller to show system
Energy consumption is smaller；T is time, e1It (t) is bound term, u1,u2For comprehensively control rule.
Further, the specific steps are as follows:
S401, the parameters value for initializing population, and the fitness function of each particle is calculated, pass through the shape of weighting
Formula calculates the performance indicator size of every group of parameter；
If the adaptive value of new particle is smaller than previous, adaptive value is updated with new particle；Otherwise, adaptive value is kept
It is constant.
Wherein, pbest (t) is the optimal adaptation value in t moment, and f () is the objective function of performance indicator, and X (t) is every
The position of a particle；The optimal solution that pbest recording individual searches records entire group with gbest and searches in an iteration
The optimal solution that rope arrives；
The more new formula of speed and particle position is as follows:
V [i]=w × v [i]+c1×rand()×(pbest[i]-present[i])+c2×rand()×(gbest-
present[i])
Wherein, v [i] represents the speed of i-th of particle, and w represents Inertia Weight, and c1 and c2 indicate learning parameter, rand ()
Indicate the random number between 0-1, pbest [i] represents the optimal value that i-th of particle search arrives, and gbest represents entire cluster and searches
The optimal value that rope arrives, present [i] represent the current location of i-th of particle；
S402, when adaptive value the smallest in pbest is less than global adaptive value, with the position of corresponding minimum adaptive value
Update global adaptive value；Otherwise, global adaptive value remains unchanged；
Gbest (t+1)=argmin { f (pbest1(t)),f(pbest2(t)),...,f(pbestn(t))}
Wherein, gbest (t) is the optimal adaptation value of the t moment overall situation, and n is the total number of particle；
S403, control parameter value is updated as the following formula
Xi'j(t+1)=Xi'j(t)+Vi'j(t+1)
Wherein, Vi'j (t) is the speed of the jth dimension a group's iteration particle of particle i-th '；Xi'j (t) is jth dimension particle the
The position of i' group's iteration particle, takes i'=20；ω is inertia weight, takes ω=0.7；c1And c2For learning rate, c is taken1=c2
=2；
S404, particle current location exceed set maximum value and minimum value, off-limits particle is assigned again
Value, i.e.,
When exceeding the maximum speed of particle, the speed of particle is assigned a value of again
Wherein, Xmin (j) and Xmax (j) is respectively the minimum position and maximum position of jth dimension；Vmin (j) and Vmax (j)
The respectively minimum speed and maximum speed of jth dimension；
S405, when the number of iterations be less than maximum setting number when, steering step S403；Otherwise terminate.
Compared with prior art, the present invention at least has the advantages that
A kind of balance car system of the present invention, including control section, Sensor section, human-computer interaction part, power pack, master
Circuit part and epigynous computer section, motor uses permasyn morot, compared with the direct current generator of traditional two-wheeled balance car, tool
There is low temperature rise, high-efficient, high start torque, the shorter starting time, exceed the advantages of loading capability.The design of five leg inverters is then
The dynamic property for improving two electric systems, reduces torque ripple, can effectively reduce the proportion of goods damageds of the energy.In the same electricity
In the case where tankage, the cruising ability of the balance car system of five leg inverters driving is clearly more powerful.
The invention also discloses a kind of balance car system control methods, using particle swarm algorithm to the ginseng of the controller of design
Number optimizes, and under the premise of guaranteeing tracking performance, reduces the energy consumption of system；To reach energy-efficient effect, need to make to control
The value of amount is minimum, is while meeting tracking accuracy and energy-efficient purpose, the unknown ginseng in control law is obtained using particle swarm algorithm
The optimal value of β in several and switching function；Second order Dynamic sliding mode is combined with Terminal sliding formwork, quickly, accurately
While trace command signal, sliding formwork buffeting is effectively removed, emulation shows the validity of this method.
Further, using Kalman filtering, valid prediction is made to the trend of system next step, even if along with
Various interference, Kalman filtering always can designate that it is true there is a situation where.Kalman filtering is used in the system of consecutive variations
Be it is ideal, it has the advantages that committed memory is small, (other than preceding state amount, does not need to retain other history numbers
According to) and speed is quickly, is well suited for being applied to real time problem and embedded system.
