CN118151531B - Distributed electric vehicle multi-agent cooperative control method based on cooperative game - Google Patents
Distributed electric vehicle multi-agent cooperative control method based on cooperative game Download PDFInfo
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Abstract
The invention discloses a distributed electric vehicle multi-agent cooperative control method based on cooperative game, which comprises the steps of firstly dividing each subsystem of a distributed electric vehicle into 6 agents, constructing a dynamic model representing each subsystem agent, combining a vehicle-mounted sensor to collect state information of the vehicle in real time, adopting a T-S fuzzy theory to treat a system nonlinearity problem caused by vehicle speed change, establishing a distributed electric vehicle subsystem dynamic cooperative control strategy based on the cooperative game theory, and finally establishing a robust compensation control strategy to reduce the influence of system disturbance and uncertainty on system control and realize intelligent cooperative control and efficient safe running of the distributed electric vehicle. Compared with the traditional layered control method, the method is safer and more reliable, can dynamically adjust the control output of each intelligent body in real time, and provides a new exploration direction for the modularized cooperative control of the distributed electric automobile.
Description
Technical Field
The invention relates to the field of intelligent interaction of distributed electric vehicles and the field of advanced auxiliary driving of vehicles, in particular to a distributed electric vehicle multi-agent cooperative control method based on cooperative game.
Background
The current distributed electric vehicle taking the hub motor as a drive control unit has a torque control mode with multiple actuators which are independently controllable, fast in response and accurately executed, and a simple chassis framework of the distributed electric vehicle endows the vehicle with a larger stable control lifting room, so that the distributed electric vehicle is considered by students in the field of home and abroad vehicles to be one of the electric vehicle frameworks with the most development potential. However, because the coupling relation exists among the dynamics and the kinematics of the hub motor, the wheels and the steering mechanism, when different controls are interposed, the execution contradiction and even the local failure of the control can be possibly caused due to the overlapping interference of functions, and particularly, in the process that a driver participates in driving the control loop, the execution contradiction is more easily caused by a plurality of execution mechanisms due to the stronger subjectivity of the driver. Therefore, a reasonable and effective real-time dynamic coordination control scheme of multiple execution mechanisms is established, and the method plays a very important role in improving the active safety of the distributed electric automobile and further promoting the transformation of the automobile industry. Particularly, an executing mechanism of the distributed electric automobile divides the intelligent bodies by functional units, a dynamic model of each intelligent body is built, a dynamic coordination control framework based on cooperative game is built, meanwhile, system disturbance and uncertainty are effectively eliminated by adopting robust compensation control, so that safe and efficient operation of the distributed electric automobile is better ensured, and theoretical basis and technical support are provided for research of the distributed electric automobile in China.
The prior art discusses a stability control method of a distributed electric automobile, which realizes stability control of the distributed electric automobile by optimally distributing lateral force and longitudinal force of a tire, and improves stability and drivability of a vehicle system. The method has the advantages that the real-time effective interaction of each actuator of the vehicle cannot be realized, only the coordination relation between the steering of the front wheels and the yaw moment is considered, and the coupling association of the vehicle under the intervention of a driver is not considered, so that the method cannot better meet the cooperative control function of multiple actuators of the distributed electric vehicle under various environments, and the problem of low intelligent level exists.
Disclosure of Invention
The invention aims to solve the technical problem of providing a coordination control method capable of dynamically coordinating the control input of each intelligent agent of a distributed electric automobile, so that the problems of coupling conflict and even local failure possibly caused by incomplete information interaction of an actuator and overlapping interference of functions are effectively solved, the intelligent level is high, and the practicability is high.
In order to solve the technical problems, the invention adopts the following technical scheme:
the multi-agent cooperative control method for the distributed electric vehicle based on the cooperative game comprises the following steps of
Step S1: aiming at the functions of each subsystem of the distributed electric automobile, an intelligent control area of a steering system is established, and the intelligent control area is divided into a driver intelligent body and a steering auxiliary control intelligent body; in the intelligent control area of the driving/braking system, the distributed electric automobile controls the driving/braking of the automobile for an in-wheel motor, and four wheels are independently controllable, so that a left front wheel intelligent body, a left rear wheel intelligent body, a right front wheel intelligent body and a right rear wheel intelligent body are established;
Step S2: aiming at the driver intelligent agent, the steering auxiliary control intelligent agent, the left front wheel intelligent agent, the left rear wheel intelligent agent, the right front wheel intelligent agent and the right rear wheel intelligent agent which are defined in the step S1, 6 intelligent agents are combined, and a dynamics model for representing each intelligent agent is built, so that a vehicle system dynamics model is built;
step S3: carrying out linearization treatment on a vehicle system by adopting a T-S fuzzy theory, and establishing a system state equation after linearization treatment;
Step S4: establishing a distributed electric vehicle multi-agent coordination control strategy based on cooperative game;
step S5: aiming at the problem that the disturbance of the system cannot be eliminated in the cooperative game, a robust compensation control strategy is established, and efficient and safe dynamic coordination control of 6 intelligent bodies of the distributed electric automobile is realized.
