Codesign Of Control And Scheduling For Networked Control Systems

With the development of control theory and network communication technology, networked control systems have received more and more attention. Compared with traditional point-to-point control systems, networked control systems have many advantages, such as low installation cost, ease of diagnosis and maintenance, and flexibility of operation and so on. However, because of the introduction of communication network, the characteristics of network make networked control systems have more problems to work out, such as the limited network resources, the optimal scheduling of network and so on. The limited network resources can result in the network-induced delays and degrade the performance of control systems and even make control systems unstable. The networked control systems are made up of network and control and the closed-loop control performance depends not only on the design of control algorithm, but on the scheduling of network resource. In this paper, the co-design of network transmission protocol and controller is considered. The proper network transmission protocol can improve the quality of network service, and the advanced controller design can improve the quality of control.In this paper, some basic concepts of networked control systems are firstly introduced, such as, theoretical background, developing status, existing problems, and so on. The network-induced delays are addressed in this paper.In order to improve the network utility, the real-time scheduling algorithm is proposed. It uses feedback and optimization theory to adjust the sampling period of every control loop dynamically based on dynamic EDF scheduling in order to make the sum of value of object function of every control loop get the minimum according to the available network utility, and improve the stability of the whole systems.In order to improve the quality of control, the PID controller based on single neuron is designed. Because the traditional PID controller adjusts parameters dynamically in difficulty and can not control the complex process or systems with slow time-varying parameters effectively, the neural network is combined with PID controller to make up for the disadvantages of traditional PID controller. Smith predictive control is a widely-used control method to compensate for time. In order to work out the Jacobian information of control plant, the system identification is introduced, which bases on radial basis function, to improve the stability of control systems.