Further, permanent magnetic linear synchronous motor possess without electromagnetism, thrust density is big and high-efficient the advantages that.It is emulated
Method overcomes the deficiencies of existing technologies, and provides a kind of operation state of linear motor, realizes for permanent magnet linear synchronous motor
Emulation provides a great help for the research of permanent magnet linear synchronous motor operating status.
Further, second order dynamic Terminal sliding formwork tradition sliding formwork is in design, to make system motion be maintained at sliding formwork
On face, system input is asked in different controllers to be toggled, and is buffeted so that system exists.High-Order Sliding Mode and Dynamic sliding mode can be very
Overcome this problem well.High-Order Sliding Mode chooses controller, not only allows switching function to be equal to zero, but also make the one of switching function
Order derivative is until r-1 order derivative is also equal to zero, and control item is only operated on the r order derivative of switching function, referred to as r rank sliding formwork
Control.Dynamic sliding mode is then that discontinuous term is embedded into the derivative term of controller.
Further, the method for carrying out PID controller parameter adjusting with particle swarm optimization algorithm for every kind of parameter and is fitted
Function is answered, specific design procedure is all provided, and chooses and is applied to common permanent magnetic DC servo electricity in control engineering design
Motivation has certain practical value as research object.Simulation result has certain improvement compared with genetic algorithm.
The obtained optimal parameter under different fitness functions of analog result, can be applied to actual servomotor speed control.
In conclusion the present invention is realized, to balance car, in the signal that sensor acquires, there are biggish noises and zero-bit to float
Shift error；Poor robustness；Structure is complicated for brush motor, failure is more, maintenance workload is big, the service life is short, commutation spark is also easy to produce
Electromagnetic interference, and occur the solution of the problems such as shake, deviation is excessive, safety is low in balance car operational process.
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Detailed description of the invention
Fig. 1 is Novel balance vehicle system general diagram；
Fig. 2 is functional block diagram of the present invention；
Fig. 3 is control flow chart of the invention；
Fig. 4 is particle swarm algorithm block diagram of the invention.
Specific embodiment
Referring to Fig. 1, the present invention provides a kind of balance car system, using the second order dynamic based on Kalman filtering
Terminal sliding formwork and particle swarm algorithm, including control section, Sensor section, human-computer interaction part, power pack, main circuit
Partially, epigynous computer section；Control section use microcontroller, respectively with Sensor section, human-computer interaction part, power pack and
The connection of main circuit part；Epigynous computer section is connect with human-computer interaction part, and human-computer interaction part provides electric energy by power pack.
Control section: the STM32F103ZET6 of ST company is selected to control as main control chip, there are two advanced fixed for the chip
When device and 512K memory capacity, have 5 kinds of I/O ports, multiple serial communication interfaces meet design requirement well.
Sensor section: gyroscope, outer sensor, current sensor, voltage sensor, rotary angle transmitter, speed pass
Sensor.Wherein, gyroscope is used to acquire the angular speed of vehicle body offset, and accelerometer is used to the acceleration of collecting vehicle, velocity pick-up
Device is used to acquire actual speed, and rotary angle transmitter is used to acquire the deflection of control stick, and infrared sensor is used to detect road conditions, electricity
Pressure and current sensor are used to acquire the related data of inverter.
Human-computer interaction part: there are LCD display, wireless blue tooth, Upper machine communication module, key, LED light, buzzer.
LCD display can show vehicle speed information, information about power, battery temperature information and system running state feedback；Nothing
Line bluetooth is mainly used for transmitting data to cell phone application and sending to controller instructing；Upper machine communication module is used for and PC communication；
Key is controlled for power switch, model selection, information inquiry；LED light and buzzer are for prompting vehicle alarm information.
Power pack: there are charging module and battery.
Power supply for battery charging and system components.
Main circuit part: there are analog line driver and two permanent magnet synchronous motors.
Analog line driver is five leg inverters, can drive two motors simultaneously, and can be realized the independence of motor
Control and synthetic operation.
Epigynous computer section: it is divided into mobile phone and the end PC.
Mobile phone mainly carries out information transmission and send instructions by wireless blue tooth；The end PC is by serial communication, mainly
It is the debugging of the programming and system for program.
Functional module include computing module between region trochanterica, speed calculation module, angle sorting module, phase adjusting module,
Rate control module, current limit control module, waveform modulated module, three-phase inverter and electric motor starting module.Such as Fig. 2.