Preferably, step S2 builds a kinetic model characterizing each agent for the 6 agents defined in step S1, thereby building a vehicle system kinetic model, comprising the steps of:
Step S21: establishing a dynamics equation for representing the intelligent agent of the driver:
Kinetic expression of the hand torque input of the driver agent:
Wherein τ h is the steering torque input of the driver agent, J h is the equivalent inertia of the steering system, B h is the steering system damping, τ f is the steering system friction torque, τ e is the steering assist torque provided by the steering system motor, δ is the steering wheel angle, sign () is the sign function for indicating the sign of the parameter;
Step S22: establishing a dynamics equation of the auxiliary steering agent:
The kinetic equation expression of the auxiliary steering agent is:
τe＝IeKtη
(equation 2)
Wherein τ e is the steering torque output of the auxiliary steering agent, I e is the actual current of the steering motor, K t is the torque coefficient of the steering motor, and η is the mechanical efficiency;
step S23: dynamic model of hub motor intelligent body is established
Because the four hub motors of the distributed electric automobile have the same performance, the dynamic model of the hub motor intelligent body can be expressed as follows;
τij＝τLij+Bij+Jijωij
(equation 3)
Where ij= { ff, fr, rf, rr }, respectively represent front left, rear left, front right, rear right. Omega is the angular speed of the hub motor, tau and tau L are respectively expressed as the output torque and the load torque of the hub motor, J is the rotational inertia of the hub motor, and B is the damping coefficient;
Step S24: establishing a distributed electric automobile system model covering a driver agent
Setting the tire slip angle of the vehicle to be small, the vehicle dynamics model includes longitudinal movement, transverse movement and yaw movement, and the distributed electric vehicle system dynamics model can be expressed as:
Wherein, Delta is the steering wheel angle of the steering wheel,Is the steering wheel turning speed, beta is the vehicle mass center slip angle,The yaw rate of the vehicle, Y is the lateral displacement of the vehicle, omega r is the yaw angle of the vehicle, v x is the longitudinal speed of the vehicle, v y is the lateral speed of the vehicle, A is the vehicle state system matrix, B is the control input system matrix, B h is the driver control input system matrix, u= [ tau e τff τfr τrf τrr],τff ] is the left front wheel driving moment of the vehicle, tau fr is the left rear wheel driving moment, tau rf is the right front wheel driving moment, tau rr is the right rear wheel driving moment, H is the system disturbance matrix, w is the system disturbance, z is the system output, and C is the system output matrix.
Preferably, step S3 performs linearization processing on a vehicle system by using a T-S fuzzy theory, and establishes a system state equation after linearization processing, including the following steps:
step S31: setting the upper boundary of the longitudinal speed of the distributed electric automobile as Lower boundary is
Step S32: establishing a human-vehicle system model based on a T-S fuzzy system:
Comprehensively considering the nonlinear problem caused by the longitudinal speed to the human-vehicle system, and establishing a linearized human-vehicle system model based on the T-S fuzzy theory as follows:
Wherein k is the current moment of the system, v is an advance variable, ζ i (v) is a normalized membership function of a T-S fuzzy model, A i_b is a system state matrix after T-S fuzzy processing, B h_h is a driver control matrix after discretization in the system, B d is a vehicle control matrix after discretization in the system, and H d is an interference matrix after discretization in the system.
