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Model Predictive Control (MPC) combined with the planning layer can realize the autonomous obstacle avoidance of vehicles. Still, the generated trajectories are often not smooth enough and have considerable tracking errors, resulting in poor stability and comfort of cars. To address this problem, a PID-MPC dual closed-loop intelligent vehicle controller that can reduce tracking error and improve vehicle stability and comfort is designed. First, the differential term of the traditional PID control algorithm is introduced into the first-order inertial filtering link to enhance vehicle stability and reduce tracking error. Secondly, based on the nonlinear model of vehicle dynamics, the MPC controller was used as the outer control loop, the improved PID controller was used as the internal control loop, and the dual-loop controller solved the optimal control sequence to realize the dual closed-loop optimal control of the intelligent vehicle. Finally, the feasibility and effectiveness of the designed PID-MPC dual closed-loop controller were verified by joint CarSim/Simulink simulations. The simulation results showed that the tracking trajectory generated by the PID-MPC controller was smoother, the front wheel steering angle, yaw angle, and lateral acceleration changed more smoothly with smaller amplitudes, and the car's comfort and stability both increased.
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