CN105313957B - A kind of electric boosting steering system power assist control method based on complex controll - Google Patents
A kind of electric boosting steering system power assist control method based on complex controll Download PDFInfo
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Abstract
The present invention proposes a kind of electric boosting steering system power assist control method based on complex controll, it is therefore intended that improves security, stability and the comfort of automobile in the process of moving.First, according to characteristics such as the uncertainties of non-linear and parameter of system, sliding mode controller is devised；Secondly, for the robustness of strengthening system, reduce and even eliminate the distinctive chattering phenomenon of sliding formwork control, while improve the adaptive characteristic of control rate, on the basis of sliding formwork control, utilize the key parameter of fuzzy control optimal control rate；Finally, in order to improve the control accuracy of system, the control rule of genetic algorithm optimization fuzzy control is utilized.By the complex controll of three kinds of control methods, the final control accuracy and robustness for improving system, while reduce the chattering phenomenon for even eliminating system, and then improve security, stability and the comfort of running car.
Description
Technical field
The present invention relates to electric boosting steering system field, more particularly to a kind of electric power steering based on complex controll
System power assist control method.
Background technology
Vehicular electric power-assisted steering (EPS) system includes electronic control unit (ECU), torque sensor, speed sensing
The components such as device, current sensor, assist motor and reducing gear, steering mechanism's phase that assist motor passes through transmission mechanism and automobile
Even.EPS system is its basis there are various control pattern, wherein Power assisted control.
Method for Power assisted control is that the control accuracy of diversified, traditional control method is low, poor robustness, is rung
Long between seasonable, such that motor provides the delay of power-assisted, comfort and security to automobile cause very big influence.Compared with
It is Sliding mode variable structure control for preferable control method, but traditional Sliding mode variable structure control is in the prevalence of chattering phenomenon.
The stability of running car can be influenced by buffeting, and then influence the security of automobile.To solve the above-mentioned problems, it is necessary to have a kind of new
Electric boosting steering system power assist control method.
The present invention devises the modified fuzzy sliding mode controlling method based on genetic algorithm optimization, not only efficiently reduces system
Buffet, yet further enhances the control accuracy and robustness of system.And the characteristics of due to Sliding mode variable structure control, the control
Method can be applied on the automobile of a variety of different models.
The content of the invention
The present invention provides a kind of electric boosting steering system power assist control method based on complex controll, it is therefore intended that subtracts
It is small or even eliminate chattering phenomenon present in traditional Sliding mode variable structure control, further improve the control accuracy and robust of system
Property.When vehicle enters steering state, dtc signal and car that the ECU of electric boosting steering system is measured according to torque sensor
The speed signal that fast sensor measures obtains required target current, the actual current and target current that current sensor is measured
Input of the difference as current controller, it is ensured that actual current can quickly and accurately follow target current, be come with this fast
Speed and power torque is accurately provided.The technical solution adopted in the present invention is：
A kind of electric boosting steering system power assist control method based on complex controll, it is characterised in that：The first step, design
Sliding Mode Controller is controlled the current error of electric boosting steering system；Second step, a dual input list is defeated
The fuzzy control gone out is incorporated into the Sliding Mode Controller of the first step, the key parameter in sliding formwork control is had necessarily
Adaptive characteristic；3rd step, genetic algorithm optimization is incorporated into the fuzzy control of second step, fuzzy control rule is carried out excellent
Change.The key property of this method is：Fuzzy control is applied to sliding formwork control, reaches the key parameter in optimization sliding formwork control
Effect, while by genetic algorithm optimization fuzzy rule, the accuracy of fuzzy rule is improved with this, is finally realized to EPS systems
The complex controll of system, improves the robustness and control accuracy of system, meanwhile, reduce the chattering phenomenon for even eliminating system.
