| Intelligent vehicle technology can comprehensively improve the driving safety and comfort of automobiles,which is a research hotspot in the automobile industry.As one of the key technologies of intelligent vehicle,motion control is an important link between the upper module and the underlying actuator.Accurately tracking the desired path is an important manifestation of high-level intelligent driving.However,the intelligent vehicles are complex nonlinear systems and drive in a changing environment;at the same time,the design of path tracking controllers also faces disturbances such as system model simplification,parameter uncertainty,the delay of actuators and sensors signals,and road curvature changes.In addition,When driving on a curved lane with varying curvature,human drivers usually make a small range of longitudinal speed adjustments to maintain a yaw response that feels more stable.Taking this type of response as a reference,intelligent vehicles must also make adjustments to their longitudinal speed within a small range during the path tracking process with curvature changes.Therefore,in the face of path tracking control under complex disturbances,this paper proposes a pure pursue algorithm with dynamic preview capability,a model predictive control algorithm based on feedforward double compensation anti-disturbances mechanism,and a model predictive control algorithm with speed adaptation.The main research contents are as follows:(1)The principle of Ackermann steering and Frenet coordinate transformation is introduced,and a two-degree-of-freedom dynamics model is established to reflect the lateral and yaw motion characteristics of the vehicle considering the vehicle dynamics,which is used as the basis to construct a vehicle-road coupled dynamics tracking error model.Finally,the Car Sim-Matlab/Simulink joint environment platform and the intelligent driving real vehicle platform are introduced to lay the research foundation for the algorithm verification in this paper.(2)Aiming at the delay problem of vehicle actuation system response and sensor signals,a dynamic preview distance tracking control algorithm combining planning path and planning speed information is proposed to improve the influence of control delay and improve the accuracy of tracking.Specifically,firstly,based on the geometric pure pursue algorithm,the path tracking control law when moving forward and reversing is derived.Then,according to the real-time planned path and speed information,a dynamic adjustment method of preview distance is designed,and the relationship between the preview distance with speed adaptability and the steering angle was thereby obtained.Finally,the effectiveness and accuracy of the proposed algorithm were verified in Car Sim-Matlab/Simulink joint simulation,and a real vehicle test is performed,indicating that the proposed algorithm has lower computational consumption and higher tracking accuracy than the LQR algorithm based on dynamic model.(3)An anti-disturbances model predictive control(MPC)method is proposed for the problems of model simplification,parameter uncertainty,the delay of actuators and sensors signals,and road curvature changes.Firstly,a model prediction tracking system is established based on the single-track vehicle dynamics model,and a dynamic adjustment method of the preview distance based on real-time path planning and speed information is designed to obtain the best preview point to improve the delay disturbance of the actuators and sensor signals of the intelligent vehicle chassis.Then,an extended state observer(ESO)is introduced to estimate the unknown disturbance to the system due to the simplified vehicle model in real time and use it for feed-forward compensation.At the same time,considering the steady-state disturbance error caused by the change of the road reference curvature to the system,a feed-forward control(FFC)method with curvature constraints is designed to eliminate this disturbance;and finally the steering angle control law of the superposition of the feedback input of the MPC controller,the ESO anti-disturbances compensation input and the FFC input is formed.Finally,real vehicle test is carried out,which verifies the feasibility and superiority of the improved MPC method of integrating disturbance compensation.(4)Since the current MPC-based variable curvature path tracking algorithm basically treats the vehicle state as constant speed,it is not suitable for the case of small range of longitudinal speed adjustment,which affects the accuracy of path tracking.In this paper,the path and speed decoupled control in Frenet coordinates is used instead of longitudinal-lateral-yaw complex coupled dynamics control to simulate the small range speed variation characteristics.Meanwhile,considering the problem that the steady state error of the MPC controller caused by curvature variation in the path tracking process cannot be eliminated,the adaptive weight control(AWC)and adaptive feed-forward(AFF)models based on BP neural network(BPNN)data learning are designed to dynamically adjust the lateral error weight and feed-forward factors of the MPC controller.As a result,a more accurate path tracking effect is achieved.Simulation results in the joint Car Sim-Matlab/Simulink environment show that the proposed algorithm significantly improves the adaptive capability of the linear MPC controller in response to time-varying conditions and has a higher tracking accuracy. |
PDF Full Text Request