Preferably, step S4 establishes a distributed electric vehicle multi-agent coordination control strategy based on cooperative game, including the following steps:
step S41: establishing a cost function of each agent:
Aiming at the problem that the cooperative game cannot well process system interference and uncertainty, firstly, the system disturbance problem described in the step S5 is ignored, and a man-vehicle dynamics system based on the cooperative game is established as follows:
Wherein Δx is the state error amount of the system, Δτ h is the manipulation error amount of the driver, Δu is the execution error amount of the vehicle, and Δz is the error amount of the system output;
in game theory 6 agents are participants whose utility can take the form of a cost function, thus defining an infinite field of view as:
wherein n= { 23 45 6} represents an AFS steering auxiliary motor, a left front wheel hub motor, a left rear wheel hub motor, a right front wheel hub motor, a right rear wheel hub motor, Φ n represents a weight matrix of the agent n, r n represents a weight output by the agent n, J n represents a cost function of the agent n, J h represents a cost function of the driver agent, Φ h represents a weight matrix of the driver agent, and r h represents a weight output by the driver agent;
according to the interactive paradigm of cooperative gaming, all agents have a common goal, which can be described by a global cost function, which can be expressed as:
Wherein ρ h is the task weight of the driver agent in the whole game, ρ n is the task weight of the vehicle execution layer agent in the whole game,
Thus, the global objective cost function for 6 agents is:
Wherein R h,Rn and phi hk,Φnk are respectively represented as a weight matrix of system control input weights and state errors;
step S42: optimization problem of distributed electric automobile agent:
The optimization problem for 6 agents can be expressed as:
Wherein, P i_h and P i_n represent lyapunov solution feedback control rate, respectively;
step S43: dynamic coordination control rate solving method for distributed electric automobile based on cooperative game
The cooperative game is implemented by simultaneously solving a plurality of coupling optimization problems to obtain a global optimal solution, and according to the cost functions of the 6 agents established in the step S41 and the expression forms of the optimization problems of the 6 agents established in the step S42, the expression forms of the model predictive control algorithm can be established and calculated:
Wherein, Phi k,Φk is represented as a weight matrix of system state errors;
The optimal solution of the system is solved through the QR algorithm, and the optimal solution of 6 intelligent agents can be obtained as follows:
Wherein, ψ h and ψ n represent the optimal solution matrices of the driver control input, the auxiliary steering torque control input, the left front wheel control input, the left rear wheel control input, the right front wheel control input, the right rear wheel control input, respectively.
Preferably, step S5 establishes a robust compensation control strategy for the problem that the disturbance of the system cannot be eliminated in the cooperative game, so as to realize safe and efficient dynamic coordination control of 6 intelligent agents of the distributed electric automobile, including:
The problem of system disturbance and uncertainty cannot be effectively eliminated by the cooperative game theory, in order to ensure the stability of dynamic coordination control of the distributed electric automobile, the control rate of the intelligent system obtained by solving in the step (4) is further used for establishing a robust H ∞ compensation strategy for the control output of an auxiliary steering intelligent body, a left front wheel intelligent body, a left rear wheel intelligent body, a right front wheel intelligent body and a right rear wheel intelligent body, and the method can be used for obtaining:
Where ε is the stability parameter of the closed loop system given H ∞ performance, z (k) and w (k) are affected by the system;
therefore, the optimal control rate of the system can be rewritten as:
Wherein, deltaτ rh is the control moment of the driver for eliminating the system disturbance, deltau rn is the control moment of the intelligent agent for eliminating the system disturbance, deltau cn is the system disturbance compensation control rate of the intelligent agent, and Θ n is the system disturbance compensation matrix of the intelligent agent.
Above design fully considers the execution interaction between each agent and the intelligent coordinated control's of distributed electric automobile demand, can carry out real-time dynamic adjustment to the agent as required, has effectively guaranteed the travelling safety of vehicle.
Compared with the prior art, the invention has the beneficial effects that:
When the distributed electric automobile multi-Agent coordination control is carried out, the distributed electric automobile multi-Agent coordination control system is divided into a driver, steering auxiliary control, a left front wheel hub motor, a left rear wheel hub motor, a right front wheel hub motor and a right rear wheel hub motor, 6 large agents are divided into a left front wheel hub motor, a right rear wheel hub motor and are converted into Agent language for description, a vehicle model is linearized based on a T-S fuzzy theory by a dynamic modeling construction system integrated minimum control unit, the problem of vehicle nonlinearity caused by longitudinal speed time variation is solved, the distributed electric automobile is dynamically coordinated and controlled by utilizing a cooperative game theory, the interaction performance of each Agent is good, the real-time performance is strong, the coordination control performance of each executing component of the vehicle is safer and more stable than that of the traditional method, meanwhile, the system disturbance and the influence of uncertainty are greatly eliminated based on the optimized design of a robust compensation algorithm, the functional coupling conflict of the executing unit can be effectively eliminated, and the practicability is strong.
The 6 intelligent agents set in the distributed electric vehicle multi-intelligent agent cooperative control method based on the cooperative game can independently and controllably finish corresponding instruction operation, wherein the single intelligent agent is a microcosmic level of a multi-intelligent agent system, and has reactivity, autonomy and flexibility; the relationship between the intelligent agents forms a macroscopic level of multiple intelligent agents, and can complete higher flexibility, environmental adaptability and expandable comprehensive functions through organization and cooperation of each level, and the coordination control idea is very suitable for intelligent control of the distributed electric automobile.
