CN116215585B - Intelligent network-connected bus path tracking game control method and device - Google Patents

Abstract

The present application discloses a game control method and device for path tracking of an intelligent networked bus, wherein the method includes: constructing a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent networked bus; constructing a road model according to road information, and Combining the two-degree-of-freedom vehicle model and the road model of the vehicle system dynamics to construct the vehicle-road model; based on the quadratic optimal theory, the cost function of path tracking control in the intelligent driving domain and stability control in the chassis domain is constructed based on the vehicle-road model ;Based on the cost function, combine the intelligent driving domain path tracking control and the chassis domain stability control with the Steinkelberg closed-loop game, take the intelligent driving domain as the leader of the game, and take the chassis domain as the follower of the game, solve the optimal optimal control strategy. As a result, the problems in the related art such as understeering or oversteering caused by single-wheel braking, resulting in reduced control accuracy of path tracking, and decreased safety and stability of the vehicle are solved.

Description

Game control method and device for path tracking of intelligent connected bus

technical field

The present application relates to the technical field of intelligent driving, and in particular to a method and device for path tracking game control of an intelligent networked bus.

Background technique

In related technologies, the intelligent driving domain plans the vehicle trajectory in real time, and performs path tracking control on the planned trajectory. The chassis domain covers transmission, driving, steering and braking systems. For example, the smart car chassis can include controlling the horizontal and vertical Sporty drive-by-wire, brake-by-wire and steering-by-wire, as well as suspension-by-wire. When the intelligent networked passenger vehicle encounters unexpected instability during high-speed operation, the chassis stability control system will intervene instantly. Single wheel braking returns the vehicle to a stable state.

However, in the related art, due to understeer or oversteer caused by single-wheel braking, the control accuracy of path tracking is reduced, and there is a large gap between the vehicle state and the control target, which reduces the safety and stability of the vehicle and cannot satisfy users. driving needs need to be resolved urgently.

Contents of the invention

This application provides a path tracking game control method and device for an intelligent networked bus to solve the problem of understeering or oversteering caused by single-wheel braking in the related art, which leads to a decrease in the control accuracy of path tracking, and the discrepancy between the vehicle state and the control target. The large gap reduces the safety and stability of the vehicle and cannot meet the driving needs of users.

The embodiment of the first aspect of the present application provides a path tracking game control method for an intelligent networked bus, including the following steps: constructing a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent networked bus; constructing a road model according to road information, And combine the vehicle system dynamics two-degree-of-freedom vehicle model and the road model to construct a vehicle-road model; based on the quadratic optimal theory, build a path-following control and chassis in the intelligent driving domain based on the vehicle-road model The cost function of domain stability control; based on the cost function, the path tracking control of the intelligent driving domain and the stability control of the chassis domain are combined with the Steinkelberg closed-loop game, the intelligent driving domain is taken as the leader of the game, and the chassis The domain acts as the follower of the game to solve the optimal control strategy.

Optionally, in one embodiment of the present application, the construction of the vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent connected bus includes: establishing a two-degree-of-freedom model state with the front wheels of the vehicle as input objects Equation; the state equation of the two-degree-of-freedom model is discretized to obtain a discrete vehicle dynamics equation.

Optionally, in an embodiment of the present application, the constructing a road model based on road information, and combining the vehicle system dynamics two-degree-of-freedom vehicle model and the road model to construct a vehicle-road model includes: The preview path information of the above road information is added to the discrete vehicle dynamics equation, so as to augment the steering and brake sharing vehicle dynamics system through the preview dynamic process, and obtain the multi-objective path tracking augmentation of the intelligent networked bus. wide system.

Optionally, in one embodiment of the present application, the multi-target route tracking augmentation system for intelligent networked buses is:

,

in, is the state coefficient matrix, /> Symbols for parameters concerning the front wheel angle, /> for the current page /> moment, /> for the current page /> moment, /> is the subscript symbol of the relevant parameters of the augmented state equation, /> is the vehicle-road state variable, /> for control input /> matrix coefficients, /> for control input /> matrix coefficients.

Optionally, in one embodiment of the present application, the construction of the cost function of path tracking control in the intelligent driving domain and stability control in the chassis domain based on the quadratic optimal theory includes: selecting the The lateral position deviation and heading angle deviation are used as the weighting items of the steering system, and the ideal yaw rate of the vehicle is used as the weighting items of the braking control to generate the cost function of the multi-objective path following control problem.

Optionally, in one embodiment of the present application, based on the cost function, the intelligent driving domain path tracking control and the chassis domain stability control are combined with the Steinkelberg closed-loop game, and the intelligent driving domain is used as a game , and take the chassis domain as the follower of the game to solve the optimal control strategy, including: in the closed-loop Steinkelberg game control, the leader and the follower satisfy the preset recurrence relationship, based on The Steinkelberg feedback non-cooperative game theory is used to derive the game control strategy of the intelligent driving domain and the chassis domain, and obtain the unique feedback Steinkelberg equilibrium solution.

