CN112874504B - Control method of extensible entropy weight combined controller - Google Patents

Control method of extensible entropy weight combined controller Download PDF

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CN112874504B
CN112874504B CN202110201272.8A CN202110201272A CN112874504B CN 112874504 B CN112874504 B CN 112874504B CN 202110201272 A CN202110201272 A CN 202110201272A CN 112874504 B CN112874504 B CN 112874504B
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characteristic quantity
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longitudinal speed
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CN112874504A (en
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汪洪波
胡承磊
高含
蔡云庆
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Hefei University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T13/00—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems
    • B60T13/74—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T7/00—Brake-action initiating means
    • B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/18—Conjoint control of vehicle sub-units of different type or different function including control of braking systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
    • B62D6/002—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits computing target steering angles for front or rear wheels
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/18—Braking system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems

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Abstract

The invention discloses a control method of an extensible entropy weight combined controller. The method for controlling the extensible entropy weight combined controller selects the front wheel rotation angle and the longitudinal speed of the racing car of the unmanned formula racing car as characteristic quantities, and calculates the section area, the stable area and the extensible area of the characteristic quantities of each characteristic quantity when the racing car runs; calculating real-time information entropy according to the characteristic quantity; calculating the weight of each characteristic quantity according to the information entropy; calculating the association degree between the characteristic quantity and the stable domain according to the stable domain of the characteristic quantity; and determining the corner control output and the brake control output of the racing car according to the association degree and the weight. The invention carries out combined control on steering and braking through the extendible entropy weight combined controller, greatly reduces the interference caused by the coupling relationship between a steering control system and a braking system, reduces the risk of deviating from the race track caused by over-high speed or insufficient steering real-time performance of the racing car, improves the overall performance of the racing car and reduces the control cost.

Description

Control method of extensible entropy weight combined controller
The invention relates to a divisional application of a steering and braking coordination control method and system of an unmanned formula racing car, which has the application number of CN202010024663.2 and the application date of 2020/01/10.
Technical Field
The invention relates to a control method of an unmanned formula racing car in the field of unmanned driving, in particular to a control method of an extensible entropy weight combined controller of the unmanned formula racing car.
Background
The unmanned formula competition of college students in China is a car design and manufacturing competition sponsored by the Chinese automobile engineering society and participated in by college students in schools of the related major of all college cars. As the race progresses, the speed of the race cars in a particular track increases.
Independent steering or braking control is mostly adopted when the domestic unmanned formula racing car passes a bend at the present stage, so that the bend is decelerated greatly and the racing car smoothly passes the bend, or slow-speed constant-speed control is directly adopted to ensure that the racing car does not deviate from a track when passing the bend, but the two methods greatly increase the duration of a single turn. Similar to a common household car, when a racing car turns over a curve, a steering and braking system has a certain coupling relation and strong nonlinearity, and if steering or braking control is independently adopted during the turning over, the lateral stability of the racing car is to be improved, and the racing car can be knocked down to a pile barrel or deviated from a track.
Disclosure of Invention
The invention provides a control method of an extension entropy weight combined controller of an unmanned formula racing car, which aims to reduce the risk of deviating a racing track caused by over-high speed or insufficient steering real-time performance of the racing car, solve the problem of controlling the lateral stability of the unmanned formula racing car based on steering and braking, overcome the coupling effect of vehicle steering and braking dynamics, improve the overall performance of the racing car and reduce the control cost.
