Command-filtered backstepping robust adaptive emotional control of strict-feedback nonlinear systems with mismatched uncertainties

Parsa, Pooya; Akbarzadeh-T, M.-R.; Baghbani, Fahimeh

doi:10.1016/j.ins.2021.07.090

Cited by 17 publications

(6 citation statements)

References 37 publications

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“…(Command filter): 48 Consider the following SOCF with ϕ j ,1 0 = κ j − 1 0 and ϕ j ,2 0 = 0 as its initial conditions…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Estimator and command filtering-based neural network control for flexible-joint robotic manipulators driven by electricity

Wen

2022

International Journal of Advanced Robotic Systems

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The article proposes an estimator and command filtering-based adaptive neural network controller for the electrically driven flexible-joint robotic manipulators with output constraints under the circumstance of matched and mismatched disturbances in system dynamics. The presented method is designed based on electrically driven model of the n-link flexible-joint robotic manipulators, which introduces more uncertainties and increases the dimensionality of the system but is more in line with practical. In view of the properties of fast convergence speed and great estimation performance in radial basis function neural network, radial basis function neural network is used to approximate the internal uncertain dynamic parameters of the system. An observer-based estimator is introduced for estimating the matched and mismatched disturbances in flexible-joint robotic manipulator dynamics. As to the differential explosion problem in backstepping control design, this article utilizes second-order command filters to overcome it. This article also adopts barrier Lyapunov functions for implementing output constraint to consider security issues in practical use. For demonstrating the effectiveness of the proposed controller, numerical simulations on two-link flexible-joint robotic manipulators are conducted. On the basis of the comparisons among estimator and command filtering-based adaptive neural network controller and other advanced controllers, the superiorities of estimator and command filtering-based adaptive neural network controller in several areas are proved.

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“…(Command filter): 48 Consider the following SOCF with ϕ j ,1 0 = κ j − 1 0 and ϕ j ,2 0 = 0 as its initial conditions…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Estimator and command filtering-based neural network control for flexible-joint robotic manipulators driven by electricity

Wen

2022

International Journal of Advanced Robotic Systems

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“…Here, we introduce a distributed command‐filtered backstepping adaptive emotional controller, distributed observer‐based emotional command‐filtered backstepping (DOECFB), to address the cooperative control of uncertain heterogeneous strict‐feedback systems under input saturation. To this end, we extend our previous work [33] to the case of MAS by taking inspiration from the emotional contagion in human societies. We also introduce an adaptive emotional observer to use only local information on systems output and consider input constraints for control design.…”

Section: Introductionmentioning

confidence: 99%

“…Also, all states of an agent and its neighbours should be known for design. In [33], we equip CFBC with adaptive RBENNs and H ∞ -based robust terms for tracking control of strict-feedback non-linear systems so that the controller, CFBRAEC, can concurrently approximate mismatched uncertainties and reduce the effect of the approximation error and external disturbances to an arbitrary level.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based adaptive emotional command‐filtered backstepping for cooperative control of input‐saturated uncertain strict‐feedback multi‐agent systems

Parsa

Akbarzadeh-T

Baghbani

2023

IET Control Theory & Appl

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This paper introduces a distributed observer-based emotional command-filtered backstepping (DOECFB) approach for leader-following cooperative output-feedback control of heterogenous strict-feedback multi-agent systems (MAS) under mismatched uncertainties and input saturation. A novel state observer is designed based on radial-basis emotional neural networks (RBENNs) that approximate uncertainties of model dynamics. To model inter-agent dynamics with less complexity, emotion-inspired approximated dynamics are shared among neighbouring followers, like emotional contagion in a group of people. An auxiliary system is also used to attenuate input saturation's negative effect on the cooperative tracking performance. Also, command filters and compensating signals are applied to avoid the 'explosion of complexity' in the backstepping design. Only local information from other agents is required for the proposed approach to guarantee convergence of the cooperative tracking error to a small region around zero and cooperatively semiglobally uniformly ultimately boundedness of closed-loop signals. Simulation examples on a second-order uncertain MAS and multiple forced-damped pendulums are conducted, and quantitative comparisons verify the effectiveness of DOECFB and the proposed observer.

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“…For nonlinear systems with unknown nonlinearities, fuzzy logic systems and neural networks have been widely used in controller design due to their approximation property (Hua et al, 2019; Li et al, 2022; Parsa et al, 2021; Zhang and Yang, 2020). In fact, the action scope of approximation methods is limited to compact set, so that the relevant research only obtain semi-global results.…”

Section: Introductionmentioning

confidence: 99%

Adaptive output feedback asymptotic tracking for uncertain nonlinear systems with dead-zone input

Duan

Wei

Zhang

et al. 2023

Transactions of the Institute of Measurement and Control

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In this paper, the adaptive output feedback asymptotic tracking problem is investigated for nonlinear systems with dead-zone input and unknown nonlinearities, where only system output is available. First, an observer is constructed to estimate unavailable states. Then, by the observer and barrier Lyapunov function, an adaptive output feedback controller is designed, which compensates the influence of dead-zone input and unknown nonlinearities. Furthermore, based on the proposed controller and tuning function, the global boundedness of all signals in closed-loop system is achieved, and the tracking error can asymptotically converge to zero. Particularly, it should be noted that there is no need to calculate the derivatives of virtual control laws in controller design, thus reducing the computational complexity. Finally, two numerical examples are taken to verify the effectiveness of the designed controller.

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Command-filtered backstepping robust adaptive emotional control of strict-feedback nonlinear systems with mismatched uncertainties

Cited by 17 publications

References 37 publications

Estimator and command filtering-based neural network control for flexible-joint robotic manipulators driven by electricity

Estimator and command filtering-based neural network control for flexible-joint robotic manipulators driven by electricity

Observer‐based adaptive emotional command‐filtered backstepping for cooperative control of input‐saturated uncertain strict‐feedback multi‐agent systems

Adaptive output feedback asymptotic tracking for uncertain nonlinear systems with dead-zone input

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