Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

Abstract

To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional-integral-difference)- based control method are proposed. A lateral tracking controller is designed by using MPC with rule-based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

“…According to Equation (11), the output of the system in the predictive time N p domain can be obtained as:…”

“…Wang et al [9] targeted the vehicle's relative speed error as the control objective for longitudinal speed, employing a Radical Basis Function (RBF) neural network-based sliding mode control strategy. While PID and sliding mode control have improved tracking accuracy in complex environments, their lack of future state prediction and external disturbance identification renders them unsatisfactory in high-frequency and highly perturbed environments; predictive feedback control methods include Linear Quadratic Regulator (LQR) control [10] and model predictive control (MPC) [11]. The LQR predictive control algorithm is favored in the industry for its straightforward design and superior performance.…”

Longitudinal Functional Safety Analysis and Algorithm Design of Traffic Jam Pilot

“…Yassine Kebbati et al used genetic algorithms (GA-PID) and neural networks (NN-PID) two methods to realize adaptive PID control and thus accomplish the longitudinal control task [37]. Yang Can et al used fuzzy PID as a longitudinal tracking controller in trajectory tracking control to control the motor torque and braking pressure and accomplish the longitudinal tracking task [38]. Li Runmei et al proposed a longitudinal dynamics model for an autonomous vehicle and compared the control effects of various PID controllers [39].…”

Research on trajectory tracking control of intelligent vehicles based on model predictive control

“…In the literature [7], a trajectory tracking controller based on adaptive model predictive control was designed based on the vehicle dynamics model with side deflection angle constraints to improve the vehicle's stability. In the literature [8], a control method based on model predictive control and fuzzy PID was proposed to reduce the lateral tracking error to improve the stability and accuracy of quintuple polynomial trajectory tracking. In the literature [9], a dual closed-loop model predictive controller with external loop position control and internal loop velocity control is designed to reduce the error of AUV trajectory tracking.…”