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Composite Observer-Based Adaptive Dynamic Surface Control for Fractional-Order Nonlinear Systems with Input Saturation
  • Bai Zhiye,
  • Li Shenggang,
  • Liu Heng
Bai Zhiye
Shaanxi Normal University

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Li Shenggang
Shaanxi Normal University
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Liu Heng
Southeast University
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Abstract

This article proposes an adaptive neural output feedback control scheme in combination with state and disturbance observers for uncertain fractional-order nonlinear systems containing unknown external disturbance, input saturation and immeasurable state. The radial basis function neural network (RBFNN) approximation is used to estimate unknown nonlinear function, and a state observer as well as a fractional-order disturbance observer is developed simultaneously by using the approximation output of the RBFNN to estimate immeasurable states and unknown compounded disturbances, respectively. Then, a fractional-order auxiliary system is constructed to compensate the effects caused by the saturated input. In addition, by introducing a dynamic surface control strategy, the tedious analytic computation of time derivatives of virtual control laws in the conventional backstepping method is avoided. The proposed method guarantees that the boundness of all signals in the closed loop system and the tracking errors converge to a small neighbourhood around the origin. Finally, two examples are provided to verify the effectiveness of the proposed control method.
30 Dec 2021Submitted to Mathematical Methods in the Applied Sciences
04 Jan 2022Submission Checks Completed
04 Jan 2022Assigned to Editor
07 Jan 2022Reviewer(s) Assigned
23 Nov 2022Review(s) Completed, Editorial Evaluation Pending
25 Nov 2022Editorial Decision: Revise Major
10 Dec 20221st Revision Received
12 Dec 2022Submission Checks Completed
12 Dec 2022Assigned to Editor
12 Dec 2022Review(s) Completed, Editorial Evaluation Pending
12 Dec 2022Reviewer(s) Assigned
12 Dec 2022Editorial Decision: Accept