Quantitative Control Of Neural Networks With Time - Dependent Delays In Markov Chain

The closed-loop control systems where the plant, controller, sensor and actuator are connected through shared network are called networked control systems(NCSs). With the development of computer network technology, neural network control systems have a widely application in equipment manufacturers, aerospace, economic management, traffic control, remote medical treatment and some dangerous and special environments. Compared with the traditional point-to-point control systems, NCSs have many advantages, such as:high reliability, low cost, maintenance and easy installation. Because of the limitation of network’s communication bandwidth, time delay, packet loss and other issues may happen. At the same time, in order to optimize the use of cyber source, we will quantify the signal and consider the stability of the system. Recently, the stability of quantified neural network control systems has attracted a large number of scholars and made a lot of fruitful results. The paper consists of three parts:1. The opening section gives an overview on background and significance of the networked control systems with Markovian jumping parameters.2. We begin with introducing the feedback control model of the NCSs with Markovian jumping subject to quantization and dropout. A new Lyapunov-Krasovskii function and some new LMI techniques are employed to deal with the stability of the NCSs. A numerical example is presented to illustrate the usefulness and effectiveness of the main results obtained.3. We introduce the observer-based output feedback control system. New novel Lyapunov-Krasovskii functions and a linear matrix inequality (LMI) approach are utilized to derive sufficient conditions guaranteeing the stochastic stability of the considered neural networks. A numerical example is presented to illustrate the usefulness and effectiveness of the main results obtained.