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Longitudinal following control of intelligent vehicle fleet based on fuzzy MPC |
1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. Guangde Branch of SAIC General Motors Co., Ltd., Xuancheng, Anhui 242000, China |
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Abstract To solve the problems of low efficiency and unstable system in the traditional control, a longitudinal following model of the intelligent vehicle fleet was proposed based on the fuzzy model predictive control (MPC) method. According to the longitudinal following model of the fleet, the objective function of MPC including following performance and riding comfort was derived, and the fuzzy strategy was introduced based on the MPC controller. The followability weight coefficient in the MPC objective function was adjusted in real time, and the ideal acceleration was outputted to meet the needs of the driving scene. The lower-level controller was established by combining the longitudinal inverse dynamics model and PID control to convert the expected acceleration into throttle opening or brake pressure. The joint simulation platform of Carsim and Simulink was established to simulate the high-speed driving conditions of the intelligent vehicle fleet, and the simulation result was compared with those of PID method and traditional MPC method, respectively. The results show that the dynamic parameters of the fuzzy MPC controller under highspeed conditions can meet the constraints with the distance error less than 8 m. Compared with PID control and traditional MPC control, the maximum speed error is reduced by 6.6 and 2.5 m·s-1, respectively. In the emergency braking scenario, the speed change is more stable, and the riding comfort of the vehicle fleet is improved.
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