Neural Network Predictor Control (NNPC) for Solar-Powered Wireless Networked Control System

Abstract

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. Solar lighting system offers a clean environment, therefore able to continue for a long period. In this paper presented is a Neural Network Predictive Control (NNPC) technique for a Solar-powered Wireless Networked Control System (SWNCS). SWNCS model is created by using Back-Propagation Neural Network (BPNN) with adaptive learning rate. A learning algorithm adopting an adaptive learning rate approach is used to identify the stochastic time delays in BPNN. The performance of the NNPC controller is based on minimization of the Mean Square Error (MSE). A numerical example is given to demonstrate the effectiveness of the proposed method. Further, the SWNCS based NNPC simulation techniques using MATLAB/SimuLink are carried out to validate the proposed control algorithm.