Hybridisation of neural networks and genetic algorithms for time-optimal control

Abstract

This paper presents the use of neural networks and genetic algorithms in time-optimal control of a closed-loop robotic system. Radial-basis function networks are used in conjunction with PID controllers in an independent joint position control to reduce tracking errors. The results indicate that using neural network controllers is more effective than using the trajectory pre-shaping scheme, reported in early literature. Subsequently, a genetic algorithm with a weighted-sum approach and a multi-objective genetic algorithm (MOGA) are used to solve a multi-objective optimisation problem related to time-optimal control. The results indicate that the MOGA is the best method in terms of the Pareto front coverage while the genetic algorithm with a weighted-sum approach is more effective in terms of finding the best individual according to the weighted-sum criteria. As a result of using both neural networks and genetic algorithms in this application, an idea of a task hybridisation between neural networks and genetic algorithms for use in a control system is also effectively demonstrated.