Adaptive neural dynamic surface control of MIMO pure-feedback nonlinear systems with output constraints

Liu, Heqing; Zhang, Tianping; Xia, Xiaonan

doi:10.1016/j.neucom.2018.12.011

Cited by 26 publications

(26 citation statements)

References 28 publications

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“…Recently, many researchers are dedicated to handling the control problem of uncertain nonlinear system by combining the back-stepping technique and universal function approximators, that is, neural networks (NNs), [6][7][8][12][13][14][15][16][17][18] and fuzzy logic systems (FLSs). [19][20][21] However, most of approximation-based back-stepping control techniques focus on strict feedback nonlinear systems [6][7][8][12][13][14][19][20][21] or pure feedback nonlinear systems without the consideration of prescribed performance, [15][16][17][18] and less attention was paid to approximation-based prescribed performance back-stepping control for pure feedback nonlinear systems. In addition, the existing approximation-based back-stepping controllers for pure feedback nonlinear systems remain the problems of approximation errors, "explosion of complexity" and algebraic loop.…”

Section: Discussionmentioning

confidence: 99%

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

Chen

Yang

2020

Adaptive Control & Signal

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Summary In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non‐affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non‐affine nonlinearities with the help of Butterworth low‐pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first‐order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back‐stepping approach. The boundedness and convergence of the closed‐loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.

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Section: Discussionmentioning

confidence: 99%

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

Chen

Yang

2020

Adaptive Control & Signal

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“…where Q n is a function defined as Q n (ē n , u,z n , ω) = k n e n +f n (x n , u,z n ) −α n−1 + ω, (28) wheref n is the nth element off in (13), k n > 0 is the nth positive feedback gain,ē n = [e 1 , . .…”

Section: B Modified Adi Controller Designmentioning

confidence: 99%

“…Nonaffine-in-control nonlinear systems are very common in practical engineering, such as flight vehicles [22], hypersonic vehicles [23], and electromechanical systems [24]. Some of the studies for nonaffine nonlinear systems have utilized the neural network and fuzzy control based methods, such as adaptive neural control [25], [26], adaptive fuzzy control [27], adaptive neural dynamic surface control [28], etc. Nevertheless, the heavy computational burdens resulting from adaptive fuzzy or neural weights are unacceptable in practical implementation [24].…”

Section: Introductionmentioning

confidence: 99%

Approximate Dynamic Inversion for Nonaffine Nonlinear Systems With High-Order Mismatched Disturbances and Actuator Saturation

Hao

Pei

2020

IEEE Access

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In this paper, a novel disturbance-observer-based approximate dynamic inversion (ADI) approach is developed for pure-feedback nonaffine-in-control nonlinear systems (PFNNSs) in the presence of both high-order mismatched disturbances and actuator saturation. Finite-time disturbance observers (FTDOs) are utilized to estimate the disturbances and their derivatives. Then, we rebuild the system with the outputs of FTDOs. Thereafter, ADI is employed to derive the desired virtual and actual control of the nominal system, where no disturbances and saturation are presented. Furthermore, an augmented intermediate subsystem is constructed for the reduced slow subsystem to compensate for the difference between inputs with and without saturation by approximating its inversion. The stability of the closed-loop system is studied using Tikhonov's theorem. The proposed method is applied to a numerical example and a one-link robotic system with a brush DC motor. The simulation results demonstrate the validity of the presented approach. INDEX TERMS High-order mismatched disturbances, actuator saturation, finite-time disturbance observer, approximate dynamic inversion, singular perturbation theory.

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“…The nonaffine‐in‐control nonlinear systems are very common in practical engineering, such as manipulator, flight vehicle, hypersonic vehicle, and electromechanical system . Some of the studies for nonaffine nonlinear systems have utilized the neural network and fuzzy control scheme–based methods, such as adaptive neural control, adaptive fuzzy control, and adaptive neural dynamic surface control . Nevertheless, the heavy computational burdens resulted from adaptive fuzzy or neural weights are unacceptable in practical implementation …”

Section: Introductionmentioning

confidence: 99%

“…18,19 Some of the studies for nonaffine nonlinear systems have utilized the neural network and fuzzy control scheme-based methods, such as adaptive neural control, 20,21 adaptive fuzzy control, 17,22 and adaptive neural dynamic surface control. 23 Nevertheless, the heavy computational burdens resulted from adaptive fuzzy or neural weights are unacceptable in practical implementation. 19 The objective of this paper is to develop a feasible redesign procedure to extend the application scope of a class of existing disturbance-rejection algorithms, so that they can be applied to the pure-feedback nonaffine-in-control nonlinear systems (PFNNSs) with matched and mismatched disturbances.…”

mentioning

confidence: 99%

A novel redesign framework to extend the application scope of a class of disturbance‐rejection algorithms

Hao

Pei

2019

Intl J Robust & Nonlinear

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Summary In this paper, a novel redesign method is developed for a class of disturbance‐rejection algorithms so that they can be applied to pure‐feedback nonaffine‐in‐control nonlinear systems with matched and mismatched disturbances. First, a series of augmented dynamical equations, which evolve faster than the original system, are constructed to establish a boundary‐layer subsystem to derive the virtual and actual inputs for the nominal system. Then, the composite interconnected system is studied in the standard singular perturbed form. In the slow timescale, the reduced slow subsystem (RSS) is transformed into the chain of integrators form in the error coordinate, for which the existing antidisturbance methods can be employed. The tracking performance of the closed‐loop system is approximated by RSS under singular perturbation theory. The proposed redesign method is adopted to three existing disturbance‐rejection algorithms for a pure‐feedback nonaffine‐in‐control numerical example in the presence of matched and mismatched disturbances. The effectiveness is demonstrated by simulation experimental results.

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Adaptive neural dynamic surface control of MIMO pure-feedback nonlinear systems with output constraints

Cited by 26 publications

References 28 publications

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

Approximate Dynamic Inversion for Nonaffine Nonlinear Systems With High-Order Mismatched Disturbances and Actuator Saturation

A novel redesign framework to extend the application scope of a class of disturbance‐rejection algorithms

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