Referring to Fig. 3, a kind of balance car control method of the present invention, using particle swarm algorithm to the parameter of the controller of design
It optimizes, under the premise of guaranteeing tracking performance, reduces the energy consumption of system；To reach energy-efficient effect, need to make control amount
The value of ui is minimum, is while meeting tracking accuracy and energy-efficient purpose, the unknown ginseng in control law is obtained using particle swarm algorithm
The optimal value of β in several k1, k2, k3, k4 and switching function；Method particularly includes: tracking accuracy and energy consumption are integrated into a mesh
Scalar functions E, the final goal of optimization are that the minimum value of objective function E is acquired in the case where meeting constraint condition.
The balance car course of work is as follows:
Step 1: starting, and initializes system；
Step 2: information exchange is carried out by wireless telecommunications bluetooth module；
Step 3: the video of vehicle bottom camera shooting is obtained, and video signal compression is encoded and stored；
Step 4: keyboard scanning；
Step 5: obstacle scanning；Acquisition obstacle avoidance sensor signal simultaneously calculates route by obstacle avoidance algorithm；
Step 6: road conditions scanning；
(1) vehicle bottom is detected by infrared sensor and obtains ground flat degree information to the distance on ground, examined by road conditions
Method of determining and calculating calculates road conditions parameter, obtains the road conditions currently travelled by further analyzing data, carries out road
Condition judgement.
(2) angle and angular speed of car body, input Kalman filtering filter are obtained by accelerometer and gyro sensor
Wave device, pass through second order dynamic Terminal sliding formwork calculate and export one group output signal to motor control to car body angle into
Row adjustment, enables car body to be smoothly kept upright；
(3) signal for acquiring velocity sensor, obtains actual speed, completes the closed-loop control to speed by speed by PID；
(4) voltage signal of sampling operation bar and basis judge the direction and angle of steering compared with reference voltage
Degree, and corresponding motor control signal is generated to control section, the motor differential operating of control left and right two is completed to turn to；
(5) barrier place orientation and infrared sensor are detected by obstacle avoidance sensor to obtain the feelings around barrier
Condition selects in a certain range not all orientation of barrier, by surrounding's feature of the barrier detected, by this first
A little corresponding route calculations in orientation, which come out, to be compared, and the shortest avoidance route of detour radius is found out, thus avoiding barrier；
(6) electric moter voltage and winding current are acquired by voltage and current sensor, utilizes machine winding current and voltage
Calculating torque and magnetic linkage, then by torque and flux regulating device and using having existed and disclosed existing vector table generates
The PWM on required 12 tunnel carries out Direct Torque Control, is output to DC brushless motor；
Step 7: buzzer control, LED control, display screen is shown, judges whether to terminate, if terminated, is entered step
Eight, it is not over, return step two；
Step 8: terminate.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.The present invention being described and shown in usually here in attached drawing is real
The component for applying example can be arranged and be designed by a variety of different configurations.Therefore, below to the present invention provided in the accompanying drawings
The detailed description of embodiment be not intended to limit the range of claimed invention, but be merely representative of of the invention selected
Embodiment.Based on the embodiments of the present invention, those of ordinary skill in the art are obtained without creative efforts
The every other embodiment obtained, shall fall within the protection scope of the present invention.
The control method of balance car of the present invention, includes the following steps:
S1, Kalman's signal fused filtering
In sensor systems, balance car body sway angular speed is measured by gyroscope, and measurement result has error,
Predominantly drift error and scale error, output characteristics are
In formula:For gyroscope output valve,For the true angular velocity of gyroscope measurement, κ is scale error, and α is drift
Error.
Calculus of differences is carried out to formula (1), then in a cycle T, the difference equation model of the attitude signal of balance car is
ω0Gyroscopic drift can be established according to the AR estimation method of gyroscopic drift error for gyroscope initial angle angle value
The first-order autoregression AR model of error:
In formula:For Parameters of Autoregressive Models, value 0.9185；ν (n) is the measurement white noise that mean value is zero.
According to the difference equation model and drifting error model of the attitude signal of balance car, Extended Kalman filter is established
State equation:
In formula: ν (n-1) is the white Gaussian noise that mean value is zero.
The variance of white Gaussian noise is
In formula: Ci、CgRespectively inclinometer and gyroscope noise covariance；δ1For the standard deviation of tilt angle；δ2For gyro
Instrument Gaussian noise density criterion is poor；δ3For the standard deviation of noise in gyroscopic drift error AR (1) model.
