This paper addresses the problems of discontinuous curvature and complex path tracking of parallel parking paths for autonomous driving, establishes its vehicle kinematic model based on Ackermann steering theory; determines the collision constraints of the parking process, and uses B-spline curve theory to plan the parking path. On this basis, a multi-objective optimization method of Genetic-Particle Swarm Optimization Algorithm(G-PSOA) is proposed to optimize the design of the spline curve control points with the vehicle travel trajectory length, wheel angle, and curvature of the path curve as the optimization objectives of Path Planning (PP). The simulation results show that the proposed optimization algorithm can quickly search for the optimal path compared with the traditional Particle Swarm Optimization (PSO), shorten the PP length by 4.2%, and improve the smoothness and continuity of the parking process, thus reducing the complexity of the path tracking control.
In order to improve the performance of permanent magnet synchronous motor (PMSM), an improved index reaching law is presented by introducing the system state variable to solve the problems such as slow tightening speed and high frequency chattering of traditional index reaching law. A fuzzy sliding mode speed control system for PMSM is designed by adjusting the parameters of the reaching law with fuzzy control, and the simulation results are compared with the traditional index reaching law. The results show that the improved approach law can effectively reduce the high frequency chatting at the origin compared with the traditional index approach law, and the PMSM speed control system has faster response speed, better robustness and dynamic and static performance.
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