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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...
This paper provides a simplified design scheme for the control of a class of nonlinear systems in strict-feedback form by incorporating dynamic surface control (DSC) method into an adaptive fuzzy logic system. The DSC method overcomes the problem of ldquoexplosion of complexityrdquo in the traditional backstepping design. Fuzzy logic system (FLS) is used to approximate the nonlinear uncertainty. Adaptation...
In this paper, we investigate the control design for a class of strict-feedback nonlinear systems preceded by unknown backlash-like hysteresis. Using the characteristics of backlash-like hysteresis, adaptive dynamic surface control (DSC) is developed without constructing a hysteresis inverse. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Function uncertainties...
Based on radial basis function neural networks (RBFNNs), adaptive dynamic surface control (DSC) is investigated for a class of uncertain strict-feedback nonlinear systems in this paper. By introducing first-order filter and combining DSC with backstepping, the operation of differentiation is replaced by simpler algebraic operation. Furthermore, the explosion of complexity in traditional backstepping...
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