B. Emran

Constrained Residual Reinforcement Learning Augmentation of MRAC for Actuator-Safe Nonlinear Tracking

Robust tracking under nonlinear uncertainty and actuator constraints remains challenging for model-reference adaptive control (MRAC). Deep reinforcement learning (RL) can compensate for nonlinear effects, but a standalone policy provides no inherent model-reference structure. This paper combines an MRAC backbone with a...