Neurocontroller Design for Solar-Powered Wireless Network System

Solar lighting system offers a clean environment and therefore able to continue for a long period. In this paper presented is a neural network predictive control technique for a solar-powered wireless networked control system. Solar wireless networked control systems model is created by using back-propagation. Adaptive learning rate approach is used to identify the stochastic time delays in back-propagation neural network. The performance of the controller is based on minimisation of the mean square error. The simulation results demonstrate that the proposed design methodologies can achieve the prescribed performance requirements.