Abstract
In this paper, the output feedback adaptive multi-dimensional Taylor network (MTN) tracking control for a class of nonlinear systems with unmeasurable states is investigated. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states, and then an adaptive MTN-based output-feedback control approach is developed via backstepping technique. Secondly, in view of the simple structure of MTN, the controller based on MTN has the advantages of simple structure and fast calculation speed. Thirdly, in order to avoid the “differential explosion” problem inherited from the backstepping design, dynamic surface control (DSC) technique is introduced in the process of controller design. The results demonstrate that this scheme guarantees the stability and tracking performance of the closed-loop system. Finally, simulation examples are given to reveal the viability of the proposed method.
Highlights
In recent years, more and more scholars have begun to pay attention to the stability analysis and controller design of nonlinear systems, and many interesting results have been reported [1], [2]
Due to the output-feedback control is more suitable for practical engineering systems [3], significant progress has been made in the design of output-feedback controllers for nonlinear systems, such as uncertain nonlinear systems [4], input-delayed systems with time-varying uncertainties [5], Markovian jump systems [6], and large-scale stochastic nonlinear systems [7]
neural networks (NNs)-based or fuzzy logic systems (FLSs)-based control approaches have been applied to uncertain discrete-time nonlinear systems [11], dynamic parameters adjustment nonlinear systems [12], dynamic uncertainties nonlinear systems [13], strict-feedback nonlinear systems [14]–[16], pure-feedback nonlinear systems [17], [18], switched nonlinear systems [19]–[22], MIMO nonlinear systems [23], [24] and stochastic nonlinear systems [25], [26]
Summary
More and more scholars have begun to pay attention to the stability analysis and controller design of nonlinear systems, and many interesting results have been reported [1], [2]. (iii) The accuracy of fuzzy control is not high enough and oscillation may occur This encourages us to investigate new approximation-based adaptive control approaches for the control of nonlinear systems to solve the above problems. In this context, the idea of multi-dimensional Taylor network (MTN) emerged. Han and Yan [29] studied the problem of adaptive tracking control for SISO uncertain stochastic nonlinear systems based on MTN. For the above-mentioned observations, this paper tries to study the adaptive output-feedback tracking control design problem for a class of nonlinear systems with unmeasurable states, and proposes an output-feedback control scheme based on adaptive MTN. (i) A novel adaptive output feedback control method based on MTN is proposed for a class of nonlinear systems with unmeasurable states. In formula θ TPmn (s), n denotes the input number of MTN, m represents the highest power of the polynomials in the middle layer of MTN, θ T is the weight vector of MTN
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