Predictive Control For Networked Control Systems

Compared with traditional control systems, networked control systems have the advantages of less wiring, simplification of maintenance and installation, reduction in the cost of cables and power, and improvement of reliability. However, the limited communication bandwidth, the network-induced delay and the packet dropout should be considered in the design and analysis of networked control systems. On the other hand, there are nonlinearities in practical systems. Therefore, as an effective approach of modeling nonlinearities, the Takagi-Sugeno (T-S) fuzzy model has gained popularity. The model predictive control strategy which can handle model uncertainties and constraints has been applied in complex industrial process control. Although there have been considerable research results about model predictive control, few of them involve packet dropout.First, this thesis presents a new robust model predictive control (MPC) algorithm for a class of discrete-time linear uncertain systems with state delays and input constraints. Second, the problem of interval type-2 T-S fuzzy predictive control for nonlinear networked control systems subject to parameter uncertainties and defective communication links is studied. The problems of stabilization, stochastic stability and H2/H∞ control are investigated. Based on the existing method and technology, both the theoretical analysis and simulation results are proposed. Overall, with regard to the nonlinear networked systems with packet loss, this thesis provides the basis and reference of the theory and methods for extended research and further application.