The embodiment of the second aspect of the present application provides a path tracking game control device for an intelligent networked bus, including: a first construction module for constructing a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent networked bus; A construction module is used to construct a road model according to road information, and to construct a vehicle-road model in combination with the vehicle system dynamics two-degree-of-freedom vehicle model and the road model; a construction module is used to base on the quadratic optimal theory , based on the vehicle-road model, constructing the cost function of path tracking control in the smart driving domain and stability control in the chassis domain; the calculation module is used to combine the path tracking control in the smart driving domain and the stability control in the chassis domain with the cost function based on the vehicle-road model. Combining the Steinkelberg closed-loop game, the intelligent driving domain is used as the leader of the game, and the chassis domain is used as the follower of the game to solve the optimal control strategy.

Optionally, in one embodiment of the present application, the first construction module includes: an establishment unit, configured to establish a two-degree-of-freedom model state equation with the front wheels of the vehicle as an input object; a calculation unit, configured to convert the The state equation of the two-degree-of-freedom model is discretized to obtain a discrete vehicle dynamics equation.

Optionally, in an embodiment of the present application, the second construction module includes: a processing unit, configured to add the preview path information of the road information into the discrete vehicle dynamics equation, so as to pass The steering and braking shared vehicle dynamics system is augmented by previewing the dynamic process, and a multi-objective path tracking augmentation system for intelligent networked buses is obtained.

Optionally, in one embodiment of the present application, the multi-target route tracking augmentation system for intelligent networked buses is:

,

in, is the state coefficient matrix, /> Symbols for parameters concerning the front wheel angle, /> for the current page /> moment, /> for the current page /> moment, /> is the subscript symbol of the relevant parameters of the augmented state equation, /> is the vehicle-road state variable, /> for control input /> matrix coefficients, /> for control input /> matrix coefficients.

Optionally, in an embodiment of the present application, the construction module includes: a construction unit, which is used to select the lateral position deviation and the heading angle deviation at the preview point as the weighting items of the steering system, and take the ideal lateral position of the car The pendulum angular velocity is used as the weighting term of the braking control to generate the cost function of the multi-objective path following control problem.

Optionally, in an embodiment of the present application, the calculation module includes: a derivation unit, configured to satisfy a preset recurrence relationship between the leader and the follower in a closed-loop Steinberg game control, Based on the Steinkelberg feedback non-cooperative game theory, the game control strategy of the smart driving domain and the chassis domain is derived, and a unique feedback Steinkelberg equilibrium solution is obtained.

The embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the program to realize As described in the above-mentioned embodiments, the route tracking game control method of an intelligent networked bus.

The embodiment of the fourth aspect of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the above intelligent networked bus path tracking game control method is implemented.

In the embodiment of the present application, the vehicle system dynamics two-degree-of-freedom vehicle model can be constructed according to the actual parameters of the intelligent connected bus, the road model can be constructed according to the road information, and the vehicle-road model can be constructed by combining the vehicle system dynamics two-degree-of-freedom vehicle model and the road model , based on the quadratic optimal theory, and based on the vehicle-road model, the cost functions of path tracking control in the intelligent driving domain and stability control in the chassis domain are constructed, so that the path tracking control in the intelligent driving domain and the stability control in the chassis domain are combined with the tank Combining the Berger closed-loop game, the smart driving domain is used as the leader of the game, and the chassis domain is used as the follower of the game to solve the optimal control strategy, thereby effectively improving the control accuracy of path tracking and improving the safety of the vehicle Sex and stability, effectively meet the driving needs of users. This solves the problem in the related art that understeering or oversteering due to single-wheel braking leads to reduced control accuracy of path tracking, lowers safety and stability of the vehicle, and fails to meet the driving needs of users.

Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.

Description of drawings

The above and/or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the accompanying drawings, wherein:

Fig. 1 is a flow chart of a game control method for path tracking of an intelligent networked bus provided according to an embodiment of the present application;

Fig. 2 is a schematic diagram of a vehicle system dynamics two-degree-of-freedom model of a specific embodiment of the present application;

Fig. 3 is the schematic diagram of the Steinkelberg game control of a specific embodiment of the present application;

FIG. 4 is a schematic diagram of preview theoretical design of a specific embodiment of the present application;

Fig. 5 is a schematic diagram of the Steinkelberg game principle of a specific embodiment of the present application;

FIG. 6 is a schematic diagram of comparing parameters of different path tracking control methods in a specific embodiment of the present application;

FIG. 7 is a schematic structural diagram of an intelligent networked bus route tracking game control device provided according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of an electronic device provided according to an embodiment of the present application.

Detailed ways

Embodiments of the present application are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary, and are intended to explain the present application, and should not be construed as limiting the present application.