In the first step, by the target current I needed for systemmThe current actual current with current sensor collection assist motor
The deviation e of I is as state variable, with reference to the exponential approach rate in sliding formwork control, with reference to the electrical characteristic of assist motor, most
Tentatively obtain sliding mode controller eventually.The sign function sgn (s) in preferable sliding mode is replaced with saturation function sat (s), by
The switching control item serialization of formation, forms the boundary layer with certain neighborhood, the robustness of strengthening system.
In second step, the exponential approach rate formula in sliding formwork control isε can significantly affect system
Performance, self adaptive control is carried out according to Fig. 3 using fuzzy control to it, if the input s of fuzzy controller andOn fuzzy domain
Fuzzy subsetExport fuzzy subset εs of the ε on fuzzy domain=NB, NS, ZO, PS,
PB}.Thus it may make up the Fuzzy Sliding Model Controller to assist motor control；
In 3rd step, the fuzzy rule in the fuzzy control to second step is optimized using genetic algorithm, specifically
The rule optimization process is controlled to be：The parameter of random initializtion initial population, calculate target function value and accordingly just when；Calculate
Initial population just when summation, most value, average value and maximum sample sequence number；Calculate individual just when with average just when it
Than with individual reproduction of the ratio more than 1, keeping individual number in population constant；Matched somebody with somebody two-by-two at random in previous generation populations
It is right, randomly selected using crossover probability as 0.85 and intersect position progress crossover operation, just thus kind of new generation is produced as 0.01. using probability
Group, calculate each sample target function value machine maximum and just when；Calculate population of new generation just when summation, most be worth, it is average
Value and maximum sample sequence number；Judge whether to meet end condition, satisfaction then terminates, is unsatisfactory for repeating the above process；By maximum
Just when sample decoding, optimized control rule.
Primely, current controller of the invention will use the fuzzy sliding mode variable structure control based on genetic algorithm optimization,
Since Sliding Mode Controller has a switching characteristic, and it is unrelated with system parameter in itself and disturbance, therefore, the control method
It can be applied on the automobile of different model.
Primely, the present invention utilizes the parameter in fuzzy control optimization sliding formwork control so that parameter can be according to different
Adaptive change occurs for operating mode, while using the control rule in genetic algorithm optimization fuzzy control, enhances the control of system
Precision, further increases the adaptive characteristic and robustness of system, meanwhile, reduce and even eliminate traditional sliding moding structure control
Chattering phenomenon specific to system.
Below in conjunction with attached drawing, the advantages of the present invention will be described in detail and feature.
Brief description of the drawings
Fig. 1 is electric boosting steering system sub-model schematic diagram；
Fig. 2 is the logic diagram of electric boosting steering system power assist control method；
Fig. 3 is fuzzy sliding mode variable structure control block diagram；
Fig. 4 is genetic algorithm flow chart.
Embodiment
Such as Fig. 2, a kind of electric boosting steering system, including Micro-processor MCV 1, and the steering wheel torque being attached thereto
Sensor 2, vehicle speed sensor 3, PWM assist motors drive module 4, assist motor 5 and feedback current acquisition module 6, microprocessor
The speed signal that device MCU1 gathers the torque signal produced by steering wheel torque sensor 2 in real time and vehicle speed sensor 3 produces, and
The target current for the assist motor that electric boosting steering system should export is obtained by built-in assist characteristic curve, by the parameter
It is input to fuzzy controller to be handled, the pulsewidth adjusted through Micro-processor MCV is exported to PWM assist motors drive module 4
Modulated signal (PWM), controls assist motor 5 to export booster torquemoment, while feedback current by way of controlling PWM ripple duty cycles
6 acquisition modules gather assist motor actual current in real time, and the fuzzy controller 7 in input microprocessor MCU1, forms closed loop
Control.
The invention discloses a kind of electric boosting steering system power assist control method based on complex controll, wherein key exists
It is as follows in the design to current controller, specific implementation：
1. when vehicle enters steering state, torque that the ECU of electric boosting steering system is measured according to torque sensor
The speed signal that signal and vehicle speed sensor measure obtains required target current, the actual current that current sensor is measured with
Input of the difference of target current as current controller, it is ensured that actual current can quickly and accurately follow target current,
With this comes quick and accurately provides power torque.