The invention is realized by adopting the following technical scheme: a control method of an extensible entropy weight combined controller of an unmanned formula racing car comprises the following steps:
firstly, selecting the front wheel corner delta of the formula racing car of the unmanned formula racing carfAnd taking the longitudinal speed u of the racing car of the unmanned formula racing car as a characteristic quantity, and calculating a section P, a stable domain J and an extension domain E of the characteristic quantity when the racing car runs;
node region P is
Figure GDA0003437209660000021
A stable region J of the characteristic quantity of
Figure GDA0003437209660000022
Has an extension field E of
Figure GDA0003437209660000023
Wherein, deltafCharacterizing steering control characteristics of the racing vehicle; u characterizes a brake control characteristic of the racing car; deltaf1Is the maximum front wheel angle, delta, of said car racef2Maximum front wheel angle u for ensuring normal running of said racing car1Is the maximum longitudinal speed, u, of said racing car2A maximum longitudinal speed for ensuring normal running of the racing car;
two, define D (δ)fU) is the characteristic state of the racing car at a certain moment, and the characteristic quantity is normalized to obtain the normalized characteristic quantity D (delta)f', u'), the normalized formula is:
D(δf′,u′)＝(D(δf,u)-Dmin(δf,u))/(Dmax(δf,u)-Dmin(δf,u))
wherein D ismin(δfU) and Dmax(δfU) is obtained from the node region P;
thirdly, calculating real-time information entropy E (delta) according to the characteristic quantityfU), said real-time information entropy E (δ)fThe calculation method of u) comprises the following steps:
calculating a distance L (delta) between the feature quantity and the stable regionf,u)：
Figure GDA0003437209660000024
According to the available distance L (delta)fU), feature quantity D (δ) after normalizationf', u') computing the entropy E (delta)f,u)：
Figure GDA0003437209660000025
Figure GDA0003437209660000026
n is a positive integer;
fourthly, according to the information entropy E (delta)fU) calculating the weight W (delta) of each feature quantityf,u)：
Figure GDA0003437209660000027
K represents a Kth feature quantity;
fifthly, calculating the association degree K (D) between the characteristic quantity and the stable domain according to the stable domain J of the characteristic quantity;
Figure GDA0003437209660000031
sixthly, determining the rotation angle control output and the brake control output of the racing car according to the degree of association K (D):
when K (D) is more than or equal to 0, the characteristic state of the racing car is in a stable domain, the longitudinal speed is low, the racing car can smoothly pass a bend without adopting braking action, and the control output of the turning angle is
Figure GDA0003437209660000032
δf *Is an ideal front wheel corner;
when-1 is equal to or less than K and (D) is equal to or less than 0, thenThe characteristic state of the racing car is in an extension range, the longitudinal speed is high, and the weight W (delta) of the characteristic quantity is considered through the combined control of steering control and braking controlfU) the rotation angle control output is
Figure GDA0003437209660000033
The brake control output is Y (u) ═ W (u) K (D) u*，u*The ideal longitudinal speed;
when K (D) is less than-1, the longitudinal speed is too fast, the vehicle speed is greatly reduced, then the steering operation is carried out based on the upper layer command, and the brake control output is Y (u) ═ W (u) K (D) u*。
As a further improvement of the scheme, the environment sensing system senses the surrounding track and plans the ideal running track of the racing car, and the track tracking algorithm is based on the track tracking algorithm according to the front wheel turning angle delta of the racing carfAnd calculating the ideal front wheel corner delta from the longitudinal speed u of the racing carf *And a desired longitudinal speed u*。
The invention aims at a steering and braking coordination control system of the unmanned formula racing car, and performs combined control on steering and braking through the extendible entropy weight combined controller, thereby greatly reducing the interference between the steering control system and the braking system caused by the coupling relationship, reducing the risk of deviating from a racing track due to over-high speed or insufficient steering real-time property of the racing car, improving the overall performance of the racing car and reducing the control cost.
Drawings
FIG. 1 is a flow chart of a method for coordinating steering and braking of an unmanned formula racing car according to the present invention;
FIG. 2 is a schematic diagram of a steering control system of the coordinated control method for steering and braking of the formula racing unmanned vehicle according to the invention;
FIG. 3 is a schematic diagram of a brake control system of the coordinated control method for steering and braking of the formula racing unmanned vehicle according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses an unmanned formula racing car steering and braking coordination control system which comprises an extension entropy weight combined controller, an incremental PID (proportion integration differentiation) -based steering control system and an adaptive fuzzy neural network-based braking control system.