The following describes the route tracking game control method and device for an intelligent networked bus according to the embodiments of the present application with reference to the accompanying drawings. In the related technologies mentioned in the background technology center mentioned above, understeering or oversteering caused by single-wheel braking reduces the control accuracy of path tracking, reduces the safety and stability of the vehicle, and fails to meet the driving needs of users. Problem, this application provides a path tracking game control method for intelligent connected buses. In this method, a vehicle system dynamics two-degree-of-freedom vehicle model can be constructed according to the actual parameters of the intelligent connected passenger vehicle, and a road model can be constructed according to road information. Combined with the vehicle system dynamics two-degree-of-freedom vehicle model and the road model to construct the vehicle-road model, based on the quadratic optimal theory, the cost of intelligent driving domain path tracking control and chassis domain stability control is constructed based on the vehicle-road model function, so that the intelligent driving domain path tracking control and the chassis domain stability control are combined with the Steinkelberg closed-loop game, and the intelligent driving domain is taken as the leader of the game, and the chassis domain is used as the follower of the game to solve the optimal control strategy, thereby effectively improving the control accuracy of path tracking, and improving the safety and stability of the vehicle, effectively meeting the driving needs of users. This solves the problem in the related art that understeering or oversteering due to single-wheel braking leads to reduced control accuracy of path tracking, lowers safety and stability of the vehicle, and fails to meet the driving needs of users.

Specifically, FIG. 1 is a schematic flow chart of a game control method for path tracking of an intelligent networked bus provided in an embodiment of the present application.

As shown in Figure 1, the intelligent connected bus path tracking game control method includes the following steps:

In step S101, a vehicle system dynamics two-degree-of-freedom vehicle model is constructed according to the actual parameters of the intelligent connected bus.

It can be understood that the embodiment of the present application can construct a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent networked bus in the following steps, thereby effectively improving the executable path tracking game control of the intelligent networked bus sex.

Among them, in one embodiment of the present application, constructing a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent connected bus includes: establishing a two-degree-of-freedom model state equation with the front wheels of the vehicle as input objects; The state equation of the degree of freedom model is discretized to obtain a discrete vehicle dynamics equation.

In the actual implementation process, it is assumed that the tire lateral force of the vehicle is a linear function of the tire slip angle, the speed in the x-axis direction is constant, and the influence of the suspension characteristics is ignored. By default, the vehicle only moves parallel to the ground without load transfer, and Ignoring the influence of the steering system, the front wheel steering angle is directly used as input.

Further, as shown in Figure 2, the embodiment of the present application can establish a two-degree-of-freedom model state equation with the front wheels of the vehicle as the input object, namely:

,

in, is the state variable matrix of the two-degree-of-freedom vehicle model, /> is the variable matrix of the front wheel angle coefficient of the two-degree-of-freedom vehicle model, /> is the direct yaw moment coefficient variable matrix of the two-degree-of-freedom vehicle model, /> is a diacritic, /> for time, /> is a continuous system state variable, /> Angle for the front wheels.

in, are continuous system state variables, which are lateral velocity, yaw rate, lateral displacement, and yaw angle, respectively.

Then, the state equation coefficient matrix is as follows:

,

Furthermore, in the embodiment of the present application, the c2d command of MATLAB can be used to discretize the state equation of the two-degree-of-freedom model in the above steps to obtain a discrete vehicle system, namely:

in, is the discrete state of the current time step, /> is the discrete state of the next time step, /> , , /> respectively by the corresponding continuous-time matrix /> , /> , /> The discrete bilinear transformation of is obtained.

in,

,

in, is the time step, /> for time

In step S102, a road model is constructed according to the road information, and a vehicle-road model is constructed by combining the vehicle system dynamics two-degree-of-freedom vehicle model and the road model.

It can be understood that the embodiment of the present application can construct a road model based on the road information in the following steps, and combine the vehicle system dynamics two-degree-of-freedom vehicle model and the road model to construct a vehicle-road model, so that the vehicle is in path tracking and lateral stability. The control distribution is more reasonable, which effectively improves the stability of the vehicle.

Among them, in one embodiment of the present application, the road model is constructed according to the road information, and the vehicle-road model is constructed in combination with the vehicle system dynamics two-degree-of-freedom vehicle model and the road model, including: adding the preview path information of the road information to In the discrete vehicle dynamics equation, the steering and brake sharing vehicle dynamics system is augmented by previewing the dynamic process to obtain a multi-objective path tracking augmentation system for intelligent networked buses.

For example, as shown in Figure 3 and Figure 4, Figure 4 is a road preview model, which discretizes the preview distance into a fixed points to provide road information for the next step of control.

