Sine-map chaotic PSO-based neural network predictive control for deployable space truss structures

The neural network predictive control (NNPC) based upon a novel sine-map chaotic particle swarm optimization (SCPSO), which is applied to the kinetic energy control of the deployable space truss structures (DSTS), is developed in this paper. The proposed control scheme is proved to be feasible to the kinetic energy control of the DSTS via the simulation experiment. Furthermore, the experiment demonstrates that SCPSO has better searching performances than chaotic particle swarm optimization (CPSO) and particle swarm optimization (PSO).