Automatic control of ball and beam system using Particle Swarm Optimization

Over the last few decades, many evolutionary algorithms have emerged. One such algorithm is Particle Swarm Optimization which emulates social and cognitive behavior of bird-flocks. In this paper, particle swarm optimization algorithm is presented as a robust and highly useful optimization technique to tune the gains of the PID controllers in the two feedback loops of the classic Ball and Beam Control System. As the name implies, Ball and Beam control system tends to balance a ball on a particular position on the beam as defined by the user. Various trials of the algorithm with varying parameters is implemented on the control system and the fitness of the response of the system to a unit step input is used as a criterion for judging the most optimal solution. A slight variation in the fitness value function is also introduced, rather than using conventional performance integrals as a criterion. Furthermore, the results from PSO tuning are quantitatively compared to ITAE equations method of PID tuning and Fuzzy-logic controller to reach a conclusion on the most efficient controlling technique.