The mathematical model of S2, permanent-magnetism linear motor
Permanent-magnetism linear motor electromagnetic push expression formula is
The mechanical motion equation of PMLSM are as follows:
Wherein, s is mover displacement；V is mover speed；M is mover and its institute's bringing onto load gross mass；BvFor viscous friction because
Number；F∑For total perturbed force F∑=Frip+Fload+Ffric；FloadFor load resistance；FripThe equivalent drag generated for end effect；Wherein, FMFor end effect force oscillation amplitude；τ is pole span；For initial phase electrical angle；
FfricFor frictional force；fcFor Coulomb friction coefficient；fsFor static friction coefficient；v
For mover speed；vsFor critical friction velocity.
S3, second order dynamic Terminal sliding formwork control
Using permanent magnet synchronous motor as control object, it is assumed that magnetic circuit is unsaturated, and space magnetic field is in Sine distribution, disregard magnetic hysteresis and
Eddy-current loss influences, and voltage equation is
Torque equation is
Tm=p (ψd*iq-ψq*id) (10)
Mechanical motion equation is
In formula, ud、uqFor stator voltage dq axis component；id、iqFor stator current components；ω is rotor velocity；Ld、LqFor
Stator winding dq axis equivalent inductance；RsFor stator resistance；ψfFor each pair of magnetic pole magnetic linkage；TLFor load torque；J is rotary inertia；B
For coefficient of friction；P is stage logarithm.
Enabling speed preset signal is ω, defines error state em=ω-ω, obtaining speed error system is
Realize system without buffeting synovial membrane control according to the property of high-order synovial membrane, therefore by synovial membrane more than second order or second order
System.
For ease of description, x=[x is enabled1,x2]T=[s, v]TFor the state variable of system, input control quantity u=iq。
The then state equation form of system (3) are as follows:
Wherein,
If system tracking error is e=x1 *-x1, wherein x1 *For x1Given value.
The nonsingular fast terminal sliding variable of the second order of definition system are as follows:
Wherein, 0 < α < 1, β ∈ R+, p, q ∈ N be odd number, λ > p/q, it is desirable that 1 < p/q < 2 meets the non-surprise in synovial membrane face
It is anisotropic.
By the sliding-mode surface it is found that when system mode is close to equalization point, the high-order term of tracking error e (t) levels off to 0, can
To ignore, convergence rate is similar to non-singular terminal sliding formwork；When system is far from equalization point, the height of tracking error e (t)
Secondary item plays a major role, so its convergence rate is faster than non-singular terminal sliding formwork.
It must satisfy sliding variable σ and its first derivative according to the requirement that System with Sliding Mode Controller designsConverge to zero point.
The first derivative of synovial membrane variableFor
Assuming that sliding variable σ designed in formula (14) is the output variable of uncertain system, and two known to formula (15)
The Relative order of rank System with Sliding Mode Controller is 1, and u appropriate need to be selected to make output variable σ and its derivativeIn Finite-time convergence
It is zero.
The state equation of formula (15) sliding variable can be written as
Wherein, r is position command, by obscuring synovial membrane algorithm, if error e are as follows: e=r- θ.
Separately set global dynamic synovial membrane face are as follows:
S=e+ce-F (t) (17)
Wherein, F (t) is the function in order to reach the design of global sliding mode face, and F (t)=s (0) exp (- λ t), λ > 0, s (0) are
Initial time is s (t).
Defining Lyapunov function isThen:
So synovial membrane control law are as follows:
Wherein: K (t) is handoff gain, K (t)=max (| E (t) |)+ρ, ρ > 0.
Formula (19) is brought into formula (18) to obtain:
V=-K (t) | s |-E (t) s (20)
Wherein, V≤- η | s |≤0.
In conclusion can be obtained according to Leah Pu Nuofu stability theoremWhen ss < 0, synovial membrane exists.
Fuzzy rule:
If 1, ss > 0, K (t) should increase.
2, ss < 0, K (t) should reduce.
If ss is system input, Δ K (t) is system output, and according to fuzzy rule, vague definition is as follows:
Ss={ NB NM ZO PM PB }
Δ K={ NB NM ZO PM PB } (21)
Wherein, NB, NM, ZO, PM, PB be negative respectively it is big, negative in, zero, just neutralizing it is honest.
