Control of a ball-bot using a PSO trained neural network

Abstract

A ball-bot is an extremely agile mobile robotic platform due to its inherent instability. In order to maneuver at high speeds, a specialized controller is needed. A ball-bot can be modelled as two decoupled, 2-DOF pendulum on a cart systems. These systems comprise a classical and frequently encountered problem in the area of control theory. This paper proposed a novel technique for adaptive control of a ball-bot based on inverted pendulum on a cart system using particle swarm optimization (PSO) trained neural network. The generic PID controller is used to control the above mentioned system. The controller is able to learn the demonstrative behavior and keep the pendulum up right when subjected to perturbations. Mean Square Error for training data is found to be 7.68×10−3 and 5.5×10−4 for the testing data. The results show a promising future of the proposed technique.