Design of intelligent optimal controller for hybrid position/force control of constrained reconfigurable manipulators

Rani, Komal; Kumar, Naveen

doi:10.1007/s12652-022-03797-x

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…An adaptive neural force/position terminal sliding mode control method was proposed by Cao et al 33 for manipulator contact with the environment. A hybrid position/force controller with RBF NNs estimating system uncertainty was presented in Peng et al 34 Rani and Kumar 35 investigated an optimal neural position/force controller for uncertain constrained reconfigurable manipulators.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fixed‐time minimal learning force/position control of uncertain manipulators subject to input saturation

Fang

et al. 2023

Adaptive Control & Signal

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This article solves the fixed-time force/position control problem for constrained manipulators in the presence of input saturation and uncertain dynamics. Under the fixed-time stability theory, a novel fixed-time auxiliary dynamic system (ADS) is first presented to compensate for the effects of input saturation nonlinearity. System uncertainties are estimated by using radial basis function neural networks (RBF NNs) and only need to tune one neural parameter online. In addition, with a fixed-time sliding mode surface and the proposed fixed-time ADS, a novel fixed-time adaptive neural force/position controller is designed which can not only ensure the fixed-time stability of the position tracking error but also enable the manipulator to track the desired force trajectory. By using the Lyapunov method, the boundedness of all signals in the closed-loop system is proved. Finally, the effectiveness of the proposed method is demonstrated by comparative simulation works.

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Section: Introductionmentioning

confidence: 99%

Adaptive fixed‐time minimal learning force/position control of uncertain manipulators subject to input saturation

Fang

et al. 2023

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

Design of intelligent optimal controller for hybrid position/force control of constrained reconfigurable manipulators

Cited by 1 publication

References 28 publications

Adaptive fixed‐time minimal learning force/position control of uncertain manipulators subject to input saturation

Adaptive fixed‐time minimal learning force/position control of uncertain manipulators subject to input saturation

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