Robust Control for Uncertain Networked Control Systems with Random Delays

Networked control systems (NCSs) are a type of distributed control systems where sensors, actuators, and controllers are interconnected through a communication network. This system setup has the advantage of low cost, flexibility, and less wiring, but it also inevitably invites some delays and data loss into the design procedure. The focus of this thesis is to address the problem of analysis and design of networked control systems when the communication delays are varying in a random fashion. This random feature of the time delays is typical for commercially used networks, such as a DeviceNet (which is a controller area network (CAN)) and Ethernet network. Models for communication network delays are first developed, in which Markov processes are used to model these random network-induced delays. Based on such models, we establish novel methodologies for stability analysis, control with disturbance attenuation, and fault estimation for a class of uncertain linear/nonlinear uncertain NCSs with random communication network-induced delays in both sensor-to-controller and controller-toactuator channels. Data packet dropouts in the communication channels also have been taken into consideration in the modelling and design procedure. The main technique used in this thesis is based on the Lyapunov-Razumikhin method, which results in delay-dependent controllers. We first consider the design problems for uncertain linear NCSs. In this case, state feedback controllers and dynamic output feedback controllers are designed to satisfy both stability and disturbance attenuation requirements for this class of NCSs. Moreover, a robust fault estimator that ensures the fault estimation error is less than a prescribed performance level is designed. We further go on to address the control problems for uncertain nonlinear NCSs. The nonlinear plant is first described by the T-S fuzzy model. Based on this model, stability analysis, disturbance attenuation, and fault estimation problems are studied for uncertain nonlinear NCSs. It should be noted that system uncertainties, disturbances and noises are addressed in both cases. The existence of such controllers and fault estimators are given in terms of the solvability of bilinear matrix inequalities. Iterative algorithms are proposed to change this non-convex problem into quasi-convex optimization problems, which can be solved effectively by available mathematical tools. Finally, to demonstrate the effectiveness and advantages of the proposed design methodologies in this thesis, numerical examples are given in each designed control sys-