Fault Tolerant Control Using Fractional-order Terminal Sliding Mode Control for Robotic Manipulators

Ahmed, Saim; Wang, Haoping; Tian, Yu‐Ping

doi:10.24846/v27i1y201806

Cited by 29 publications

(23 citation statements)

References 24 publications

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“…It is a valuable modelling tool in control science and engineering to model the fractional-order (FO) systems [23,24]. Therefore, in order to enhance the performance of the controller, fractional calculus has been used with notable control techniques such as adaptive SMC [25], intelligent control [24] and TDC [6]. Furthermore, FO based adaptive TSMC schemes are introduced to enhance the system performance in terms of fast convergence and high trajectory tracking with small steady-state error [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fractional High‐order Terminal Sliding Mode Control for Nonlinear Robotic Manipulator under Alternating Loads

Ahmed

Wang

Tian

2020

Asian Journal of Control

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In this study, trajectory tracking of robotic manipulators under varying loads with uncertainties and external disturbances is obtained by proposing model-independent adaptive fractional high-order terminal sliding mode control (AFO-HoTSMC). The proposed AFO-HoTSMC method is composed of an adaptive high-order terminal sliding mode control integrated with fractional-order (FO) control. An adaptive tuning control is utilised to evaluate the uncertain unknown dynamics of the system without relying on the prior knowledge of the upper bounds. FO control and HoTSMC are used to achieve the fast finite-time convergence, chatter-free control inputs, better tracking performance and robustness. The finite-time stability of the overall system is investigated and derived from the Lyapunov stability criterion. Finally, to validate the effectiveness and robustness of the developed control method, comparative simulations with H ∞ -Adaptive control, intelligent PD (iPD), intelligent PID (iPID) and adaptive third-order SMC (ATOSMC) are realized to demonstrate the performance of AFO-HoTSMC.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fractional High‐order Terminal Sliding Mode Control for Nonlinear Robotic Manipulator under Alternating Loads

Ahmed

Wang

Tian

2020

Asian Journal of Control

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“…Obtaining a suitable nominal model is not straightforward [15]. Thus, model-free and robust/adaptive controllers are preferred [16][17][18][19]. During the last two decades, various neuro-fuzzy control approaches have been applied to robust control of many uncertain non-linear systems [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive back‐stepping cancer control using Legendre polynomials

Khorashadizadeh

Kalat

2020

IET Systems Biology

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Here, a model-free controller for cancer treatment is presented. The treatment objective is to find a proper drug dosage that can reduce the population of tumour cells. Recently, some solutions have been proposed according to the control theory. In these approaches, based on the mathematical description of the number of effector cells, tumour cells, and concentration of the interleukin-2 (IL-2), a non-linear controller is designed. Here, based on the back-stepping design procedure and function approximation property of Legendre polynomials, a novel controller for MIMO cancer immunotherapy is presented. In fact, Legendre polynomials play the role of uncertainty estimation and compensation. In comparison with other uncertainty estimators such as neural networks, Legendre polynomials have simpler structure. Thus, the contribution of this study is simplifying the design procedure and reducing the controller computational load in comparison with Neuro-Fuzzy controllers. The resulting closed-loop system is capable of overcoming various uncertainties. Simulation results verify the efficiency of the proposed method in the fast reduction of tumour cells. Moreover, a comparison between the performance of Legendre polynomials and a radial basis functions neural network (RBFN) is presented.

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“…However, the problem was that it faces slow convergence speed and singularity problem . Thus, the non‐singular TSM (NTSM) and nonsingular fast TSM (NFTSM) were developed to obtain fast finite‐time convergence, eliminate the singularity problem, and obtain robustness against uncertainties and load disturbances . In the literature, to obtain the advantages of SMC, several SMC strategies for the application of lower‐limb exoskeleton have been developed .…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Fractional‐Order Terminal Sliding Mode Control for Lower‐Limb Exoskeleton

Ahmed

Wang

Tian

2018

Asian Journal of Control

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This paper introduces a robust adaptive fractional-order non-singular fast terminal sliding mode control (RFO-TSM) for a lower-limb exoskeleton system subject to unknown external disturbances and uncertainties. The referred RFO-TSM is developed in consideration of the advantages of fractional-order and non-singular fast terminal sliding mode control (FONTSM): fractional-order is used to obtain good tracking performance, while the non-singular fast TSM is employed to achieve fast finite-time convergence, non-singularity and reducing chattering phenomenon in control input. In particular, an adaptive scheme is formulated with FONTSM to deal with uncertain dynamics of exoskeleton under unknown external disturbances, which makes the system robust. Moreover, an asymptotical stability analysis of the closed-loop system is validated by Lyapunov proposition, which guarantees the sliding condition. Lastly, the efficacy of the proposed method is verified through numerical simulations in comparison with advanced and classical methods.

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Fault Tolerant Control Using Fractional-order Terminal Sliding Mode Control for Robotic Manipulators

Cited by 29 publications

References 24 publications

Adaptive Fractional High‐order Terminal Sliding Mode Control for Nonlinear Robotic Manipulator under Alternating Loads

Adaptive Fractional High‐order Terminal Sliding Mode Control for Nonlinear Robotic Manipulator under Alternating Loads

Adaptive back‐stepping cancer control using Legendre polynomials

Robust Adaptive Fractional‐Order Terminal Sliding Mode Control for Lower‐Limb Exoskeleton

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