A Reinforcement Learning Fuzzy Controller for the Ball and Plate system

Abstract

In this paper, a new fuzzy logic controller, namely Reinforcement Learning Fuzzy Controller (RLFC), is proposed and implemented. Based on fuzzy logic, this newly proposed online-learning controller is capable of improving its behavior by learning from experiences it gains through interaction with the plant. RLFC is well established for hardware implementation with or without a priori knowledge about the plant. To evidence this claim, a hardware implementation of Ball and Plate system was established, and RLFC was then developed and applied to it. The obtained results are illustrated in this paper. Index Terms-Fuzzy Logic Controller, Reinforcement Learning, Ball and Plate system, Balancing Systems, Model-free optimization I. INTRODUCTIONALANCING systems are one of the most popular and challenging test platforms for control engineers. Such systems are the traditional cart-pole system (inverted pendulum), the ball and beam (BnB) system, the multiple inverted pendulums, the ball and plate system (BnP), etc. These systems are the promising test-benches for investigating the performance of both model-free and model-based controllers. Considering those complicated ones (such as multiple inverted pendulums or BnP) even if one bothers to mathematically model them, the resulted model is likely to be too complicated to be used in a model-based design. One would highly prefer to use an implemented version of such a system (if available and not risky) and observe its behavior while the proposed controller is applied to it. This paper is devoted to the efforts done for a project in which the main goal is to control a ball over a flat rotary surface (the plate) mimicking human's behavior controlling the same plant, i.e. the BnP system. The proposed controller neither should be dependent on any physical characteristics of the BnP system nor should it be supervised by an expert. It should learn an optimal behavior from its experiences interacting with the BnP system and improve its action-generation strategy; however, some prior knowledge about the overall behavior of the BnP

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

“…The Ball-and-Plate (B&P) system can be described as an enhanced version of a Ball-and-Beam system, whereby the ball positioning is controlled in dual directions (Mohajerin et al, 2010). The B&P system finds practical application in several dynamic systems, such as robotics, rocket systems, and unmanned aerial vehicles.…”