The previous of K (t) is estimated using the method for integral:
Wherein: G is proportionality coefficient, G > 0.Formula (19) are brought into obtain:
Wherein,r(t,x1,x2) it is nondeterministic function, and meet with downstream condition:
State trajectory can be set in σ-σ plane using super-twisting algorithm and spirally converge to original around origin in finite time
Point.Specific algorithm is as follows:
Wherein, M1、M2Meet:
It for system (13), chooses nonlinear sliding mode variable (14), meets condition (24), make in supercoil control law (25)
Under, if meeting condition (26), system will be in Finite-time convergence.
S4, with particle swarm algorithm to tracking control unit in dual motors system, isochronous controller, backlash control device parameter with
And switching function optimizes simultaneously, as shown in Figure 4.
It chooses and is based on particle swarm algorithm setting parameter.In order to guarantee tracking performance and the smallest two conditions of energy consumption, design
Following performance index function:
The steady-state error of the smaller expression system of the first item of performance indicator is smaller；The smaller energy consumption for showing system of Section 2 is got over
It is small.Constraint condition is guarantee tracking accuracy within 10% and the gross energy of system consumption is minimum.By tracking accuracy and energy consumption
It is integrated into an objective function E, the final goal of optimization is that the minimum of objective function E is acquired in the case where meeting constraint condition
Value.
S401, the parameters value for initializing population, and calculate the fitness function of each particle.Pass through the shape of weighting
Formula calculates performance indicator (objective function adaptive value) size of every group of parameter.
If the adaptive value of new particle is smaller than previous, adaptive value is updated with new particle；Otherwise, adaptive value is kept
It is constant.
Wherein, pbest (t) is the optimal adaptation value in t moment, and f () is usually the objective function of performance indicator, i.e., more
The weighting of a important indicator, X (t) are the positions of each particle.
Pbest carrys out the optimal solution that recording individual searches, and entire group is recorded with gbest and is searched in an iteration
Optimal solution.
The more new formula of speed and particle position is as follows:
V [i]=w × v [i]+c1×rand()×(pbest[i]-present[i])+c2×rand()×(gbest-
present[i]) (29)
Wherein, v [i] represents the speed of i-th of particle, and w represents Inertia Weight, and c1 and c2 indicate learning parameter, rand ()
Indicate the random number between 0-1, pbest [i] represents the optimal value that i-th of particle search arrives, and gbest represents entire cluster and searches
The optimal value that rope arrives, present [i] represent the current location of i-th of particle.
S402, when adaptive value the smallest in pbest is less than global adaptive value, with the position of corresponding minimum adaptive value
Update global adaptive value；Otherwise, global adaptive value remains unchanged.
Gbest (t+1)=argmin { f (pbest1(t)),f(pbest2(t)),...,f(pbestn(t))} (30)
Wherein, gbest (t) is the optimal adaptation value of the t moment overall situation, and n is the total number of particle.
S403, control parameter value is updated according to following formula
Xi'j(t+1)=Xi'j(t)+Vi'j(t+1) (31)
Wherein, Vi'j (t) is the speed of the jth dimension a group's iteration particle of particle i-th '；Xi'j (t) is jth dimension particle the
The position of i' group's iteration particle, takes i'=20；ω is inertia weight, takes ω=0.7；C1 and c2 is learning rate, takes c1=
C2=2.
S404, particle current location exceed set maximum value and minimum value, off-limits particle is assigned again
Value, i.e.,
Similarly it is found that for the current speed of particle, when exceeding the maximum speed of particle, the speed weight of particle
Newly it is assigned a value of
Wherein, Xmin (j) and Xmax (j) is respectively the minimum position and maximum position of jth dimension；Vmin (j) and Vmax (j)
The respectively minimum speed and maximum speed of jth dimension.
S405, when the number of iterations be less than maximum setting number when, steering step S3；Otherwise, terminator.
In balancing bicycle motor control particle swarm algorithm seek in the case where optimized parameter, the not only tracking error of system
There is no trembling, and two motors only disappear gap when there is backlash, keep synchronous operation in the case of other, realize tracking,
Synchronous and the gap that disappears comprehensively control target.And two motors are alternately through backlash, when a motor passes through backlash, separately
The load of one motor drag plays the role of biasing torque and disappears gap.It can be seen that integrated controller designed by the present embodiment
The influence of Backlash Nonlinearity under the premise of proof load tracking performance, can be eliminated and guarantee the synchronization of two motors.