Next, in this embodiment of the present application, the preview path information of the road information can be added to the discrete vehicle dynamics equation, where the vehicle's preview lateral displacement/> can be generated by a shift register, i.e.:

in, for /> , /> control input token, /> Define symbols for overall control objectives, /> for No. /> step road information matrix, /> is the shift register matrix, /> is the road information matrix to be updated at the current moment.

in,

,

in, For the control target matrix, /> for path markers, /> is the heading mark, /> is the lateral displacement, is the heading angle, and /> .

in,

in, Control the target value for the farthest point, /> for No. /> moment, /> is the preview value, /> to update the matrix, /> is a shift register.

Next, the embodiment of the present application can augment the dynamics system of the steering and brake sharing type vehicle through the preview dynamic process, and obtain the multi-objective path tracking augmentation system of the intelligent networked bus, that is:

,

in, is the farthest preview point in the preview area of the intelligent driving domain path tracking system and the chassis domain stability control system, /> is the vehicle-road state variable, /> Update the matrix for the control objective.

in:

,

, />

in, is the vehicle parameter state variable, /> Enter the weights for the control of the front wheel angle, /> Enter the weights for the control of the direct yaw moment, /> is the control target of the smart driving domain , /> is the control target of the chassis domain.

Since the preview information of the two agents in the intelligent driving domain path tracking system and the chassis domain stability control system are in the augmented state Therefore, the farthest preview point information can be omitted /> .

Wherein, in one embodiment of the present application, the multi-target path tracking augmentation system for intelligent networked buses can be further simplified as:

in, is the vehicle-road state variable, /> .

In addition, the multi-target path tracking augmentation system for intelligent networked bus for No. /> The system state variable at time, that is, the first /> The state variables of vehicles at any time, road preview and stability target preview information, in the mathematical formula, in the multi-target path tracking augmentation system of intelligent networked buses /> represents a matrix, namely:

,

in, for control input /> matrix coefficients, /> for control input /> matrix coefficients.

In step S103, based on the quadratic optimal theory and the vehicle-road model, the cost functions of path tracking control in the intelligent driving domain and stability control in the chassis domain are constructed.

It can be understood that the embodiments of the present application can be based on the quadratic optimal theory in the following steps, and construct the cost functions of path tracking control in the intelligent driving domain and stability control in the chassis domain based on the vehicle-road model, thereby effectively improving It improves the stability and safety of the vehicle, and improves the user's driving experience.

Among them, in one embodiment of the present application, based on the quadratic optimal theory, the cost function of path tracking control in the intelligent driving domain and stability control in the chassis domain is constructed, including: selecting the lateral position deviation at the preview point and The heading angle deviation is used as the weighting item of the steering system, and the ideal yaw rate of the vehicle is used as the weighting item of the braking control to generate the cost function of the multi-objective path following control problem.

In some embodiments, as shown in Figure 3, in the embodiment of the present application, the lateral position deviation and the heading angle deviation at the preview point can be selected as the weighting items of the steering system, and the ideal yaw rate of the car can be used as the weighting of the braking control , and design the cost function of the multi-objective path tracking control problem with Nu step in the prediction and control time domain as _:

,

in, Accumulate values for iterations of moments, /> Refers to symbols for state variables, /> Define the sign for the front wheel angle cost function, /> Define the sign for the cost function of the direct yaw moment, /> and /> are the self-input weighting coefficients of the steering and braking systems, respectively, /> , /> are the tracking error weighting matrices of steering and braking systems respectively, /> , /> respectively The weighting matrix of the steering and braking system performance index functions at all times, and /> , /> .

in,

,

,

in, Construct the matrix for the deviation of the front wheel angle, /> Construct the matrix for the deviation of the direct yaw moment, /> Construct the transpose of the matrix for the deviation of the front wheel angle, /> Construct the transpose of the matrix for the deviation of the direct yaw moment, /> Tracking target for front wheel angle control, /> track target for direct yaw moment control, /> and /> are the state weighting matrices of the steering and braking systems, respectively, /> and /> are the self-input weighting coefficients of the steering and braking systems, respectively.

In step S104, based on the cost function, the intelligent driving domain path tracking control and the chassis domain stability control are combined with the Steinkelberg closed-loop game, the intelligent driving domain is taken as the leader of the game, and the chassis domain is used as the follower of the game to find the optimal control strategy.

It can be understood that, based on the cost function, the embodiment of the present application can combine the intelligent driving domain path tracking control and the chassis domain stability control with the Steinkelberg closed-loop game in the following steps, and use the intelligent driving domain as the leader of the game , and the chassis domain is used as the follower of the game to solve the optimal control strategy, so that the intelligent connected bus is more stable and reliable while tracking the path.

Optionally, in one embodiment of the present application, based on the cost function, the intelligent driving domain path tracking control and the chassis domain stability control are combined with the Steinkelberg closed-loop game, and the intelligent driving domain is used as the leader of the game, And take the chassis domain as the follower of the game to solve the optimal control strategy, including: in the closed-loop Steinkelberg game control, the leader and the follower satisfy the preset recursive relationship, and based on the Steinkelberg feedback non-cooperative game Based on the theory, the game control strategy between the intelligent driving domain and the chassis domain is derived, and the unique feedback Steinkelberg equilibrium solution is obtained.