Drawings
FIG. 1 is a schematic diagram of an intelligent auxiliary steering system of the present invention;
FIG. 2 is a logic diagram of 6 agent information interactions based on cooperative gaming of the present invention;
FIG. 3 is a schematic diagram of a distributed electric vehicle multi-agent cooperative control method based on cooperative game;
Detailed Description
The following description of the embodiments of the application is presented in conjunction with the accompanying drawings to provide a better understanding of the application to those skilled in the art. It should be noted that, in the description of the present application, the descriptions will be omitted herein when the detailed descriptions of the currently known functions and actual functions may obscure the main content of the present application, and the distributed electric vehicle of the present application refers to a distributed driving electric vehicle.
As shown in fig. 3, a distributed electric vehicle multi-agent cooperative control method based on cooperative game comprises the following steps:
Step S1: dividing a distributed electric automobile intelligent body by taking an actuator as a minimum unit, dividing functions of each subsystem of the distributed electric automobile into intelligent control areas, establishing an intelligent control area of a steering system, and dividing the intelligent control area into a driver intelligent body and a steering auxiliary control intelligent body; in the intelligent control area of the driving/braking system, the distributed electric automobile controls the driving/braking of the automobile for an in-wheel motor, and four wheels are independently controllable, so that a left front wheel intelligent body, a left rear wheel intelligent body, a right front wheel intelligent body and a right rear wheel intelligent body are established;
the minimum execution unit is described by using an Agent language:
The 6 agents are described in detail by combining the intelligent control area established in the step S1, wherein the driver agents can accurately represent the behavior characteristics of the driver, including the thinking characteristics and the maneuvering characteristics of the driver; the auxiliary steering intelligent body can autonomously and intelligently steer, and has the characteristics of high execution precision and quick response, and an active steering control function is realized; the control area of the driving/braking system can perform a distributed cooperative control function on the vehicle, and each intelligent body is independently controllable, so that the intelligent vehicle has the characteristics of high efficiency, intelligence and cooperative control; each intelligent agent is a microcosmic level of multiple intelligent agents, and has reactivity, autonomy and flexibility; the relationship among 6 intelligent agents established by the application constructs a macroscopic level of multiple intelligent agents, and can realize the intelligent and expandable comprehensive functions of the distributed electric automobile through the organizational cooperation of each level.
Step S2: the 6 constructed agents are described by adopting kinetic language,
For the driver agent, the steering assist control agent, the left front wheel agent, the left rear wheel agent, the right front wheel agent and the right rear wheel agent defined in step S1, 6 agents are combined, and a dynamics model representing each agent is constructed, so that a vehicle system dynamics model is built, including:
Step S21: establishing a dynamics equation for representing the intelligent agent of the driver:
Kinetic expression of the hand torque input of the driver agent:
Wherein τ h is the steering torque input of the driver agent, J h is the equivalent inertia of the steering system, B h is the steering system damping, τ f is the steering system friction torque, τ e is the steering assist torque provided by the steering system motor, δ is the steering wheel angle, sign () is the sign function for indicating the sign of the parameter;
Step S22: and establishing a dynamics equation of the auxiliary steering agent. Referring to fig. 1, there is a mechanical coupling between the auxiliary steering agent and the driver agent, and the driver agent can directly influence the auxiliary steering agent through the control parameter, and the auxiliary steering agent can also directly influence the driver agent. For the distributed electric automobile steering system, the dynamics characterization of the auxiliary steering agent can adopt the control moment input of the steering execution motor as a key parameter for characterizing the auxiliary steering agent, and the dynamics equation expression is as follows:
τe＝IeKtη
(equation 2)
Wherein τ e is the steering torque output of the auxiliary steering agent, I e is the actual current of the steering motor, K t is the torque coefficient of the steering motor, and η is the mechanical efficiency;
s23: and establishing a dynamic model of the intelligent body of the hub motor. For the distributed electric automobile, the four hub motors have the same performance, so that a dynamic model of the hub motor intelligent body can be expressed as follows;
τij＝τLij+Bij+Jijωij
(equation 3)
Where ij= { ff, fr, rf, rr }, respectively represent front left, rear left, front right, rear right. Omega is the angular speed of the hub motor, tau and tau L are respectively expressed as the output torque and the load torque of the hub motor, J is the rotational inertia of the hub motor, and B is the damping coefficient;
and (3) collecting experimental data of the distributed electric automobile and processing the experimental data:
And (I) collecting test data. The method comprises the steps of applying a vehicle-mounted sensor to acquire test data in real time aiming at 6 intelligent agents of a distributed electric vehicle, wherein the test data comprise a driver hand moment, a steering control moment, a driving pedal stroke, a brake pedal stroke, a steering wheel corner, a steering wheel rotating speed, a hub motor output moment, a vehicle speed, a centroid side deflection angle and a yaw angle speed;
(II) test data processing. Because the data acquisition is carried out through different sensors, the acquired partial data are not easy to intuitively understand and observe, the data are required to be subjected to unit conversion, steering wheel rotation angles and rotation speeds are converted into angle systems from radian systems, speeds are converted into Km/h from m/s, the data are divided into three types, and the driver intelligent body data, auxiliary steering intelligent body data and wheel hub motor intelligent body data are conveniently input into 6 intelligent bodies for identification. Segmenting data in each group of data, wherein each time period represents the operation behavior of the functional Agent in a period of time, and eliminating abnormal values in each data period by adopting an adaptive Kalman filtering algorithm aiming at each data period; and when the prediction result is that the error is increased, judging that the Taylor series expansion algorithm under the current iteration times is in a divergent state, and taking the measured value in the current state as a dead point to perform the rejecting operation.
