Reinforcement learning control for trajectory tracking of rotary flexible link

This paper presents a deep deterministic policy gradient-based reinforcement learning (DDPG-RL) controller to address the reference tracking and vibration suppression problem of rotary flexible link (RFL) manipulator. Formulating the control design based on the actor-critic RL method that offers better stability of convergence compared to value based RL method, we synthesize a data-driven control framework that can guarantee precise trajectory tracking while minimizing the vibration of the flexible link. As the RFL system is unstable in open loop, for identifying the dynamical model of the RFL, we build an empirical auto-regressive (ARX) model using the closed loop identification technique. Subsequently, this work also performs the validation test to assess the goodness of the model. Moreover, the efficacy of the DDPG-RL control framework is experimentally validated on a laboratory scale RFL system using the hardware in loop (HIL) testing for precise tracking and robustness. The experimental results substantiate that the DDPG-RL scheme offers precise trajectory tracking and robustness against the external disturbances.