In some embodiments, for the convenience of description, the embodiment of the present application can ignore white noise and road reference information, and combine the multi-objective path tracking augmentation system of intelligent networked buses and the cost function of the multi-objective path tracking control problem to have the following definition, And define the control set of active steering and braking system as and /> .

further:

,

in, , /> and /> are the generalized definitions of the state equation, front wheel angle and direct yaw moment, respectively.

As shown in Figure 3, in the closed-loop Steinberg game control, the leader and the follower must satisfy the following recursive relationship, namely:

,

in, and /> are respectively the definition of the state equation when the state is the optimal state and the definition of the cost function when the direct yaw moment obtains the optimal value, /> is the value function of direct yaw moment, /> is the iterative control rate of direct yaw moment.

Then, there will be a series of optimal Steinberg game control strategies .

in,

in, is the weight matrix of the direct yaw moment.

However, the optimal solution of intelligent driving domain control is the recursive solution set obtained on the basis of considering the chassis domain control decision, namely:

in, is the optimal solution of the front wheel angle, /> is the value function of the front wheel angle, /> For the definition of the state equation, is the definition of the cost function of the front wheel angle, /> Definition of the cost function for obtaining the optimum value for the front wheel angle.

in:

,

Similarly, the optimal solution of the chassis domain control is the recursive solution set obtained on the basis of considering the intelligent driving domain control decision, namely:

in:

in, , /> and /> are the optimal direct yaw moment, the state equation under the optimal control input and the cost function of the direct yaw moment under the optimal front wheel angle, respectively, /> is the optimal front wheel angle, /> Transpose for the optimal front wheel angle.

It can be seen that the embodiment of the present application can derive the game control strategy between the intelligent driving domain and the chassis domain based on the Steinkelberg feedback non-cooperative game theory. For the special case of a linear quadratic game with a strict convex cost function, a unique The feedback Steinkelberg equilibrium solution of , and the equilibrium solution is linear in the current value of the state, the form of the solution is as follows:

in, is the control rate, /> is the feedback Steinkelberg equilibrium solution of the front wheel angle, is the feedback Steinkelberg equilibrium solution for the direct yaw moment.

while control rate The following relations are satisfied, namely:

,

in, is the front wheel rotation angle/direct yaw moment The solution of the Riccati equation at time, /> is the control rate of the feedback Steinkelberg equilibrium solution of the front wheel angle, /> is the control rate of the feedback Steinkelberg equilibrium solution for the direct yaw moment, /> is the front wheel rotation angle/direct yaw moment The solution of the Riccati equation at time, /> is the control rate of the moment feedback Steinkelberg equilibrium solution of front wheel angle/direct yaw force.

For example, as shown in Figure 5, in the dynamic game evolution process, the intelligent networked bus is based on the lateral stability working conditions and road information, and based on the strategy interaction between the intelligent driving domain and the chassis domain, so that the intelligent networked bus Path tracking is more stable and reliable.

For example, as shown in Figure 6, it is the state parameter curve of the vehicle during the experiment, from (a) to (g) are the lateral displacement, yaw angle, front wheel rotation angle, center of mass sideslip angle, additional yaw moment , Game control wheel cylinder pressure and distributed control wheel cylinder pressure, among which, the first four items are for comparison of three experimental schemes, and the last three items are the data of input distributed control and game control for stability control.

Then, it can be seen from Figure (a) that the game control path tracking effect is the best, and the error is the smallest. Although the LQR path tracking control scheme without stability control has a good tracking effect before the 7th second, it is obviously unstable after 7 seconds , seriously offset the DLC road, and although the distributed control has a small error in the 7th-9th second, the overall curve tracking error is relatively large, and the offset position is obviously lagging behind, and the DLC road cannot be completed well.

Secondly, Figure (b) is the yaw angle comparison curve. Generally speaking, the game control tracking is the best, followed by the distributed one. The LQR active steering control without stability control has a good tracking effect before 5 seconds, and seriously deviates from it after 5 seconds. The target yaw angle has obviously become unstable. Therefore, excessive pursuit of control effects under low adhesion conditions will easily lead to the instability of the overall tracking control.

Finally, it can be seen from Figure (c) that the game control has the smallest front wheel angle. Although the distributed control path tracking offset in Figure (a) is small and the error is large, but the front wheel angle in Figure (a) is larger, mainly The reason is that the stability control seriously inhibits the steering control, which makes the conflict of interests between steering and braking more obvious. It can be seen from the figure (g) that the maximum wheel cylinder pressure in the figure (g) can reach 0.8MPa. For the wheel cylinder pressure in (f), the maximum wheel cylinder pressure value in figure (g) is relatively large, and the yaw moment comparison curve in figure (e) has a large distribution range, especially at the 6th second, reaching - 2500N*m, which is 500N*m larger than the value of the game control. Overall, the yaw moment curve of the game control is more coordinated, and, as can be seen from Figure (d), the game control is more stable when the sideslip angle of the center of mass changes.