Step S24: establishing a distributed electric automobile system model covering a driver agent
Setting the tire slip angle of the vehicle to be small, the vehicle dynamics model includes longitudinal movement, transverse movement and yaw movement, and the distributed electric vehicle system dynamics model can be expressed as:
Wherein, Delta is the steering wheel angle of the steering wheel,Is the steering wheel turning speed, beta is the vehicle mass center slip angle,The yaw rate of the vehicle, Y is the lateral displacement of the vehicle, omega r is the yaw angle of the vehicle, v x is the longitudinal speed of the vehicle, v y is the lateral speed of the vehicle, A is the vehicle state system matrix, B is the control input system matrix, B h is the driver control input system matrix, u= [ tau e τff τfr τrf τrr],τff ] is the left front wheel driving moment of the vehicle, tau fr is the left rear wheel driving moment, tau rf is the right front wheel driving moment, tau rr is the right rear wheel driving moment, H is the system disturbance matrix, w is the system disturbance, z is the system output, and C is the system output matrix.
Step S3: and carrying out linearization treatment on a vehicle system by adopting a T-S fuzzy theory, and establishing a system state equation after linearization treatment, wherein the method comprises the following steps:
S31: setting the upper boundary of the longitudinal speed of the distributed electric automobile as Lower boundary is
S32: establishing a human-vehicle system model based on a T-S fuzzy system:
Comprehensively considering the nonlinear problem caused by the longitudinal speed to the human-vehicle system, and establishing a linearized human-vehicle system model based on the T-S fuzzy theory as follows:
Wherein k is the current moment of the system, v is an advance variable, ζ i (v) is a normalized membership function of a T-S fuzzy model, A i_b is a system state matrix after T-S fuzzy processing, B h_h is a driver control matrix after discretization in the system, B d is a vehicle control matrix after discretization in the system, and H d is an interference matrix after discretization in the system.
A distributed electric vehicle multi-agent coordination control strategy based on a robust cooperative game comprises the following contents:
step S4: establishing a distributed electric vehicle multi-agent coordination control strategy based on cooperative game, and acquiring a reference yaw rate and a reference centroid slip angle required by a patent through a linearization vehicle model; the reference yaw-rate dynamics are characterized as:
Wherein, The reference yaw rate required by the patent is represented by L, the length of the wheelbase of the vehicle, K, the stability coefficient of the vehicle, mu, the road adhesion coefficient and g, the gravity parameter;
the reference centroid slip angle dynamics are characterized as:
wherein, beta tar is the vehicle reference mass center cornering angle, b is the distance from the vehicle rear axle to the mass center, a is the distance from the vehicle front axle to the mass center, and C r is the cornering stiffness of the vehicle rear wheel.
The reference yaw angle and the like are characterized by:
Where y p is the lateral deviation at the pretightening point and x p is the longitudinal deviation at the pretightening point.
The reference steering wheel angle dynamics is characterized as:
Wherein delta tar is the reference steering wheel angle, l is the pretightening distance based on the current speed, N ss is the steering system transmission ratio, t pr is the pretightening time, and y is the lateral displacement of the current vehicle.
The reference steering wheel angle angular velocity dynamics are characterized as:
Wherein, For reference steering wheel angular velocity, K sr is the steering column drag coefficient, and K p is the active steering torque gain coefficient.
