Wireless Networked Control Systems with Neural Networks and Ant Colony Optimization Algorithm

Due to Wireless network delay and large delay time of the controlled plant, the performance of the wireless networked control systems (WNCS) is deteriorates. In order to effectively eliminate the influence of network delay and large delay time, a novel structure of the WNCS is proposed that new Smith predictor combined with neural network prediction and identification model. Nonlinear system's delay time must be well identified. The abrupt mutation result from the training error sum square of the real output and the expected output of the neural network was used to identify the delay time. The input sample period of the neural network was changed so that it could discriminate the delay time of the nonlinear model. The discriminated large time-delay was applied to neural network prediction model. The errors between input and prediction model output were used to search PID controller parameters based on ant colony optimization algorithm. The method was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness. This is the paper style requirement for the Chinese Control Conference. The writers of papers should and must provide normalized electronic documents in order for readers to search and read papers conveniently.