Control And Filtering For Nonlinear Networked Systems Based On Polynomial Fuzzy Model

Most systems in practical are usually nonlinear systems. The T-S fuzzy model based control has drawn considerable attention because of its high ability on modeling nonlinear systems. In recent years, the T-S system approach has been generalized to the polynomial T-S system approach, which inherits the virtues of the T-S systems approach and has two additional advantages, such as simplify the modeling process and reduce the conservativeness of stability conditions.On the other hand, as the rapid development of information technology, the networked systems has gotten considerable attention, however, some challenges are introduced at the same time, such as transmission delay, data packets dropout and data quantization. Referring to the aforementioned problems, the research methods of polynomial fuzzy networked systems are investigated from the following aspects.The first chapter introduces the background and advantages of polynomial fuzzy networked systems so that the significance of this paper is established. Meanwhile, the methods and technologies used in this paper are presented.The second chapter is concerned with the stabilization problem for polynomial fuzzy networked control systems. Two communication features including signal transmission delays and data missing are taken into account in a network environment. To solve the network-induced communication problems, a novel sampled-data fuzzy controller is designed to guarantee that the closed-loop system is asymptotically stable. Finally, a simulation example is provided to demonstrate the feasibility of the proposed method.The third chapter investigates the tracking control problem for polynomial fuzzy networked systems with repeated scalar nonlinearities. A polynomial fuzzy controller is designed to drive the system states to follow those of a given reference model. The phenomena of data missing and data quantization are taken into account, and handled via multiple Bernoulli trials and logarithmic quantizer model. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.The fourth chapter investigates the filtering problem for polynomial fuzzy networked systems under event-triggered scheme. A polynomial fuzzy filter is designed to estimate the control output. In order to reduce the data size transmitted in the network, the signal quantization is managed via a logarithmic quantizer. A novel method is designed to guarantee the filtering error system to be asymptotically stable and satisfy the desired performance. A simulation example is provided to demonstrate the effectiveness of the proposed results.The fifth chapter investigates the problem of fault detection for nonlinear networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel method is developed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed to reduce the conservativeness. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed results.