The invention also discloses a coordination control method for steering and braking of the unmanned formula racing car, which comprises the following specific steps:
1. the environment sensing system of the unmanned formula racing car plans an ideal running track of a racing car by sensing a peripheral track and based on a track tracking algorithm according to a front wheel corner delta of the racing carfAnd calculating the ideal front wheel corner delta from the longitudinal speed u of the racing carf *And a desired longitudinal speed u*。
2. A control method of an extensible entropy weight joint controller.
Referring to fig. 1, the extensible entropy weight combination controller selects the front wheel corner δ of the racing carfAnd longitudinal speed u of racing car as characteristic quantity, front wheel steering angle deltafCharacterizing the steering control characteristics of the racing car; the longitudinal speed u characterizes the braking control characteristics of the racing car.
And determining the value range of each characteristic quantity when the racing car runs, wherein the range is the section area of the characteristic quantity and is represented by P. Then
Figure GDA0003437209660000041
A stable region J of the characteristic quantity of
Figure GDA0003437209660000042
Has an extension field E of
Figure GDA0003437209660000051
Wherein, deltaf1Is the maximum front wheel angle, delta, of said car racef2Maximum front wheel angle u for ensuring normal running of said racing car1Is the maximum longitudinal speed, u, of said racing car2To ensure the maximum longitudinal speed of the car for normal running. Taking the steering and speed characteristics of the formula of unmanned racing car into consideration and taking deltaf1＝30°，δf2＝18°，u1＝25km/h，u2＝15km/h。
Definition D (δ)fU) represents a characteristic state of the racing car at a certain time, and the characteristic quantity is normalized to obtain a normalized characteristic quantity D (delta'fU'), standardized by the formula
D(δ′f,u′)＝(D(δf,u)-Dmin(δf,u))/(Dmax(δf,u)-Dmin(δf,u))
Wherein Dmin(δfU) and Dmax(δfU) is available from the node region P.
The extension L (delta) between the feature quantity and the stable region is calculated by the following formulaf,u)。
Figure GDA0003437209660000052
Computing real-time information entropy
Figure GDA0003437209660000053
Wherein the content of the first and second substances,
Figure GDA0003437209660000054
n is a positive integer.
Calculating the weight W (delta) of each feature quantity according to the information entropy obtained abovef,u)：
Figure GDA0003437209660000055
Where K denotes the kth characteristic amount.
To calculate the degree of association between the feature quantity and the stable domain, the following association function is defined
Figure GDA0003437209660000056
When K (D) is more than or equal to 0, the characteristic state of the racing car is in a stable domain, the longitudinal speed of the racing car is low at the moment, the racing car can smoothly pass a curve without adopting a braking action, a steering execution mechanism at the bottom layer strictly tracks a target corner sent from the upper layer, and a corner value is output at the bottom layer
Figure GDA0003437209660000061
When K is larger than or equal to-1 and D is smaller than or equal to 0, the characteristic state of the racing car is in the extension range, the longitudinal speed of the racing car is higher, a combined control strategy based on steering control and braking control is adopted, and the purpose is to keep the racing car transversely stable and avoid the racing car from deviating from the track and shorten the overbending time. Considering the weight of the characteristic quantity, the turning angle control output is
Figure GDA0003437209660000062
The brake control output is Y (u) ═ W (u) K (D) u*. The extendible domain is a domain in which output steering and brake coordination control is required.
When K (D) is less than-1, the longitudinal speed is too high, the vehicle speed needs to be reduced greatly, and then the steering operation is carried out based on the upper layer command, and the brake control output is Y (u) ═ W (u) K (D) u*。
3. The control method of the steering control system based on the incremental PID comprises the following steps: the incremental PID algorithm is used for controlling a racing car steering control system, the incremental PID algorithm is used for controlling the difference between an ideal rotating angle and an actual rotating angle, the algorithm process does not need to be accumulated, and the size of the current difference is only closely related to the difference of the last three times.
