A Robust Adaptive Model Reference Impedance Control of a Robotic Manipulator With Actuator Saturation

Arefinia, Elaheh; Talebi, Heidar Ali; Doustmohammadi, Ali

doi:10.1109/tsmc.2017.2759148

Cited by 48 publications

(33 citation statements)

References 32 publications

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“…In [32], an adaptive neural impedance control for uncertain robotic manipulator under input saturation was presented, where an auxiliary dynamic system (ADS) was constructed for compensating the effect of input saturation. Using the same ADS as [32], the tracking control issue of uncertain robotic manipulators under input saturation was resolved under the adaptive neural tracking control scheme [33] and robust adaptive model reference impedance control scheme [34]. Different from the ADS in [32], for the tracking control problem of mechanical systems, [35] adopted the auxiliary filter, whose input is the non-executable part of actuator, to eliminate the input saturation effect.…”

Section: Introductionmentioning

confidence: 99%

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Wang¹,

Kuang²

2021

IEEE Access

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This paper investigates the control issue of Euler-Lagrange systems (ELSs) subject to dynamic uncertainties and external disturbances under input and output constraints, and develops two neuroadaptive control schemes, i.e., direct neuroadaptive approximation method and indirect neuroadaptive approximation method. In the control design, a smooth saturation function called Gaussian error function is used to replace the saturation model, which is applied to solve the input saturation issue. Moreover, a new function is used to guarantee that the output does not violate the restricted boundary. The uncertain dynamic of the ELSs is reconstructed by the direct or indirect neuroadaptive method, and then a virtual-parameter learning method is proposed to reduce the computational load of control schemes. With the aid of Lyapunov stability theory, it is proven that all signals in the closed-loop control system are bounded and the tracking error of ELSs converges to zero under the proposed neuroadaptive control control schemes. The simulations on a robotic manipulator illuminate the effectiveness and preponderance of the developed neuroadaptive control schemes.INDEX TERMS Euler-Lagrange system, dynamic uncertainty, input and output constraints, neuroadaptive control.

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Section: Introductionmentioning

confidence: 99%

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Wang¹,

Kuang²

2021

IEEE Access

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“…For uncertain ELSs under input saturation, [38] proposed an adaptive tracking control scheme, where the saturated linear correction term was designed to comply with the imposed input saturation; [39] developed a robust adaptive model reference impedance control scheme, where an auxiliary dynamic system (ADS) was constructed for compensating the effect of input saturation. However, in [38]- [39], the uncertainties must satisfy the parameterized decomposition conditions. Using the same ADS as [39], [40] developed an adaptive neural impedance control scheme.…”

Section: Introductionmentioning

confidence: 99%

“…However, in [38]- [39], the uncertainties must satisfy the parameterized decomposition conditions. Using the same ADS as [39], [40] developed an adaptive neural impedance control scheme. Note that the schemes developed in [38]- [40] can not ensure the FT stability of system.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Finite-Time Tracking Control for Uncertain Euler-Lagrange Systems With Input Saturation

et al. 2020

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In this paper, a robust adaptive finite-time (FT) tracking control scheme is proposed for Euler-Lagrange systems (ELSs) subject to nonparametric uncertainties, unknown disturbances and input saturation. In the design procedure, a Gaussian error function is utilized to approximate the input saturation nonlinearity. Following that, by employing the natural property that the upper bound of model parameters uncertainties is linear-in-parameters, the lumped uncertain term that caused by uncertain model parameters and external disturbances is formulated by a linear-parametric form with a single parameter. And then, a novel robust adaptive tracking control law is designed to resolve the tracking control problem of uncertain ELSs. The proposed control scheme is featured by FT convergence rate, and robustness against uncertainties and unknown disturbances. Furthermore, the robust adaptive FT tracking control scheme is insensitive to the character of the uncertainties, and is with low computational burden and easy to implement in engineering applications. And its rigorous stability is analyzed with the aid of the Lyapunov stability theory, and its effectiveness is verified by simulation results and comparison.

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“…Adaptive control is capable of regulating the parameters of the controller in real-time to adapt to the uncertainty of parameter perturbation, modeless dynamics, or external disturbance of the control object. In [10], a new robust adaptive model reference impedance controller is developed for nonlinear robot manipulator with parameter uncertainties. In [11], a novel bio-inspired dynamics-based adaptive tracking control is proposed for improving the energy efficiency of adaptive active control of vehicle suspension systems, considering the issues simultaneously (e.g., robustness, stability, actuator saturation, and nonlinear benefits).…”

Section: Introductionmentioning

confidence: 99%

Terminal Traction Control of Teleoperation Manipulator With Random Jitter Disturbance Based on Active Disturbance Rejection Sliding Mode Control

Feng³

2020

IEEE Access

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A Robust Adaptive Model Reference Impedance Control of a Robotic Manipulator With Actuator Saturation

Cited by 48 publications

References 32 publications

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Robust Adaptive Finite-Time Tracking Control for Uncertain Euler-Lagrange Systems With Input Saturation

Terminal Traction Control of Teleoperation Manipulator With Random Jitter Disturbance Based on Active Disturbance Rejection Sliding Mode Control

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