Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Abstract

In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

A Review of Autonomous Vehicle Path Tracking Algorithm Research

“…For example, in the literature [46][47][48][49][50], the MPC algorithm was improved to improve the path tracking accuracy and driving stability of driverless cars under right-angle turns, continuous curves and arc curves, and MPC-based integrated control algorithm was proposed and the tracking accuracy and driving stability were verified. There are also studies combining MPC with other algorithms, for example, Shi et al proposed a path tracking algorithm based on MPC and PID, added front wheel side bias constraints on the basis of traditional MPC and introduced relaxation factors, and designed hybrid PID controllers for different road conditions to improve the accuracy of vehicle speed control, and simulation results proved that the algorithm greatly improved the stability and tracking accuracy of vehicle control [ 51].…”

Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach

“…To address the problem of automatic control in autonomous driving systems, a variety of automatic control strategies have been developed and implemented, e.g., proportional–integral–derivative (PID) control [ 7 , 8 , 9 ], robust control ( ) [ 10 ], fuzzy logic control [ 7 , 11 , 12 ], sliding-mode control (SMC) [ 13 , 14 ], Lyapunov-based control [ 14 , 15 ], linear parameter-varying (LPV) control [ 16 ], Takagi–Sugeno control [ 17 ], and linear quadratic regulator (LQR) control [ 18 ]. Model predictive control (MPC) stands out as a highly effective control strategy, relying on the dynamic model of the system to anticipate future states.…”

MPC-based lane keeping for commercial vehicles with steering dead zone consideration

“…In reference [8], MPC was designed to address the properties of parameter uncertainty, time variation and nonlinearity in vehicle models. Reference [9] proposes an improved MPC vehicle path tracking control method. And introduces relaxation factors to improve the stability of vehicle control.…”