The above content is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, all to press
According to technical idea proposed by the present invention, any changes made on the basis of the technical scheme each falls within claims of the present invention
Protection scope within.
Claims (10)
1. a kind of balance car system, which is characterized in that including control section, Sensor section, human-computer interaction part, power supply
Point, main circuit part and epigynous computer section；Control section use microcontroller, respectively with Sensor section, human-computer interaction part,
Power pack is connected with main circuit part；Epigynous computer section is connect with human-computer interaction part, and human-computer interaction part is by power pack
Electric energy is provided；
Sensor section includes the gyroscope for acquiring the angular speed of vehicle body offset, the acceleration of the acceleration for collecting vehicle
Meter, for acquiring the velocity sensor of actual speed, the rotary angle transmitter of the deflection for acquiring control stick, for detecting road
The infrared sensor of condition, for acquiring the voltage and current sensor of inverter related data；
Human-computer interaction part includes for showing vehicle speed information, information about power, battery temperature information and system running state feedback
LCD display, be used for transmission data to cell phone application and to controller send instruction wireless blue tooth；For what is communicated with PC
Upper machine communication module controls, model selection for power switch, the key of information inquiry, and for prompting car alarming
The LED light and buzzer of information；
Power pack includes the charging module and battery for battery charging and the power supply of system components；
Main circuit part includes two permanent magnet synchronous motors and the three-phase inverter that can drive two permanent magnet synchronous motors, is used
In the independent control and synthetic operation of realizing motor；
Epigynous computer section includes for carrying out information transmission and send instructions mobile phone by wireless blue tooth and by serial communication
The end PC.
2. a kind of balance car system control method, which is characterized in that the attitude signal of balance car system according to claim 1
Difference equation model and drifting error model, establish the state equation of Extended Kalman filter and the number of permanent-magnetism linear motor
Learn model；Permanent magnet synchronous motor is driven using second order dynamic Terminal synovial membrane；With particle swarm algorithm to bi-motor
Tracking control unit in system, isochronous controller, backlash control device parameter and switching function optimize simultaneously.
3. a kind of balance car system control method according to claim 2, which is characterized in that the shape of Extended Kalman filter
State equation is as follows:
Wherein,For gyroscope output valve,For the true angular velocity of gyroscope measurement, κ is scale error, and ε is drift error, ν
(n-1) it is white Gaussian noise that mean value is zero, T is the period.
4. a kind of balance car system control method according to claim 3, which is characterized in that the attitude signal of balance car
Difference equation model are as follows:
Wherein, ω0For gyroscope initial angle angle value, κ is scale error, and α is drift error, and the single order of gyroscopic drift error is certainly
It is as follows to return AR model:
Wherein,For Parameters of Autoregressive Models；ν (n) is the measurement white noise that mean value is zero；
The variance of white Gaussian noise is as follows:
Wherein, Ci、CgRespectively inclinometer and gyroscope noise covariance；δ1For the standard deviation of tilt angle；δ2For gyroscope height
This noise density standard deviation；δ3For the standard deviation of noise in gyroscopic drift error AR model.
5. a kind of balance car system control method according to claim 2, which is characterized in that the machinery of permanent-magnetism linear motor
The equation of motion is as follows:
Wherein, s is mover displacement；V is mover speed；M is mover and its institute's bringing onto load gross mass；BvFor viscous friction factor；F∑
For total perturbed force.
6. a kind of balance car system control method according to claim 5, which is characterized in that total perturbed force F∑It is as follows:
F∑=Frip+Fload+Ffric
Wherein, FloadFor load resistance；FripThe equivalent drag generated for end effect；FMFor end effect force oscillation amplitude；τ
For pole span；For initial phase electrical angle；FfricFor frictional force；fcFor Coulomb friction coefficient；fsFor static friction coefficient；V is
Sub- speed；vsFor critical friction velocity.