Step S41: and establishing a cost function of each intelligent agent. Aiming at the problem that the cooperative game cannot well process system interference and uncertainty, firstly, the system disturbance problem described in the step S5 is ignored, and a man-vehicle dynamics system based on the cooperative game is established as follows:
Wherein Δx is the state error amount of the system, Δτ h is the manipulation error amount of the driver, Δu is the execution error amount of the vehicle, and Δz is the error amount of the system output;
in game theory 6 agents are participants whose utility can take the form of a cost function, thus defining an infinite field of view as:
Wherein n= { 23 45 6} represents an AFS steering assist motor, a left front wheel hub motor, a left rear wheel hub motor, a right front wheel hub motor, a right rear wheel hub motor, Φ n represents a weight matrix of agent n, r n represents a weight output by agent n, J n represents a cost function of agent n, J h represents a cost function of a driver agent, Φ h represents a weight matrix of a driver agent, and r h represents a weight output by a driver agent, respectively.
Referring to fig. 2, it can be seen that, according to the interaction paradigm of the cooperative game, all agents have a common goal, which can be described by a global cost function, which can be expressed as:
Wherein ρ h is the task weight of the driver agent in the whole game, ρ n is the task weight of the vehicle execution layer agent in the whole game,
Thus, the global objective cost function for 6 agents is:
Wherein R h,Rn and phi hk,Φnk are respectively represented as a weight matrix of system control input weights and state errors;
Step S42: the optimization problem of the distributed electric automobile intelligent body. The optimization problem for 6 agents can be expressed as:
Wherein, P i_h and P i_n represent lyapunov solution feedback control rates, respectively.
Step S43: and solving the dynamic coordination control rate of the distributed electric automobile based on the cooperative game. The cooperative game is realized by simultaneously solving a plurality of coupling optimization problems to obtain a global optimal solution, and according to the established cost functions of 6 intelligent agents and the established expression forms of the optimization problems of 6 intelligent agents, the expression forms of a model predictive control algorithm can be established and calculated:
Wherein, Phi k,Φk is represented as a weight matrix of system state errors;
The optimal solution of the system is solved through the QR algorithm, and the optimal solution of 6 intelligent agents can be obtained as follows:
Wherein, ψ h and ψ n represent the optimal solution matrices of the driver control input, the auxiliary steering torque control input, the left front wheel control input, the left rear wheel control input, the right front wheel control input, the right rear wheel control input, respectively.
Step S5, aiming at the problem that the disturbance of the system cannot be eliminated in the cooperative game, a robust compensation control strategy is established to realize the efficient and safe dynamic coordination control of 6 intelligent agents of the distributed electric automobile, and the method comprises the following steps:
The problem of system disturbance and uncertainty cannot be effectively eliminated by the cooperative game theory, in order to ensure the stability of dynamic coordination control of the distributed electric automobile, the control rate of multiple intelligent agents obtained by solving in the step (7) is further used for establishing a robust H ∞ compensation strategy for the control output of an auxiliary steering intelligent agent, a left front wheel intelligent agent, a left rear wheel intelligent agent, a right front wheel intelligent agent and a right rear wheel intelligent agent, so that the following steps can be obtained:
Where ε is the stability parameter of the closed loop system given H ∞ performance, z (k) and w (k) are affected by the system.
Therefore, the optimal control rate of the system can be rewritten as:
Wherein Deltaτ rh is the driver control moment for eliminating the system disturbance, deltau rn is the vehicle agent control moment for eliminating the system disturbance, deltau cn is the system disturbance compensation control rate of the vehicle agent, and Θ n is the system disturbance compensation matrix of the vehicle agent, thus completing the dynamic coordination of multiple agents of the distributed electric automobile.
Referring to fig. 3, the present example designs a multi-Agent coordination control method for a distributed electric vehicle based on a cooperative game, and in the process of dynamic coordination control of multiple agents, agent language is first adopted to intelligently divide each functional area of the distributed electric vehicle, so that interactivity and control accuracy of coordination control of the multiple agents can be ensured based on data acquired in real time. The control input of each intelligent agent is intelligently coordinated through the cooperative game control algorithm, and disturbance and uncertainty existing in the control process of the distributed electric automobile are eliminated based on a robust compensation control strategy. The design effectively avoids the problem that the distributed electric automobile is difficult to coordinate and control under the condition that the interaction of the vehicle agents is imperfect and the agents are administrative.
