Signal and Data Processing

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This paper addresses a novel control method adapted with varying time delay to improve NCS performance. A well-known challenge with NCSs is the stochastic time delay. Conventional controllers such as PID type controllers which are just tuned with a constant time delay could not be a solution for these systems. Fuzzy logic controllers due to their nonlinear characteristic which is compatible with these systems are potentially a wise option for their control purpose. Fuzzy logic controller could become adaptive by means of neural networks and beneficial to deal with the varying time delay problem. This novel method suggests an adaptive fuzzy logic controller which has been controlled and adapted through the neural network. The rule-based table of designed fuzzy logic controller rotates in relation to estimated time delay. The amount of rotation is obtained from neural network. The proposed method follows the input easily, despite classical methods which result in an unstable system especially over the large time delays as large as 600 ms.

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Networked control systems (NCSs) 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.