Fuzzy-Model-Based Nonlinear Networked Control Systems Modeling And Analysis

With the development of the computer, communications and information technology, information through the network to realize control has become a development tendency. Networked Control Systems (Networked Control Systems, NCSs for short) is a kind of transmission information through the network to form a closed loop Control system, which has low cost, low weight, low power consumption, easy installation and maintenance and so on, thus gradually applied in industrial processes control, the mobile agent remote control and formation, aeronautics and astronautics and so on. However, due to the networked control systems exist in the network induced delay, packet loss, limited network access, such as the limited bit rate special circumstances, which results in the decrease of system performance in different ways, even divergence in the system. Therefore these negative factors have to be taken into account in analysis and synthesis of NCSs, and it is known that time-varying packet losses and variable sampling can be equivalently deemed as time-varying transmission intervals. How to compensate trans-mission delays and intervals in a linear NCS is a fundamental problem.Based on the results of the existing theory at present, this paper is concerned with fuzzy-model based stabilization of nonlinear networked control systems with time-varying transmission delays and transmission intervals based on a random-delay approach. First, the real-time distribution of input delays resulting from transmission delays and intervals is modeled as a dependent and nonidentically distributed process. Then a randomly switched Takagi-Sugeno fuzzy system with multiple input-delay subsystems is proposed to model the nonlinear NCSs. Based on an improved Lyapunov-Krasovskii method, which takes into account the real-time distribution of input delays in estimating cross-product integral terms, new sufficient conditions are derived for the exponential stability of the overall systems.And then, this paper is concerned with fuzzy-model-based robust stabilization of nonlinear networked control systems with time-varying transmission delays, transmission intervals and input missing based on a random-delay approach. The real- time distribution of input delays resulting from transmission delays and intervals is modeled as a dependent and non-identically distributed process, and the occurrence of input missing is represented as a Bernoulli process. Then a randomly switched Takagi-Sugeno fuzzy system with multiple input-delay subsystems is proposed to model the nonlinear NCSs. Based on an improved Lyapunov-Krasovskii method, which appropri-ately takes into account the real-time distribution of input delays in estimating cross-product integral terms and the characteristics of T-S fuzzy model, new sufficient conditions are derived for the mean-square robust exponential stability of the overall systems.The resulting controller design method is equivalent to a nonlinear convex optimization problem with LMI constraints. Numerical examples are presented to substantiate the effectiveness and advantage of our results.