Residual RL on a PSO-tuned Fuzzy Controller for Mobile Robot Trajectory Tracking
This paper presents a two-wheeled mobile robot trajectory-tracking controller combining a particle swarm optimization (PSO)-tuned fuzzy logic controller (FLC) with a residual reinforcement learning (RL) correction layer.PSO tuning reduces the global distance error by 35% and the integral absolute error by 44% over the initial FLC.The residual RL layer further reduces the global distance error by approximately 2.3% and improves cornering-region tracking by 3.9% in RMSE, 4.7% in IAE, and 5.2% in peak distance error.The proposed controller also reduces the global distance error by 41% and 66% relative to independently tuned PID and fuzzy-PID baselines.Trained across four trajectory families with a held-out test split, the generalized agent reduces the average test distance error by 18% relative to the tuned FLC baseline.These results show that a lightweight residual correction improves both accuracy and generalization while preserving the fuzzy controller's interpretability.