基于改进差分搜索的沿墙机器人模糊逻辑控制算法OA

Fuzzy logic control effectively handles uncertainties and nonlinear problems in mo-bile robot motion control,but its performance depends on parameter and rule base configura-tion,requiring significant manual tuning effort.To reduce the workload of fuzzy logic pa-rameter adjustment in wall-following robot control,a fuzzy logic control algorithm based on improved differential search(FLC-IDS)is proposed.The algorithm introduces an adap-tive adjustment strategy for randomization probability parameters P1 and P2 to balance global exploration and local exploitation and automatically optimizes fuzzy logic control pa-rameters through preset reward-penalty strategies and reinforcement learning-based fit-ness evaluation mechanisms.Experimental results show that compared with manual tuning methods,FLC-IDS reduces the number of manually tuned parameters by 55.7%,achieving mean absolute errors of 43 mm and 52 mm in training and testing environments respectively,demonstrating good control accuracy and robustness.The method provides an effective ap-proach for automatic FLC parameter optimization without relying on labeled data.