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      Event-Triggered Dynamic Surface Control of an Underactuated Autonomous Surface Vehicle for Target Enclosing

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          Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks

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            Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form.

            The dynamic surface control (DSC) technique was developed recently by Swaroop et al. This technique simplified the backstepping design for the control of nonlinear systems in strict-feedback form by overcoming the problem of "explosion of complexity." It was later extended to adaptive backstepping design for nonlinear systems with linearly parameterized uncertainty. In this paper, by incorporating this design technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing method. In addition, a stability analysis is given which shows that our control law can guarantee the uniformly ultimate boundedness of the solution of the closed-loop system, and make the tracking error arbitrarily small.
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              From PID to Active Disturbance Rejection Control

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                Author and article information

                Contributors
                Journal
                IEEE Transactions on Industrial Electronics
                IEEE Trans. Ind. Electron.
                Institute of Electrical and Electronics Engineers (IEEE)
                0278-0046
                1557-9948
                April 2021
                April 2021
                : 68
                : 4
                : 3402-3412
                Article
                10.1109/TIE.2020.2978713
                89aeff9f-83b8-4bba-a28d-1e7b875b5e7e
                © 2021

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

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