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In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state...
In this paper, by incorporating the dynamic surface control technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in pure-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of ??explosion of complexity?? inherent in the existing method. In addition, the...
A simple adaptive fuzzy control (SAFC) is proposed for a class of strict-feedback uncertain nonlinear systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of SAFC is developed base on the universal approximation of...
An adaptive neural network control (ANNC) is proposed for a class of strict-feedback uncertain nonlinear systems with unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of ANNC is developed based on the universal approximation of neural...
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