Adaptive Control With Dynamic System Test Device And Research

The research of combination of neural network and adaptive theory has been an important topic in the intelligent control. As neural network adaptive control not only has the good robustness as that in the adaptive systems, but also has the ability of self-learning and good fault-tolerant, it is very interesting for the control theory and application to research how to combine neural network with adaptive control.The paper mainly researches the modeling and control for the resisting moment loading system in a loading and test device for servo system. The paper firstly expounds the basic problem and theory of neural network, and a typical multi-layer feed-forward artificial neural networks named BP network has been studied. But traditional BP neural network has many defects, such as slow training velocity and converge to a local minimum point, while LM-BP algorithm has much better performance.The paper then researches the model identification based on neural network, presents the normal structures of neural network identification and discusses the identification for BP neural network. According to a set of experiment data of magnetic particle brake running in constant speed, the paper has made system identifications of positive and offline model based on neural networks, and analyses identification result.Finally, the method incorporates adaptive theory with neural network to obtain an intelligent controller, on account of model identification. The paper, combining the method and the control of resisting moment loading system, researches the neural network of indirect self-tuning controller algorithm, and carries out simulation studies. A set of simulation results shows that the design of indirect self-tuning controller can make the system has good dynamic and static performance, and realizes precise control of resisting moment loading system.