Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor-critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov's direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control.

Automated summary

This article discusses the control design and experiment validation of a flexible two-link manipulator system represented by ordinary differential equations, introducing a reinforcement learning control strategy based on actor-critic structure. The closed-loop system with the proposed control algorithm is proven to be semi-global uniform ultimate bounded by Lyapunov's direct method. Simulation and experiments demonstrate the effectiveness of the control approach in vibration suppression and trajectory tracking.