The racing car uses the super group servo motor of the era of 36V power supply as a steering control system driver, the rated torque of the super group servo motor is 1.27 N.m, and the speed reduction ratio of the super group servo motor is 1: 24. The PWM duty ratio signal is adopted to control the rotating speed of the motor, the motor rotates to drive the steering column and the front wheel to rotate, and the steering wheel corner sensor positioned above the steering column can read the steering wheel corner in real time. The ideal steering wheel angle can be obtained by calculating the ideal front wheel angle and the transmission ratio of a steering control system, and in order to stop the motor at a target position, the number of PWM pulses for controlling the motor to rotate until the actual steering wheel angle is consistent with the ideal steering wheel angle is calculated by using an incremental PID algorithm.
The incremental PID control is performed when 2 ° is set as a threshold value between the ideal steering wheel angle and the actual steering wheel angle, i.e., when the difference between the two exceeds ± 2 °. If the actual steering wheel angle is 5 degrees and the ideal steering wheel angle is 10 degrees, the PID algorithm is used for calculating that 1000 PWM waves are theoretically needed to enable the steering wheel to rotate from 5 degrees to 10 degrees, and the PWM waves are used for driving the servo motor to rotate next step. However, due to an algorithm error, 1000 PWM waves may actually rotate the steering wheel to a position where the actual steering wheel angle is 8 °, and since the incremental PID control is performed when the difference between the two set values exceeds ± 2 °, the default control is terminated when the actual steering wheel angle is 8 °.
The number of PWM pulses is:
CI＝Kp(e(k)-e(k-1))+Kie(k)+Kd(e(k)-2e(k-1)+e(k-2))
wherein e (k) ═ Y (δ) - δfIs the error value of this time;
e (k) -e (k-1) is the difference value of the current error and the last error;
and e (k-1) -e (k-2) is the difference value of the last error and the last error.
For the calculation of the number of PWM pulses at a certain moment, there are
Figure GDA0003437209660000071
After a plurality of real vehicle tests, K is takenp＝1,Ki＝0,KdWhen the value is 0.01, the control effect is preferable.
4. The control method of the brake control system based on the self-adaptive fuzzy neural network comprises the following steps: adaptive fuzzy neural network systems are also known as network-based adaptive fuzzy systems, abbreviated ANFIS, and were proposed by Jang Roger in 1993. The neural network learning system integrates the advantages of a learning mechanism of a neural network, the language reasoning ability of a fuzzy system and the like, makes up the respective defects, and belongs to a neural fuzzy system. Compared with other neuro-fuzzy systems, the ANFIS has the characteristics of convenience and high efficiency, and is successfully applied in a plurality of fields.
The control of the fuzzy neural network of the racing car brake control system has five levels for control:
the first layer is a blurring layer, which is responsible for blurring the input signal. Wherein the input signal is the longitudinal speed error e of the racing caruAnd acceleration error eaThe number of network nodes is 2. And calculating the membership degree of each node by applying a bell-shaped function as follows:
Figure GDA0003437209660000072
wherein x is an input to a node i (i ═ 1, 2); j is 1, 2; { ai，bi，ciThe set of antecedent parameters whose changes will affect the specific shape of the membership function, which may also be other suitable parameterized functions.
Defining:
eu＝Y(u)-u
the second layer is a rule layer, which is used to calculate the excitation strength of each rule, and multiplies the two input signals, the product of which is output as
Figure GDA0003437209660000081
GiFor the output of the ith node, the output of each node represents the fitness of a rule.
The third layer is a normalization layer used to calculate the normalized excitation strength of the rule, i.e., the normalized confidence of the ith rule.
Figure GDA0003437209660000082
The fourth layer is a node output layer, and the output of each node is calculated.
Figure GDA0003437209660000083
In the formula (f)i＝piea+qieu+ri，{pi，qi，riAnd the parameter set of the node is called a back-piece parameter.
