A fuzzy-TD3 hybrid reinforcement learning framework for robust trajectory tracking of the Mitsubishi RV-2AJ robotic arm

This paper proposes a novel hybrid control architecture that synergistically integrates a fuzzy logic system with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to achieve precise, robust trajectory tracking for a 5-degree-of-freedom (5-DOF) robotic manipulator. The design merges the interpretable, rule-based reasoning and rapid transient response of fuzzy logic with the model-free, long-term adaptive optimization capabilities of deep reinforcement learning. Within this framework, a fuzzy supervisor delivers immediate corrective actions using real-time error states, while the TD3 agent concurrently learns an optimal control policy to manage the system’s nonlinear dynamics. Extensive…