NETWORKED CONTROL SYSTEMS ((NCS)) is a new field in CONTROL SYSTEMS that has emerged by the use of communication networks in CONTROL SYSTEMS. (NCS) refers to a CONTROL system in which sensors, actuators, and CONTROLlers are connected via a communication network and enable remote monitoring and CONTROL. The use of communication networks in CONTROL SYSTEMS have many advantages, like reducing the cost of wiring, increasing flexibility, and improving scalability. However, the integration of communication networks in CONTROL SYSTEMS will also carries new challenges such as data transmission delay, data packet loss, and network congestion, that require new methods for modeling and designing CONTROL SYSTEMS. Research in the NETWORKED CONTROL SYSTEMS is focused on the development of new modeling, estimating, identifying, and designing methods, which the effects of communication networks on the system’s behavior will be taken into account. In general, the field of (NCS) is an important research area because it allows the CONTROL SYSTEMS to be applied in a wide range of applications and develop it in more efficient and flexible way. (NCS) can be found in various applications such as industrial automation, smart energy grids, transportation SYSTEMS, and house automation. This article describes the emergence of CONTROL SYSTEMS from the field of continuous time to NETWORKED CONTROL and compares it with traditional CONTROL SYSTEMS. Furthermore, the most important challenges in (NCS) and related approaches will be discussed. Important applications of NETWORKED CONTROL SYSTEMS by reviewing some featured case studies in Iran and other countries are also discussed. In the end, possible future directions in the development of NETWORKED CONTROL SYSTEMS will be presented.

A new Markov-based method for real time prediction of network transmission time delays is introduced. The method considers a Multi-Layer Perceptron (MLP) neural model for the transmission network, where the number of neurons in the input layer is minimized so that the required calculations are reduced and the method can be implemented in the real-time. For this purpose, the Markov process order is estimated offline, using pr-recorded network time delay history. Unlike most of the previously existing methods, the proposed approach is both accurate and fast enough for a real time implementation. Using such a scheme for real-time estimation of the upcoming time delays, a variable state feedback gain CONTROL scheme is also proposed and applied to the predicted discretized model of the plant. The proposed approach is shown, through well known benchmark problems, to be both accurate and fast enough for a real time implementation.

In this paper, based on the state-dependent Riccati equation (SDRE) technique, a new method is proposed to design nonlinear observers in a iiotb class of NETWORKED CONTROL SYSTEMS. Towards this end, an inequality condition is obtained to detect the time-instants of sending messages from the sensors to the CONTROLler. In this way, the data transmission is done in some specific instants and therefore, the proposed method can considerably reduce the information exchange between the sensors and the CONTROLler in a nonlinear NETWORKED CONTROL system. It is also proved that the obtained estimation converges to the system state. Two numerical simulations are worked to illustrate the design procedure and the flexibility of the proposed observer.

NETWORKED CONTROL SYSTEMS ((NCS)s) are distributed CONTROL SYSTEMS in which the nodes, including CONTROLlers, sensors, actuators, and plants are connected by a digital communication network such as the Internet. One of the most critical challenges in NETWORKED CONTROL SYSTEMS is the stochastic time delay of arriving data packets in the communication network among the nodes. Using the Smith predictor as the CONTROLler is a common solution to overcome network time delay. Online and accurate modeling of the plant improves the performance of the NETWORKED CONTROL system, especially when the plant is nonlinear and has unknown parameters and time-variant behavior. In this paper, a novel CONTROLler, Neural-Smith predictor, is proposed, which firstly models plant using a perceptron neural network and secondly, another neural network is used as the core of signal processing of the CONTROLler. The parameters variation of the plant during time is considered online by the CONTROLler, and then the desired CONTROL signal is generated. The Integral of Time multiplied by the Absolut value of Error (ITAE) is a proper performance index for position CONTROL, so this index has been used to compare the results. Results of simulations show that (NCS) using the Neural-Smith predictor has better performance in comparison to the common Smith predictor and the novel compensation method using a modified communication disturbance observer (MCDOB) when the values of network time delay and variation of plant’ s transfer function are excessive. For example, while the range of stochastic time delay is between 19 and 21 ms, the difference between the ITAE of CONTROLlers is 0. 0004. This value increases to 0. 027, while the range of stochastic time delay is between 910 and 930 ms.

This paper presents a novel event-triggered predictive CONTROL (ETPC) approach for the stabilization of discrete-time output-feedback NETWORKED CONTROL SYSTEMS ((NCS)s). The studied (NCS) is considered to be subject to both random external input and output disturbances, and network imperfections including random communication delay, random packet dropout, packet disorder, limitation of network bandwidth, and network resources. In the proposed algorithm, an observer-based event detector is designed for reducing the number of sent packets through the communication network using the estimated system states by the Luenberger observer. In this way, the system’s energy resources are saved and network-induced effects are skipped. A switched predictive CONTROLler with multiple gains are used to compensate for network-induced effects. CONTROLler gains are designed compatible with different possible values of delays and packet dropouts. A novel augmented representation of the state-space equations of the system is derived to design observer gain and CONTROLler gains. The asymptotic stability of the system is guaranteed by designing the observer and CONTROLler based on the Lyapunov function through solving linear matrix inequalities (LMIs). Putting all the aforementioned points together has made the whole framework presented in this paper a comprehensive one. The effectiveness of the proposed approach is demonstrated by comparative simulation results.