Evolving a neural controller for a ball-and-beam system

This work presents an evolutionary controller design method for a ball-and-beam system. The method consists of a population of feedforward neural network controllers that evolve towards an optimal controller through the use of a genetic algorithm. The optimal controller is then applied to several different initial positions from which it has to balance the system. From the simulation results, we can conclude that the evolved neural controller can balance the system effectively.