(An) accelerated co-evolutionary algorithm and its application to attitude control design of flexible satellites

Abstract

A new co-evolutionary algorithm, of which the convergence speed is accelerated by neural networks, is proposed and verified in this thesis. To reduce computational load required for co-evolutionary optimization processes, the cost function and constraint information is stored in the neural networks, and the extra offspring group, whose cost is computed by the neural networks, is generated. It increases the offspring population size without overloading computational effort; therefore, the convergence speed is accelerated. The proposed algorithm is applied to attitude control design of flexible satellites, and it is verified by computer simulations and experiments using a torque-free air bearing system.