Reinforcement Learning with FCMAC for TRMS Control

Abstract

This study proposes an intelligent control scheme that integrate reinforcement learning in Fuzzy CMAC (FCMAC) for a Twin Rotor Multi-input and multi-output System (TRMS). In the control design, fuzzy CMAC controller is utilized to compensate for PID control signal and the reinforcement learning refines the compensation to the control signal. CMAC with fuzzy system has better performance than the conventional CMAC in TRMS attitude tracking control. With reinforcement learning, the proposed control scheme provides even better performance and control for the TRMS.