2. by the deviation of the target current Im needed for system and current sensor collection assist motor current actual current I
E obtains switching function as state variable：
S=ce=c (Im- I) formula (1)
C is proportionality coefficient in formula (1), ImFor target current, I is actual current.
To s derivations：
Exponential approach rate design sliding mode observer is chosen, i.e.,：
ε is the speed of system motion point convergence diverter surface in formula 3.
Actual sliding formwork control is moved, it is necessary between different control logics to enable a system to be maintained on sliding-mode surface
Switching, thus form " buffeting "." buffeting " phenomenon by influence system accuracy, increase the energy consumption of system, system caused
Serious infringement, replaces the sign function sgn (s) in preferable sliding mode, by established switching with saturation function sat (s)
Control item serialization, forms the boundary layer with certain neighborhood, can so reduce or even eliminate and buffet, the robust of strengthening system
Property.
Wherein：Formula (4)
It is hereby achieved that the switching control item expression formula of system is：
Us=ε sat (s)+ks formula (5)
ε is the speed of system motion point convergence diverter surface in formula (5).
To sum up and the electrical characteristic of assist motor is combined, the expression formula that can obtain sliding mode controller is：
K in formula 6bFor back EMF coefficient；θmFor steering wheel rotation angle；R, L are respectively armature resistance and electricity
Sense.
3. in sliding formwork control, exponential approach rate formula isε can significantly affect the performance of system,
Reduce ε values, system chatter can be obviously reduced, but ε values are too small, when the system that can influence reaches velocity of approach and the transition of diverter surface
Between.Consider the uncertainty of parameter, in boundary layer according to s andFuzzy value carry out blurring adjusting to ε, and then realize pair
The adjusting of switching control item.
Self adaptive control is carried out to it using fuzzy control according to Fig. 3, if the input s of fuzzy controller andIn fuzzy theory
Fuzzy subset on domainExport fuzzy subset εs of the ε on fuzzy domain=NB, NS, ZO,
PS, PB }.Thus it may make up the Fuzzy Sliding Model Controller to assist motor control；
4. the fuzzy rule in fuzzy control is optimized using genetic algorithm, since fuzzy controller is dual input,
And each input is 5 fuzzy sets, therefore 25 control rules are shared, the fuzzy language value of 1 ε is corresponded to per rule.Will be fuzzy
Control rule input s,Encoded respectively with 5 fuzzy language values { PB, PS, ZO, NS, NB } of output ε, coding mode is
Binary code is translated into, is followed successively by 000,001,010,011,100.It is converted into rule in the engineering of binary string, only
Corresponding fuzzy rule need to be connected one by one.
The object function that the present invention uses for：
J=∫ t | e | dt formula (7)
In formula (7) | e | the absolute value of the error output and input for system, J values are smaller, then the performance of system is better.
, can be by object function discretization, for the ease of realizing to obtain the final product
Δ t is the sampling interval in formula (8), general very little, when taking τ=t, then Δ J=t | and E | Δ t.
Fitness function can make object function appropriate conversion and obtain.Here it is possible to determined using equation below：
It is specific to control rule optimization process to finally obtain the Fuzzy Sliding Model Controller based on genetic algorithm for such as Fig. 4.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limits the scope of the invention.
After the content for having read the record of the present invention, technical staff can make various changes or modifications the present invention, these equivalent changes
Change and modification equally falls into the scope of the claims in the present invention.
