Abstract
A novel adaptive fuzzy dynamic surface control (DSC) scheme is for the first time constructed for a larger class of (multi-input multi-output) MIMO non-affine pure-feedback systems in the presence of input saturation nonlinearity. First of all, the restrictive differentiability assumption on non-affine functions has been canceled after using the piecewise functions to reconstruct the model for non-affine nonlinear functions. Then, a novel auxiliary system with bounded compensation term is firstly introduced to deal with input saturation, and the dynamic system employed in this work designs a bounded compensation term of tangent function. Thus, we successfully relax the strictly bounded assumption of the dynamic system. Additionally, the fuzzy logic systems (FLSs) are used to approximate unknown continuous systems functions, and the minimal learning parameter (MLP) technique is exploited to simplify control design and reduce the number of adaptive parameters. Finally, two simulation examples with input saturation are given to validate the effectiveness of the developed method.
Highlights
In the past several decades, approximation-based adaptive control of nonlinear systems has been attracting much attention, and many significant results have been achieved [1,2,3,4,5,6,7,8,9,10,11]
In [12], an adaptive fuzzy control scheme was proposed for a class of nonlinear pure-feedback systems under the framework of backstepping, which requires no priori knowledge of the systems dynamic
In [25], an adaptive neural controller is investigated for a class of pure-feedback nonlinear time-varying systems with asymmetric input saturation nonlinearity in combination with the Gaussian error function
Summary
In the past several decades, approximation-based adaptive control of nonlinear systems has been attracting much attention, and many significant results have been achieved [1,2,3,4,5,6,7,8,9,10,11]. In [12], an adaptive fuzzy control scheme was proposed for a class of nonlinear pure-feedback systems under the framework of backstepping, which requires no priori knowledge of the systems dynamic. In [14], an adaptive fuzzy control scheme is presented for a class of pure-feedback nonlinear systems with immeasurable states by utilizing backstepping methodology. In [25], an adaptive neural controller is investigated for a class of pure-feedback nonlinear time-varying systems with asymmetric input saturation nonlinearity in combination with the Gaussian error function. For a class of uncertain nonlinear systems with input saturation constraint and external disturbances, a tracking control scheme is proposed by introducing an auxiliary system in [27].
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