Analysis of Model-Free Reinforcement Learning Control Schemes on self-balancing Wheeled Extendible System
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Traditional linear control strategies have been extensively researched and utilized in many robotic and industrial applications and yet they don't 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 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 Extendible Wheeled Inverted Pendulum (E-WIP) system. Such RL models make the task of finding a satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control. In this article, two RL-based controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory.
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