To sum up, in the embodiment of this application, the smart driving domain and the chassis domain can be defined as two participants in the game, and the dynamic game theory is used to derive the interactive control strategy between the smart driving domain and the chassis domain of the intelligent connected bus. When the driving domain as the leader decides the control decision, the chassis domain can observe the control decision, so that the chassis domain can decide the response according to the control decision of the intelligent driving domain system. Among them, the particularity of the Stackelberg game is that the leader When planning decisions, the follower's dynamic strategy can be fully understood, and the leader can understand the follower's cost function or performance index function.

Therefore, the leader can expect the impact of the decision on the follower, and the leader's decision will be constrained by the follower's cost function or performance index function, and maximize the benefits for itself, thus obtaining the intelligent driving domain and the chassis domain The global optimal control solution of the two systems makes the distribution of vehicle path tracking and lateral stability control more reasonable, thereby improving the safety and stability of intelligent networked buses.

According to the intelligent networked bus path tracking game control method proposed in the embodiment of this application, the vehicle system dynamics two-degree-of-freedom vehicle model can be constructed according to the actual parameters of the intelligent networked bus, and the road model can be constructed according to the road information, combined with the vehicle system dynamics A two-degree-of-freedom vehicle model and a road model are used to construct a vehicle-road model. Based on the quadratic optimal theory, the cost functions of intelligent driving domain path tracking control and chassis domain stability control are constructed based on the vehicle-road model. Combining domain path tracking control and chassis domain stability control with the Steinkelberg closed-loop game, the smart driving domain is used as the leader of the game, and the chassis domain is used as the follower of the game to solve the optimal control strategy and effectively improve It improves the control accuracy of path tracking, improves the safety and stability of the vehicle, and effectively meets the driving needs of users. This solves the problem in the related art that understeering or oversteering due to single-wheel braking leads to reduced control accuracy of path tracking, lowers safety and stability of the vehicle, and fails to meet the driving needs of users.

Next, the route tracking game control device for intelligent networked bus proposed according to the embodiment of the present application will be described with reference to the accompanying drawings.

Fig. 7 is a schematic block diagram of an intelligent networked bus route tracking game control device according to an embodiment of the present application.

As shown in FIG. 7 , the intelligent network-linked bus route tracking game control device 10 includes: a first construction module 100 , a second construction module 200 , a construction module 300 and a calculation module 400 .

Specifically, the first construction module 100 is used to construct a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent connected bus.

The second construction module 200 is configured to construct a road model according to road information, and construct a vehicle-road model in combination with a two-degree-of-freedom vehicle model of vehicle system dynamics and a road model.

The construction module 300 is used for constructing the cost functions of path tracking control in the intelligent driving domain and stability control in the chassis domain based on the vehicle-road model based on the quadratic optimal theory.

The calculation module 400 is used to combine the intelligent driving domain path tracking control and the chassis domain stability control with the Steinkelberg closed-loop game based on the cost function, using the intelligent driving domain as the leader of the game and the chassis domain as the game leader. Follower, to solve the optimal control strategy.

Optionally, in an embodiment of the present application, the first construction module 100 includes: an establishment unit and a calculation unit.

Wherein, the establishment unit is used to establish the state equation of the two-degree-of-freedom model with the front wheel of the vehicle as the input object.

The calculation unit is used to discretize the state equation of the two-degree-of-freedom model to obtain a discrete vehicle dynamics equation.

Optionally, in an embodiment of the present application, the second construction module 200 includes: a processing unit.

Among them, the processing unit is used to add the preview path information of the road information into the discrete vehicle dynamics equation, so as to augment the steering and brake sharing vehicle dynamics system through the preview dynamic process, and obtain the intelligent network bus Multi-objective path-tracking augmentation system.

Optionally, in one embodiment of the present application, the multi-target path tracking augmentation system for intelligent networked buses is:

,

in, is the state quantity coefficient matrix, /> is the vehicle-road state variable, /> for control input /> matrix coefficients, /> for control input /> matrix coefficients.

Optionally, in an embodiment of the present application, the construction module includes: a construction unit.

Among them, the construction unit is used to select the lateral position deviation and heading angle deviation at the preview point as the weighting item of the steering system, and the ideal yaw rate of the car as the weighting item of the braking control to generate a multi-objective path tracking control problem cost function.

Optionally, in an embodiment of the present application, the calculation module includes: a derivation unit.

