A moving-mass control system for spinning vehicle based on neural networks and genetic algorithm

Abstract

The ability of a moving-mass control system to control a spinning vehicle using two internal moving mass actuators is investigated. The nonlinear equations of motion are provided, and the influence to the system of moving masses' motion with respect to the vehicle's shell is described. For the selflearning capacity of the neural networks and the optimum ability of the genetic algorithm, the hybrid trajectory PID control scheme based on the neural networks and genetic algorithm is produced to improve the dynamic qualities and the adaptive capacity of the system. A nonlinear simulation of a typical mission profile demonstrates the ability of the controller to effectively control the vehicle's trajectory. © Springer-Verlag Berlin Heidelberg 2005.