Online reinforcement learning control of robotic arm in presence of high variation in friction forces

Abstract

The operation and accuracy of industrial robotic arms can be negatively affected by significant fluctuations in friction forces within their joints, potentially resulting in financial and operational losses. To mitigate these issues, an online model-free reinforcement learning controller specifically designed to handle high variations in joints’ friction forces. To the best of our knowledge this is the first time where reinforcement learning controller is used to handle high friction variations in a robotic arm. Initially, the dynamic equations of the robotic arm are derived, verified and validated to ensure an accurate representation of real-world behaviour. The stability of the closed-loop system is analyzed using the Lyapunov second method. The performance of the proposed controller in terms of position tracking is compared against four commonly used controllers found in literature for similar applications: (i) nonlinear model-based computed torque controller, (ii) proportional-derivative controller, (iii) adaptive iterative learning controller and (iv) radial basis function neural network adaptive controller. Simulation results demonstrate that the reinforcement learning controller outperforms the other controllers in terms of tracking performance, even in the presence of significant variations in joint friction forces.