Among them, the derivation unit is used in the closed-loop Steinkelberg game control, the leader and the follower satisfy the preset recursive relationship, so as to derive the game between the intelligent driving domain and the chassis domain based on the Steinkelberg feedback non-cooperative game theory The control strategy obtains a unique feedback Steinkelberg equilibrium solution.

It should be noted that the foregoing explanations on the embodiment of the intelligent networked bus route tracking game control method are also applicable to the intelligent networked bus route tracking game control device of this embodiment, and will not be repeated here.

According to the intelligent networked bus path tracking game control device proposed in the embodiment of the present application, the vehicle system dynamics two-degree-of-freedom vehicle model can be constructed according to the actual parameters of the intelligent networked bus, and the road model can be constructed according to the road information, combined with the vehicle system dynamics A two-degree-of-freedom vehicle model and a road model are used to construct a vehicle-road model. Based on the quadratic optimal theory, the cost functions of intelligent driving domain path tracking control and chassis domain stability control are constructed based on the vehicle-road model. Combining domain path tracking control and chassis domain stability control with the Steinkelberg closed-loop game, the smart driving domain is used as the leader of the game, and the chassis domain is used as the follower of the game to solve the optimal control strategy and effectively improve It improves the control accuracy of path tracking, improves the safety and stability of the vehicle, and effectively meets the driving needs of users. This solves the problem in the related art that understeering or oversteering due to single-wheel braking leads to reduced control accuracy of path tracking, lowers safety and stability of the vehicle, and fails to meet the driving needs of users.

FIG. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. This electronic equipment can include:

A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

When the processor 802 executes the program, it realizes the game control method for the path tracking of the intelligent networked bus provided in the above-mentioned embodiments.

Further, the electronic equipment also includes:

The communication interface 803 is used for communication between the memory 801 and the processor 802 .

The memory 801 is used to store computer programs that can run on the processor 802 .

The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

If the memory 801, the processor 802, and the communication interface 803 are independently implemented, the communication interface 803, the memory 801, and the processor 802 may be connected to each other through a bus to complete mutual communication. The bus may be an Industry Standard Architecture (Industry Standard Architecture, ISA for short) bus, a Peripheral Component Interconnect (PCI for short) bus, or an Extended Industry Standard Architecture (EISA for short) bus. The bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used in FIG. 8 , but it does not mean that there is only one bus or one type of bus.

Optionally, in specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on one chip, then the memory 801, the processor 802, and the communication interface 803 can communicate with each other through the internal interface.

The processor 802 may be a central processing unit (Central Processing Unit, referred to as CPU), or a specific integrated circuit (Application Specific Integrated Circuit, referred to as ASIC), or configured to implement one or more of the embodiments of the present application integrated circuit.

This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above game control method for route tracking of an intelligent networked bus is realized.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or N embodiments or examples in an appropriate manner. In addition, those skilled in the art can combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present application, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N steps of executable instructions for implementing a custom logical function or process, Also, the scope of preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order depending on the functions involved, which should be considered Those skilled in the art to which the embodiments of the present application belong can understand.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium, For use with an instruction execution system, device, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from an instruction execution system, device, or device and execute instructions), or in conjunction with such an instruction execution system, device or equipment used. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connection with one or N wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the paper or other medium can be optically scanned and subsequently edited, interpreted, or in other suitable manner as necessary Processing is performed to obtain the program electronically and then to store it in a computer memory.

It should be understood that each part of the present application may be realized by hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented by any one or a combination of the following techniques known in the art: a discrete Logic circuits, ASICs with suitable combinational logic gates, Programmable Gate Arrays (PGAs), Field Programmable Gate Arrays (FPGAs), etc.

Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. During execution, one or a combination of the steps of the method embodiments is included.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the present application, and those skilled in the art can make the above-mentioned The embodiments are subject to changes, modifications, substitutions and variations.

Claims (10)