7. a kind of balance car system control method according to claim 2, which is characterized in that utilize second order dynamic
Terminal synovial membrane drives permanent magnet synchronous motor specifically:
Enable x=[x1,x2]T=[s, v]TFor the state variable of system, input control quantity u=iq, state equation is as follows:
Wherein, k1,k2,k3For unknown parameter, kfFor resistance coefficient, BvFor viscous friction factor, F∑For total perturbed force, M is mover
And its institute's bringing onto load gross mass；
If system tracking error is e=x1 *-x1, wherein x1 *For x1Given value, define the nonsingular fast terminal of second order of system
Sliding variable are as follows:
Wherein, 0 < α < 1, β ∈ R+, p, q ∈ N are odd number, λ > p/q, 1 < p/q < 2；
When system mode is close to equalization point, the high-order term of tracking error e (t) levels off to 0, and convergence rate is similar to nonsingular
Terminal sliding mode；When system is far from equalization point, the high-order term of tracking error e (t) plays a major role, and convergence rate is than nonsingular
Terminal sliding mode is faster；System with Sliding Mode Controller meets sliding variable σ and its first derivativeConverge to zero point.
8. a kind of balance car system control method according to claim 7, which is characterized in that the first derivative of synovial membrane variableIt is as follows:
Synovial membrane control law is as follows:
Wherein: r is position command, and ce is error, and F (t) is the function in order to reach the design of global sliding mode face, F (t)=s (0)
Exp (- λ t), λ > 0, s (0) are that initial time is s (t), and sgn (s) is jump function, and B is coefficient of friction；K (t) is that switching increases
Benefit, K (t)=max (| E (t) |)+ρ, ρ > 0；When system inputs ss < 0, synovial membrane exists；If system inputs ss > 0, handoff gain
K (t) should increase；If system inputs ss < 0, handoff gain K (t) should reduce.
9. a kind of balance car system control method according to claim 2, which is characterized in that adjusted with particle swarm algorithm
The performance index function F of parameter is as follows:
Wherein, the steady-state error of the smaller expression system of the first item of performance indicator is smaller；The smaller energy consumption for showing system of Section 2
It is smaller；T is time, e1It (t) is bound term, u1,u2For comprehensively control rule.
10. a kind of balance car system control method according to claim 9, which is characterized in that specific step is as follows:
S401, the parameters value for initializing population, and the fitness function of each particle is calculated, the meter by way of weighting
Calculate the performance indicator size of every group of parameter；
If the adaptive value of new particle is smaller than previous, adaptive value is updated with new particle；Otherwise, adaptive value is kept not
Become；
Wherein, pbest (t) is the optimal adaptation value in t moment, and f () is the objective function of performance indicator, and X (t) is each grain
The position of son；The optimal solution that pbest recording individual searches records entire group with gbest and searches in an iteration
Optimal solution；
The more new formula of speed and particle position is as follows:
V [i]=w × v [i]+c1×rand()×(pbest[i]-present[i])+c2×rand()×(gbest-
present[i])
Wherein, v [i] represents the speed of i-th of particle, and w represents Inertia Weight, and c1 and c2 indicate that learning parameter, rand () indicate
Random number between 0-1, pbest [i] represent the optimal value that i-th of particle search arrives, and gbest represents entire group search and arrives
Optimal value, present [i] represents the current location of i-th of particle；
S402, when adaptive value the smallest in pbest is less than global adaptive value, with the location updating of corresponding minimum adaptive value
Global adaptive value；Otherwise, global adaptive value remains unchanged；
Gbest (t+1)=arg min { f (pbest1(t)),f(pbest2(t)),...,f(pbestn(t))}
Wherein, gbest (t) is the optimal adaptation value of the t moment overall situation, and n is the total number of particle；
S403, control parameter value is updated as the following formula
Xi'j(t+1)=Xi'j(t)+Vi'j(t+1)
Wherein, Vi'j (t) is the speed of the jth dimension a group's iteration particle of particle i-th '；Xi'j (t) is that jth dimension particle i-th ' is a
The position of group's iteration particle, takes i'=20；ω is inertia weight, takes ω=0.7；c1And c2For learning rate, c is taken1=c2=2；
S404, particle current location exceed set maximum value and minimum value, to off-limits particle again assignment, i.e.,
When exceeding the maximum speed of particle, the speed of particle is assigned a value of again
Wherein, Xmin (j) and Xmax (j) is respectively the minimum position and maximum position of jth dimension；Vmin (j) and Vmax (j) is respectively
For the minimum speed and maximum speed of jth dimension；
S405, when the number of iterations be less than maximum setting number when, steering step S403；Otherwise terminate.
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