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This A novel approach to random, time-variant network delay in the networked control systems (NCSs) and a controlled plant is proposed in this paper. The approach is a new Smith predictor combined with fuzzy radial basis function neural network (FRBFNN) control. It can identify the controlled plant and adaptively adjusts weights of the controller. Because new Smith predictor hides predictor model...
In this paper, a quasi-sliding mode variable structure control algorithm is combined with RBF neural network. So, the strong robustness of the quasi-sliding mode variable structure algorithm and the property of adaptive learning supplying from RBF neural network algorithm are combined. The algorithm is subsequent inducted into the large time-delay system. Simulation results show that the strategy...
This paper presents a generalized predictive controller based on RBF neural network (RBF-NN) for a class of nonlinear system with time delay. The procedure of the proposed control system includes two parts: RBF-NN modeling and predictive control algorithm design. The RBF-NN model can predict future outputs of the plant and the predictive value can be amended online, which allows it to employ to complex...
Researching the application of neural networks and the Smith predictor in the system with time-delay, considering the delayed problem of the system, a new control approach is introduced, which can realize the predictive control by combining the Smith predictor with the NN and PID control which is tuned by BP neural network through the predictive errors effectively. We use the RBFNN in the Smith predictor,...
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