Adaptive fuzzy-neural tracking control for uncertain nonlinear discrete-time systems in the NARMAX form

In this paper, an adaptive fuzzy-neural control scheme is investigated for a class of single-input single-output (SISO) discrete-time nonlinear systems in the presence of bounded disturbances. The SISO systems are in the form of the nonlinear autoregressive moving average with exogenous input (NARMAX), which has received much attention in the discrete control field. In order to realize the stability, the systems are first transformed into a causal state space description, and an ideal controller is obtained. The desired controller is approximated by using the fuzzy-neural networks. Compared with the previous results for controlling NARMAX, the number of adaptation parameters is less, and thus, it reduces the computational burden. Finally, the feasibility of this theory is proven in a simulation.

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