Analysis of Model-Free Reinforcement Learning Control Schemes on self-balancing Wheeled Extendible System
This work addresses control challenges in robotic systems like the E-WIP, offering an alternative to traditional nonlinear methods, but it is incremental as it applies existing RL techniques to a specific domain.
The paper tackled controlling a self-balancing wheeled extendible system by applying reinforcement learning (RL) methods, specifically Deep Deterministic Policy Gradient and Proximal Policy Optimization, and found that these RL controllers outperformed a model predictive control (MPC) controller in terms of state variables and trajectory errors.
Traditional linear control strategies have been extensively researched and utilized in many robotic and industrial applications and yet they do not respond to the total dynamics of the systems. To avoid tedious calculations for nonlinear control schemes like H-infinity control and predictive control, the application of Reinforcement Learning(RL) can provide alternative solutions. This article presents the implementation of RL control with Deep Deterministic Policy Gradient and Proximal Policy Optimization on a mobile self-balancing Extendable Wheeled Inverted Pendulum (E-WIP) system with provided state history to attain improved control. Such RL models make the task of finding satisfactory control schemes easier and responding to the dynamics effectively while self-tuning the parameters to provide better control. In this article, RL-based controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables and trajectory errors of the E-WIP system while following a specific desired trajectory.