The advantages of this example are:
The distributed electric vehicle dynamic coordination control method adopted by the method can effectively identify the intention of each intelligent body and has perfect information interaction, has very important significance for intelligent, safe and efficient driving of the distributed electric vehicle, has the advantages of intelligent interaction control coupling and convenient real-time control of the intelligent bodies based on a coordination control algorithm of the cooperative game, eliminates the influence of system disturbance and interference on control, and is safer and more reliable than the traditional method based on coordination control output after the cooperative game, suitable for various driving working conditions and capable of meeting the intelligent coordination control requirement of the distributed electric vehicle.
The foregoing description is only illustrative of the preferred embodiments of the present invention and is not intended to limit the scope of the claims, but rather the equivalent structural changes made using the description and drawings are intended to be included within the scope of the present invention.
Claims (4)
1. A distributed electric automobile multi-agent cooperative control method based on cooperative game is characterized by comprising the following steps:
Step S1: aiming at the functions of each subsystem of the distributed electric automobile, an intelligent control area of a steering system is established, and the intelligent control area is divided into a driver intelligent body and a steering auxiliary control intelligent body; in the intelligent control area of the driving/braking system, the distributed electric automobile controls the driving/braking of the automobile for an in-wheel motor, and four wheels are independently controllable, so that a left front wheel intelligent body, a left rear wheel intelligent body, a right front wheel intelligent body and a right rear wheel intelligent body are established;
Step S2: aiming at the driver intelligent agent, the steering auxiliary control intelligent agent, the left front wheel intelligent agent, the left rear wheel intelligent agent, the right front wheel intelligent agent and the right rear wheel intelligent agent which are defined in the step S1, 6 intelligent agents are combined, and a dynamics model for representing each intelligent agent is built, so that a vehicle system dynamics model is built;
step S3: carrying out linearization treatment on a vehicle system by adopting a T-S fuzzy theory, and establishing a system state equation after linearization treatment;
Step S4: establishing a distributed electric vehicle multi-agent coordination control strategy based on cooperative game;
Step S5: aiming at the problem that the disturbance of the system cannot be eliminated in the cooperative game, a robust compensation control strategy is established, and the efficient and safe dynamic coordination control of 6 intelligent bodies of the distributed electric automobile is realized;
Step S2 builds a kinetic model characterizing each agent for the 6 agents defined in step S1, thereby building a vehicle system kinetic model, and comprises the following steps:
Step S21: establishing a dynamics equation for representing the intelligent agent of the driver:
Kinetic expression of the hand torque input of the driver agent:
Wherein τ h is the steering torque input of the driver agent, J h is the equivalent inertia of the steering system, B h is the steering system damping, τ f is the steering system friction torque, τ e is the steering assist torque provided by the steering system motor, δ is the steering wheel angle, sign () is the sign function for indicating the sign of the parameter;
Step S22: establishing a dynamics equation of the auxiliary steering agent:
The kinetic equation expression of the auxiliary steering agent is:
τ e＝IeKt η equation 2
Wherein τ e is the steering torque output of the auxiliary steering agent, I e is the actual current of the steering motor, K t is the torque coefficient of the steering motor, and η is the mechanical efficiency;
step S23: dynamic model of hub motor intelligent body is established
Because the four hub motors of the distributed electric automobile have the same performance, the dynamic model of the hub motor intelligent body can be expressed as follows;
τ ij＝τLij+Bij+Jijωij equation 3
Wherein ij= { ff, fr, rf, rr }, respectively represent left front, left back, right front, right back, ω is the hub motor angular velocity, τ, τ L are respectively represented as the hub motor output torque and the load torque, J is the hub motor moment of inertia, and B is the damping coefficient;
Step S24: establishing a distributed electric automobile system model covering a driver agent
Setting the tire slip angle of the vehicle to be small, the vehicle dynamics model includes longitudinal movement, transverse movement and yaw movement, and the distributed electric vehicle system dynamics model can be expressed as:
Wherein, Delta is the steering wheel angle of the steering wheel,Is the steering wheel turning speed, beta is the vehicle mass center slip angle,The yaw rate of the vehicle, Y is the lateral displacement of the vehicle, omega r is the yaw angle of the vehicle, v x is the longitudinal speed of the vehicle, v y is the lateral speed of the vehicle, A is the vehicle state system matrix, B is the control input system matrix, B h is the driver control input system matrix, u= [ tau e τff τfr τrf τrr],τff ] is the left front wheel driving moment of the vehicle, tau fr is the left rear wheel driving moment, tau rf is the right front wheel driving moment, tau rr is the right rear wheel driving moment, H is the system disturbance matrix, w is the system disturbance, z is the system output, and C is the system output matrix.
