Research On Networked Control Systems Based On Delay Compensation And Predictive Control

Networked control systems (NCS) are distributed feedback control systems whose sensors, actuators, and control units are connected through communication networks. NCS are the collaboration of three engineering fields, computer engineering, communication engineering and control engineering, and have become a novel interdisciplinary research front as the new direction of modern industry. This type of systems has the advantage of greater flexibility with respect to traditional control systems. It also permits greater agility in diagnosis and maintenance procedures. Therefore, some problems such as time delay, data package loss, may occur in the network transmission, which can make the analysis to NCS a difficult task. Because the classical control theory can not be applied to NCS'controller design directly, in this thesis, the research focuses on the problem of the networked delay during the design of the networked control systems.Firstly, this thesis introduces the background and the present control methods of NCS, then the degradation caused by delay is shown via theory analysis and simulation.Secondly, a state prediction control method that models the state of the plant in the controller is adopted to compensate the delay between sensor and controller. Stability analysis, controller design are studied based on state prediction.Thirdly, an improved dynamic matrix control (DMC) algorithm for delay compensation design is adopted based on the model of networked control system with random delays. The algorithm can compensate the time delays between sensor and controller, and the effectiveness of this algorithm is presented in this thesis.Finally, an approach based on generalized predictive control (GPC) to realize compensation of the network delay by setting network delay compensator actuator is adopted. The algorithm makes full use of the predictive output sequence and control increments sequence. A new GPC algorithm combined with fuzzy control is adopted to solve model mismatch and noise disturbance. Simulation results show the validity of the proposed method.