The fifth layer is a total output layer, and the layer can calculate the sum of all input signals to obtain the brake oil pressure.
Figure GDA0003437209660000084
The front part parameters and the back part parameters of the membership functions are adjusted by adopting a hybrid learning algorithm combining a back propagation method and a least square method, so that the self-learning function of the fuzzy system based on data can be realized.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (2)

1. A control method of an extensible entropy weight combined controller of an unmanned formula car is characterized by comprising the following steps:
firstly, selecting the front wheel corner delta of the formula racing car of the unmanned formula racing carfAnd taking the longitudinal speed u of the racing car of the unmanned formula racing car as a characteristic quantity, and calculating a section P, a stable domain J and an extension domain E of the characteristic quantity when the racing car runs;
node region P is
Figure FDA0003437209650000011
A stable region J of the characteristic quantity of
Figure FDA0003437209650000012
Has an extension field E of
Figure FDA0003437209650000013
Wherein, deltafCharacterizing steering control characteristics of the racing vehicle; u characterizes a brake control characteristic of the racing car; deltaf1Is the maximum front wheel angle, delta, of said car racef2Maximum front wheel angle u for ensuring normal running of said racing car1Is the maximum longitudinal speed, u, of said racing car2A maximum longitudinal speed for ensuring normal running of the racing car;
two, define D (δ)fU) is a characteristic state of the racing car at a certain time, and the characteristic quantity is normalized to obtain a normalized characteristic quantity D (delta'fU'), the normalized formula is:
D(δ′f,u′)＝(D(δf,u)-Dmin(δf,u))/(Dmax(δf,u)-Dmin(δf,u))
wherein D ismin(δfU) and Dmax(δfU) is obtained from the node region P;
thirdly, calculating real-time information entropy E (delta) according to the characteristic quantityfU), said real-time information entropy E (δ)fThe calculation method of u) comprises the following steps:
calculating a distance L (delta) between the feature quantity and the stable region Jf,u)：
Figure FDA0003437209650000014
According to the available distance L (delta)fU), characteristic quantity D (δ ') after normalization'fU') computing the entropy E (delta)f,u)：
Figure FDA0003437209650000021
Figure FDA0003437209650000022
n is a positive integer;
fourthly, according to the information entropy E (delta)fU) calculating the weight W (delta) of each feature quantityf,u)：
Figure FDA0003437209650000023
K represents a Kth feature quantity;
fifthly, calculating the degree of association K (D) between the characteristic quantity and the stable domain E according to the stable domain J of the characteristic quantity;
Figure FDA0003437209650000024
sixthly, determining the rotation angle control output and the brake control output of the racing car according to the degree of association K (D):
when K (D) is more than or equal to 0, the characteristic state of the racing car is in a stable region J, the longitudinal speed u is small, the racing car can smoothly pass a bend without adopting a braking action, and the control output of the turning angle is
Figure FDA0003437209650000025
δf *Is an ideal front wheel corner;
when K is-1 ≦ K (D ≦ 0), the racing car feature state is within the extension area E, the longitudinal speed u is large, and the weight W (δ) of the feature quantity is considered by the combined control of the steering control and the braking controlfU) the rotation angle control output is
Figure FDA0003437209650000026
The brake control output is Y (u) ═ W (u) K (D) u*，u*The ideal longitudinal speed;
when K (D) is less than-1, the longitudinal speed u is too fast, the vehicle speed is greatly reduced, then the steering operation is carried out based on the upper layer command, and the brake control output is Y (u) ═ W (u) K (D) u*。
2. The method for controlling the extendible entropy weight joint controller of the formula racing unmanned vehicle as claimed in claim 1, wherein: sensing the surrounding track through an environment sensing system, planning an ideal running track of the racing car, and tracking the algorithm according to the front wheel turning angle delta of the racing car based on the trackfAnd calculating the ideal front wheel corner delta from the longitudinal speed u of the racing carf *And a desired longitudinal speed u*。
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