融合改进势场与模糊预测控制的智能车轨迹规划OA

To address the challenge of integrating trajectory planning and stability control for intelligent vehicles in complex dynamic environments,a trajectory planning and control method that combines a Modified Artificial Potential Field(MAPF)with Fuzzy Model Predictive Control(FMPC)was proposed.The MAPF improved the smoothness of path planning and obstacle avoidance capability by optimizing the repulsive force function and introducing a virtual target point and an equilibrium point strategy.The FMPC dynamically adjusted weighting coefficients using fuzzy rules,thereby enhancing the controller's adaptability to nonlinearities and uncertainties.Furthermore,an integrated closed-loop architecture of"perception-planning-control-stability"was constructed to achieve online collaborative optimization of obstacle-avoidance trajectory generation and stability constraints.Co-simulations were carried out based on CarSim and MATLAB/Simulink platforms.The results showed that within a speed range of 60～80 km/h,the proposed method reduces the lateral tracking error by 43.0%and the heading error by 59.6%under dynamic obstacle conditions compared with the traditional APF-MPC method,while the convergence of phase-plane trajectories is significantly improved,verifying its superiority and robustness under complex operating conditions.