Fuzzy dynamic surface control for uncertain nonlinear systems under input saturation via truncated adaptation approach

Gao, Shigen; Ning, Bin; Dong, Hairong

doi:10.1016/j.fss.2015.02.013

Cited by 67 publications

(27 citation statements)

References 33 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%

“…In order to evaluate the property of the developed state observer (20) and disturbance observer (27), the following Lyapunov function candidate is considered for the estimation errors of these two observers.…”

Section: Neural State Observer and Disturbance Observer Designmentioning

confidence: 99%

“…Remark 6. Based on the output of RBFNN and BLPF, the coupled design between state observer and disturbance observer is presented as (20), (26), and (27), which can eliminate the algebraic loop problem and solve the control problem of pure feedback nonlinear systems in the presence of unmeasured states, unknown nonlinearities and external disturbances for the first time.…”

Section: Neural State Observer and Disturbance Observer Designmentioning

confidence: 99%

“…Theorem 1. For the unknown pure feedback nonlinear system (1) obeying Assumptions 1-4, the state observer and disturbance observer are designed as (20), (26), and (27), and the adaptive output feedback controller u is presented as (77). Then, with the action of the proposed controller, the following properties always hold:…”

Section: Similar Tomentioning

confidence: 99%

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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%

Section: Neural State Observer and Disturbance Observer Designmentioning

confidence: 99%

Section: Neural State Observer and Disturbance Observer Designmentioning

confidence: 99%

Section: Similar Tomentioning

confidence: 99%

See 2 more Smart Citations

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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“…Then other researchers proposed a dynamic surface control (DSC) method by introducing a first-order filter at each step of the backstepping procedure [21]. Due to the property of DSC method, many researchers presented their control schemes combined with DSC method [22][23][24][25][26][27]. In [24], a new robust output feedback control approach for flexible joint electrically driven robots via the observer-based dynamic surface method was proposed, which only requires position measurement of the system.…”

Section: Introductionmentioning

confidence: 99%

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Wang

2017

Complexity

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This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

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Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

Naderolasli

Hashemi

Shojaei

2020

Adaptive Control & Signal

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Summary An observer‐based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional‐order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable conditions, a fuzzy fractional‐order state‐observer is extended. Applying fault‐tolerant approach in a backstepping design methodology would provide a new fault‐tolerant adaptive fuzzy output‐feedback approach for fractional‐order strict‐feedback systems. This control structure would assure the considered system stability through selection of the appropriate Lyapunov candidate function. Two numerical simulations are run to exhibit the validity herein.

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Fuzzy dynamic surface control for uncertain nonlinear systems under input saturation via truncated adaptation approach

Cited by 67 publications

References 33 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

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

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