1. An intelligent network-linked passenger car path tracking game control method, is characterized in that, comprises the following steps: Construct a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent connected bus; Constructing a road model according to road information, and constructing a vehicle-road model in combination with the vehicle system dynamics two-degree-of-freedom vehicle model and the road model; Based on the quadratic optimal theory, the vehicle-road model is used to construct the cost function of path tracking control in the intelligent driving domain and the stability control in the chassis domain. The cost function of path following control and chassis domain stability control includes selecting the lateral position deviation and heading angle deviation at the preview point as the weighting items of the steering system, and taking the ideal yaw rate of the car as the weighting items of the braking control, generating a cost function for the multi-objective path-following control problem; and Based on the cost function, the intelligent driving domain path tracking control and the chassis domain stability control are combined with the Steinkelberg closed-loop game, and the intelligent driving domain is taken as the leader of the game, and the chassis domain is used as the follower of the game to solve The optimal control strategy, based on the cost function, combines the intelligent driving domain path tracking control and the chassis domain stability control with the Steinkelberg closed-loop game, taking the intelligent driving domain as the leader of the game, and the chassis domain As a follower of the game, solving the optimal control strategy includes in the closed-loop Steinkelberg game control, the leader and the follower satisfy a preset recursive relationship, so that based on the Steinkelberg feedback non-cooperative game theory, The game control strategy of the smart driving domain and the chassis domain is derived to obtain a unique feedback Steinkelberg equilibrium solution. 2. The intelligent connected bus path tracking game control method according to claim 1, wherein said constructing the vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent connected bus comprises: Establish the state equation of the two-degree-of-freedom model with the front wheel of the vehicle as the input object; The state equation of the two-degree-of-freedom model is discretized to obtain a discrete vehicle dynamics equation. 3. The intelligent network-connected passenger car path tracking game control method according to claim 2, wherein the road model is constructed according to road information, and combined with the vehicle system dynamics two-degree-of-freedom vehicle model and the road model Construct vehicle-road model, including: Adding the preview path information of the road information into the discrete vehicle dynamics equation to augment the steering and brake sharing vehicle dynamics system through the preview dynamic process to obtain the multi-objective path of the intelligent networked bus Track augmentation system. 4. The intelligent networked bus path tracking game control method according to claim 3, characterized in that, the intelligent networked bus multi-target path tracking augmentation system is: Among them, A _Γ is the state coefficient matrix, f is the parameter mark symbol about the front wheel angle, k is the current kth moment, k+1 is the current k+1th moment, Γ is the subscript symbol of the relevant parameters of the augmented state equation , Γ(k) is the vehicle-road state variable, For the matrix coefficients of the control input δ _f , /> is the matrix coefficient of the control input M, and δ _f and M are different control inputs. 5. An intelligent network-linked bus path tracking game control device, characterized in that it comprises: The first construction module is used to construct a vehicle system dynamics two-degree-of-freedom vehicle model according to the actual parameters of the intelligent connected bus; The second construction module is configured to construct a road model according to road information, and construct a vehicle-road model in combination with the vehicle system dynamics two-degree-of-freedom vehicle model and the road model; The construction module is used to construct the cost function of path tracking control in the intelligent driving domain and the stability control in the chassis domain based on the vehicle-road model based on the quadratic optimal theory, and the quadratic optimal theory is the basis , constructing the cost function of path tracking control in the intelligent driving domain and stability control in the chassis domain includes selecting the lateral position deviation and heading angle deviation at the preview point as the weighting items of the steering system, and taking the ideal yaw rate of the car as the braking control The weighting term of , generating a cost function for the multi-objective path-following control problem; and The calculation module is used to combine the intelligent driving domain path tracking control and the chassis domain stability control with the Steinkelberg closed-loop game based on the cost function, using the intelligent driving domain as the leader of the game, and using the chassis domain as the game The follower of is to solve the optimal control strategy. Based on the cost function, the intelligent driving domain path tracking control and the chassis domain stability control are combined with the Steinkelberg closed-loop game, and the intelligent driving domain is used as the leader of the game , and taking the chassis domain as the follower of the game, solving the optimal control strategy includes in the closed-loop Steinkelberg game control, the leader and the follower satisfy the preset recursive relationship, and based on the Steinkelberg feedback Non-cooperative game theory, deriving the game control strategy of the smart driving domain and the chassis domain, and obtaining a unique feedback Steinkelberg equilibrium solution. 6. The intelligent network-linked bus path tracking game control device according to claim 5, wherein the first construction module comprises: Establishing a unit for establishing a two-degree-of-freedom model state equation with the front wheel of the vehicle as an input object; The calculation unit is used to discretize the state equation of the two-degree-of-freedom model to obtain a discrete vehicle dynamics equation. 7. The intelligent network-linked bus route tracking game control device according to claim 6, wherein the second construction module comprises: The processing unit is used to add the previewed path information of the road information into the discrete vehicle dynamics equation, so as to augment the steering and brake sharing type vehicle dynamics system through the preview dynamic process, and obtain the intelligent network Multi-target path tracking augmentation system for connected passenger vehicles. 8. The intelligent networked bus path tracking game control device according to claim 7, characterized in that, the intelligent networked bus multi-target path tracking augmentation system is: Among them, A _Γ is the state coefficient matrix, f is the parameter mark symbol about the front wheel angle, k is the current kth moment, k+1 is the current k+1th moment, Γ is the subscript symbol of the relevant parameters of the augmented state equation , Γ(k) is the vehicle-road state variable, For the matrix coefficients of the control input δ _f , /> is the matrix coefficient of the control input M, and δ _f and M are different control inputs. 9. An electronic device, characterized in that it comprises: a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the program to realize the The route tracking game control method for intelligent networked buses described in any one of requirements 1-4. 10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor, so as to realize the path tracking of an intelligent networked bus according to any one of claims 1-4 Game control method.