2. The cooperative game-based distributed electric vehicle multi-agent cooperative control method according to claim 1 is characterized in that the step S3 adopts a T-S fuzzy theory to carry out linearization treatment on a vehicle system, and establishes a linearized system state equation, and the method comprises the following steps:
step S31: setting the upper boundary of the longitudinal speed of the distributed electric automobile as Lower boundary is
Step S32: establishing a human-vehicle system model based on a T-S fuzzy system:
Comprehensively considering the nonlinear problem caused by the longitudinal speed to the human-vehicle system, and establishing a linearized human-vehicle system model based on the T-S fuzzy theory as follows:
Wherein k is the current moment of the system, v is an advance variable, ζ i (v) is a normalized membership function of a T-S fuzzy model, A i_b is a system state matrix after T-S fuzzy processing, B h_h is a driver control matrix after discretization in the system, B d is a vehicle control matrix after discretization in the system, and H d is an interference matrix after discretization in the system.
3. The distributed electric vehicle multi-agent coordination control method based on the cooperative game according to claim 1, wherein step S4 establishes a distributed electric vehicle multi-agent coordination control strategy based on the cooperative game, and the method comprises the following steps:
step S41: establishing a cost function of each agent:
Aiming at the problem that the cooperative game cannot well process system interference and uncertainty, firstly, the system disturbance problem described in the step S5 is ignored, and a man-vehicle dynamics system based on the cooperative game is established as follows:
Wherein Δx is the state error amount of the system, Δτ h is the manipulation error amount of the driver, Δu is the execution error amount of the vehicle, and Δz is the error amount of the system output;
in game theory 6 agents are participants whose utility can take the form of a cost function, thus defining an infinite field of view as:
wherein n= { 23 45 6} represents an AFS steering auxiliary motor, a left front wheel hub motor, a left rear wheel hub motor, a right front wheel hub motor, a right rear wheel hub motor, Φ n represents a weight matrix of the agent n, r n represents a weight output by the agent n, J n represents a cost function of the agent n, J h represents a cost function of the driver agent, Φ h represents a weight matrix of the driver agent, and r h represents a weight output by the driver agent;
according to the interactive paradigm of cooperative gaming, all agents have a common goal, which can be described by a global cost function, which can be expressed as:
Wherein ρ h is the task weight of the driver agent in the whole game, ρ n is the task weight of the vehicle execution layer agent in the whole game,
Thus, the global objective cost function for 6 agents is:
Wherein R h,Rn and phi hk,Φnk are respectively represented as a weight matrix of system control input weights and state errors;
step S42: optimization problem of distributed electric automobile agent:
The optimization problem for 6 agents can be expressed as:
Wherein, P i_h and P i_n represent lyapunov solution feedback control rate, respectively;
step S43: dynamic coordination control rate solving method for distributed electric automobile based on cooperative game
The cooperative game is implemented by simultaneously solving a plurality of coupling optimization problems to obtain a global optimal solution, and according to the cost functions of the 6 agents established in the step S41 and the expression forms of the optimization problems of the 6 agents established in the step S42, the expression forms of the model predictive control algorithm can be established and calculated:
Wherein, Phi h,Φn is represented as a weight matrix of system state errors;
The optimal solution of the system is solved through the QR algorithm, and the optimal solution of 6 intelligent agents can be obtained as follows:
Wherein, ψ h and ψ n represent the optimal solution matrices of the driver control input, the auxiliary steering torque control input, the left front wheel control input, the left rear wheel control input, the right front wheel control input, the right rear wheel control input, respectively.
4. The distributed electric vehicle multi-agent cooperative control method based on the cooperative game according to claim 1, wherein step S5 establishes a robust compensation control strategy for the problem that the cooperative game cannot eliminate system disturbance, and realizes efficient and safe dynamic cooperative control of 6 agents of the distributed electric vehicle, and the method comprises the following steps:
The problem of system disturbance and uncertainty can not be effectively eliminated by the cooperative game theory, and in order to ensure the stability of dynamic coordination control of the distributed electric automobile, the control rate of the multi-agent obtained by solving according to claim 3 further establishes a robust H ∞ compensation strategy for the control output of the auxiliary steering agent, the left front wheel agent, the left rear wheel agent, the right front wheel agent and the right rear wheel agent, and can obtain:
Where ε is the stability parameter of the closed loop system given H ∞ performance, z (k) and w (k) are affected by the system;
therefore, the optimal control rate of the system can be rewritten as:
Wherein, deltaτ rh is the control moment of the driver for eliminating the system disturbance, deltau rn is the control moment of the agent for eliminating the system disturbance, deltau cn is the system disturbance compensation control rate of the agent, and Θ n is the system disturbance compensation matrix of the agent.
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