A hybrid evolutionary algorithm for recurrent neural network control of a three-dimensional tower crane

This paper is concerned with the control of an underactuated three-dimensional tower crane system using a recurrent neural network (RNN) which is evolved by an evolutionary algorithm. In order to improve the performance in evolving the RNN, a hybrid evolutionary algorithm (HEA) which utilizes the operators of a constricted particle swarm optimization (PSO) and a binary-coded genetic algorithm (GA) is proposed. Simulation results show that the proposed HEA has superior performance in a comparison with the canonical algorithms and that the control system works effectively.

► A control method of an underactuated 3D tower crane is introduced. ► To be more practical, damping and constraints of the system are considered. ► A recurrent neural network (RNN) is employed as a controller. ► To optimize the RNN effectively, a hybrid evolutionary algorithm (HEA) is proposed. ► The HEA outperforms the canonical particle swarm optimization and genetic algorithm.