Analysis And Design Of Networked Control Systems Under Constraints

The rapid development of network technology has advanced many new research areas, among which the networked control system (NCS) has drawn a lot of attentions in recent years. The NCS which covers multiple disciplines, including control, communication, networks and computer, has been widely applied in many engineering fields and a number of theoretic achievements have also been reported. Yet, some open problems still exist to be solved. Among various issues restricting the application of NCSs, the stability and controller design have been studied in this thesis, considering various communication constraints, completeness of plant information, and operation conditions of control system components. The following technical contributions have been made in this thesis:1) First, we assume that the information of plant is complete, i.e. the model of the plant is known, a single communication channel presents in the system and the communication time is limited. Given these assumptions, the stability analysis and controller design are examined under two cases: in the first case, Lyapunov-Razumikhin approach is used to analyse the asymptotic stability of NCSs with a linear plant model and Markov type stochastic delays, and a dynamic output feedback controller is designed to stabilize the system. In the second case, the Lyapunov-Krasovskii method is employed to analyse the asymptotic stability of NCSs with a more complex nonlinear plant model and time-varying delays with an upper bound, and a fuzzy controller is designed to deal with the system stabilization problem.2) Then, we consider the problem where the information of the plant is incomplete, and assume that the plant model has an uncertainty with known boundary. Following the two cases studies in 1) we can obtain the stability conditions and the corresponding controller design with norm bounded uncertainties. Further, a modified cone-complementarity linearization (CCL) is used to solve bilinear problems and a parallel distributed compensation (PDC) technique is employed to model the network delays.3) Next, we assume that the plant model is unknown a priori, which is more common in many engineering applications. In particular, we investigate the identification and control of Hammerstein systems over wireless networks. Firstly, a new model-based networked identification (MBNI) method is proposed for Hammerstein systems. Then with this identified model, model-based control strategy with inverse transformation is applied to networked control to compensate network induced delays and nonlinearity respectively. In addition, H∞control is used to deal with disturbances induced by the error between the model and the real system. Then a sufficient condition for system mean-square asymptotic stability is obtained in the form of linear matrix inequalities (LMIs) and controller gains can thus be directly solved.4) We then consider the NCS with multiple communication channels and stochastic delays. In 1)– 3), we focus on stability analysis and controller design with different degree of completeness of plant information and a single communication channel. In this part, we concentrate on different communication constraints. We assume that multi-sensors are connected in a Token-ring network, and one and multiple Markov chains are used to characterize the stochastic delays. A parameter-dependent Lyapunov function is employed to analyse the system stability, and the state feedback controller and the buffer technique are applied to the system stabilization problem.5) We further investigate the stability and stabilization of NCSs subject to both communication time and signal-to-noise ratios (SNRs) constraints. Firstly the minimal signal-to-noise ratios (SNRs) required for stabilisability are obtained in continuous-time networked control system (NCS) with varying delays using the linear matrix inequality (LMI) approach. The results are then extended to discrete-time heterogeneous networked control systems consisting of a wireless network with more channel noise and a Token-ring network with more delays. Finally the above theoretic results are tested on several numerical examples, and relations among unstable poles, delays and SNRs are illustrated explicitly.6) Next, we consider the situations where the operation conditions of control system components are subject to constraints. In 1)-5) it is assumed that the actuators and sensors are fault free, but these components sometimes may be subject to faults. To tackle this problem, an adaptive fault-tolerant control approach for networked control systems (NCSs) with actuator faults is developed. Based on a new network-induced delay model proposed recently, we design an adaptive state feedback controller. A sufficient condition for the existence of a controller is given in terms of linear matrix inequality (LMI), which guarantees the stability of NCSs under normal and faulty conditions in the H∞sense.7) Finally, the validity and effectiveness of the above techniques are demonstrated in numerical simulations. It should be noted that in the simulations of wireless network delays and SNRs, the inverse Gaussian distribution delays model developed by the Intelligent System and Control group at Queen's University Belfast, and the two inverted pendulums on carts coupled by a spring used in Shanghai Key Laboratory of Power Station Automation Technology at Shanghai University, have provided a solid foundation for development and application of the proposed techniques.