联合参数整定与自适应的ADRC车道保持策略OA

With the development of intelligent driving technology,lane keeping has become an important function for active safety in commercial vehicles.However,under the influence of load variations,delayed steering response,and complex road disturbances,traditional control methods struggle to balance dynamic tracking performance and robustness.Active Disturbance Rejection Control(ADRC)exhibits good system adaptability,however,its parameter tuning relies heavily on empirical experience and lacks a dynam-ic optimization mechanism.To address this issue,a lane-keeping strategy combining parameter tuning with adaptive ADRC is pro-posed.This strategy optimizes controller parameters using the Adaptive Particle Swarm Optimization(APSO)algorithm and intro-duces a self-learning coefficient module to enhance system adaptability and stability in correcting lane deviations under different loads and road conditions.Simulation results show that,compared with traditional ADRC and Model Predictive Control(MPC)methods,the proposed method reduces vehicle lateral deviation more quickly while ensuring steering safety.Additionally,the pro-posed method exhibits lower computational complexity and smaller convergence values.