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An adaptive neural network control scheme is developed for a class of nonlinear systems in the strict-feedback form. Compared with the existing approaches, the main advantage is that the developed scheme can be implemented by utilizing only one neural network approximator. Thus, the designed controller structure is simplified. In addition, less neural network can reduce the running cost in practical...
This paper addresses the robust fuzzy control problem for nonlinear multiple time-delay large-scale systems in the presence of modeling errors and the state variables unavailable for measurement. The Takagi-Sugeno (T-S) fuzzy control system with modeling errors is adopted for modeling the nonlinear multiple time-delay large-scale systems and establishing fuzzy state observer. In terms of Lyapunovpsilas...
Using backstepping method, a direct adaptive robust fuzzy control algorithm is proposed for a class of uncertain nonlinear strict-feedback systems in this paper. Fuzzy systems are utilized to approximate unknown parts of the desired control inputs. The key assumption is that the norms of the optimal approximation parameter vectors and the approximation errors are bounded, and the bounds are unknown...
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