Research Of Networked Control System Based On Grey Prediction

Network induced delay becomes the outstanding issue while the network control system is widely used with the expansion of the industrial process control system. To reduce the adverse impact from network factors, how to design an optimum controller to increase the efficiency of network control system and to expand the scope of application is a new challenge. With considering the demands of the actual control system and the network environmental factors, studying and investigating an appropriate control strategy is an urgent issue to be solved, and it has great significance in theoretical research and actual application. As a result, a golden section controller based on grey prediction is designed by the detailed analysis of the network induced delay. And the system stability is derived from Lyapunov theory. Meanwhile, the feasibility and effectiveness of the proposed method are verified by the network control platform based on the inverted pendulum. The specific research is as follows:Firstly, the statistic characteristics of the random delay from Ian were analyzed. The experimental platform was established based on the campus network application layer, so that enough network time delay data could be collected on the experimental platform. On this basis, the stability, periodicity and normality of the time delay were researched by the method of constructing statistics based on spectral density, the periodic chart method and the J-B test method respectively, which demonstrated that the network time delay data tended to be stable and without any periodization, and the data didn’t obey the normal distribution.Secondly, a new adaptive control strategy of golden section with grey prediction based on feature model was designed. Based on the two order characteristic model of the controlled object, the golden section controller was constructed, and a grey prediction model was introduced in its feedback loop. At the same time, the stability of the system was analyzed by the Lyapunov theory; the step size of the grey prediction model was adjusted to improve the control performance on account of the output error of the system. The good control effect was proved by the simulation.Lastly, the adaptive control strategy of golden section with grey prediction based on feature model was applied to the inverted pendulum system. The two order characteristic model and the grey prediction step size were derived from the kinetic equation of single inverted pendulum. So the optimal gold segmentation controller could be established. The results by inverted pendulum system platform control experiment demonstrated that the performance of the proposed control method was superior and its real-time was higher, and an experimental basis was laid for the further application in actual.