Study On Control Strategy For Networked Control Systems

In recent years, networked control systems have been used widely in national defense, industrial control and medicine. At the same time, networked control systems also have become one of the academic research focuses of control theory and have made some achievements. Compared with the conventional point-to-point control systems, networked control systems have the following main advantages: the cyber source can be conveniently used for sharing of information resources; remote monitoring and remote control can be carried out; control system flexibility can be enhanced, system wiring and investment can be reduced; system can be expanded easily and is open extensively. Although networked control systems have advantages above, there are many challenging problems which need be solved urgently. The main problem is uncertain and time-varying network transmission delay introduced by communication network.The main research works are as follows:The network transmission delay is learned by statistical analysis for a large number of measured network transmission delays. Three kinds of common network transmission delay modeling methods are introduced, and time stamped linear neural network prediction method of network transmission delay is simulated which is used extensively.Networked control systems with random and bounded network transmission delay are modeled as a discrete control system with time-varying time delay under reasonable assumptions. Based on the Lyapunov-Krasovskii functional, linear matrix inequality sufficient conditions which make state-feedback and output-feedback closed-loop networked control systems asymptotically stable and H-infinity stable are derived. By matrix transformation, sufficient conditions above are converted to controller design methods which make the state-feedback closed-loop control system asymptotically stable and H-infinity stable.In the derivation process of matrix inequalities, a reasonable zero-equation is adopted to obtain upper bound and the sum term of the differential of Lyapunov functional is eliminated by constrained free weighting matrix. Despite the increase in the number of free weight matrix, the system conservativeness is reduced.The model reference adaptive control in brushless DC motor networked control systems is discussed. The network transmission delay element is converted to inertia element by one step Pade method and the approximate linear mathematical model of brushless DC motor networked control systems is obtained; The common Narendra model reference adaptive control strategy is adopted to design controller; For random and time-varying network transmission delay, time stamped linear neural network is employed to access current sampling period prediction value of network transmission delay. Therefore, the approximate linear mathematical model is obtained in every sampling period with different network transmission delay. Finally, the model reference adaptive control based on known object model is used to design feedback controller.The optimal state feedback controller of networked control systems based on dynamic programming is studied deeply. For uncertain and time-varying network transmission delay, two optimal state feedback controllers based on dynamic programming are presented. First controller:Actuator node is time driven by using buffer which stores control signal, and the actuating time is set to τ∈{max[τ(k)], h), therefore, the uncertain and time-varying network transmission delay is transformed into deterministic delay. The optimal state feedback matrix based on dynamic programming can be calculated off-line, which simplify the design of state feedback controller. However, the design approach is conservative for stability analysis and system design, because network transmission delay is increased artificially. Second controller:Time stamped linear neural network is introduced to access prediction value of network transmission delay in every sampling period, and optimal state feedback matrix is calculated on-line. The control system using this method has higher precision and better performance, but large amount of calculation, the next important research task is looking for faster algorithms. At last, the simulation results of these two control strategies are given.