Claims (3)
1. a kind of electric boosting steering system power assist control method based on complex controll, this method are measured according to vehicle speed sensor
Speed signal and the dtc signal comprehensive function that measures of torque sensor in the ECU of electric boosting steering system, according in ECU
Assist characteristic curve draw target current, the difference for the actual current that target current and current sensor are measured passes through electric current
Controller action exports power torque, it includes the following steps in assist motor by assist motor：
The first step：The current control of electric boosting steering system is obtained using Sliding mode variable structure control, by target current and reality
The difference of electric current obtains switching function input, i.e., as state variable：
S=ce=c (Im-I) (1)
Each variable representative is respectively in formula (1)：E is target current I needed for systemmWith the power-assisted electricity of current sensor collection
The deviation of the current actual current I of machine, c are proportionality coefficients, ImIt is target current, I is actual current；
To s derivations：
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Sgn (s) is sign function in formula (3), and ε is the speed of motor point convergence diverter surface, and sgn (s) is replaced with sat (s), is formed
Boundary layer with certain neighborhood, obtains switching control item expression formula：
Us=ε sat (s)+ks (4)
Sat (s) is saturation function, i.e., after input reaches certain value, output just no longer changes, i.e., when parameter s is more than a certain
After a value, its functional value just no longer changes, and with reference to the electrical characteristic of motor, obtaining current controller is：
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In formula (5), KbIt is back EMF coefficient, θmIt is steering wheel rotation angle, R, L are armature resistance and inductance respectively；
Second step：Using fuzzy control so that the speed ε of system motion point convergence diverter surface has in Sliding Mode Controller
Certain adaptive characteristic；
3rd step：Utilize the fuzzy control rule in genetic algorithm optimization fuzzy control.
2. a kind of electric boosting steering system power assist control method based on complex controll according to claim 1, it is special
Sign is：In second step, the fuzzy control that dual input list exports is introduced into first step Sliding mode variable structure control, optimizes sliding formwork control
Key parameter ε in system, form to assist motor control Fuzzy Sliding Model Controller, if the input s of fuzzy controller andIn mould
The fuzzy subset s on domain is pasted,Export fuzzy subset εs of the ε on fuzzy domain=NB, NS,
ZO, PS, PB }, by the blurring to input and output, fuzzy rule is formulated, obtains ε's finally by the anti fuzzy method to output
Value.
3. a kind of electric boosting steering system power assist control method based on complex controll according to claim 1, it is special
Sign is：In 3rd step, genetic algorithm is incorporated into the fuzzy control of second step, fuzzy control rule is optimized, optimized
Process：The parameter of random initializtion initial population, calculate target function value and accordingly just when；Calculate initial population just when
Summation, most value, average value and maximum sample sequence number；Calculate individual just when with it is average just when the ratio between, with ratio more than 1
Individual reproduction, keeps individual number in population constant；Matched two-by-two at random in previous generation populations, using crossover probability as
0.85, which randomly selects intersection position, carries out crossover operation, and mutation probability 0.01, thus produces population of new generation, calculate each sample
Target function value machine maximum and just when；Calculate population of new generation just when summation, most value, average value and maximum sample sequence
Number；Judge whether to meet end condition, satisfaction then terminates, is unsatisfactory for repeating the above process；By maximum just when sample decode, obtain
To the control rule of optimization.
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| GB201609872D0 (en) * | 2016-06-06 | 2016-07-20 | Trw Ltd | Quadrant dependent damping |
| CN105946858B (en) * | 2016-06-08 | 2019-02-15 | 吉林大学 | Four-drive electric car state observer parameter optimization method based on genetic algorithm |
| CN107253453B (en) * | 2017-07-05 | 2019-08-27 | 厦门大学 | A kind of distributed electric automobile lateral stability adaptive control system and method |
| CN108646751A (en) * | 2018-06-11 | 2018-10-12 | 南京航空航天大学 | Automatic steering control system based on genetic algorithm and Single neuron self adaptive PID and its control method |
| CN111478592A (en) * | 2020-05-09 | 2020-07-31 | 哈尔滨理工大学 | Sliding mode control method of double-active full-bridge DC-DC converter |
| CN112026777B (en) * | 2020-07-23 | 2021-09-17 | 南京航空航天大学 | Vehicle composite steering system and mode switching control method thereof |
| CN115848488B (en) * | 2023-02-09 | 2023-07-07 | 安徽大学 | Drive-by-wire steering system based on self-adaptive tracking control and control method |
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