Adaptive fuzzy tracking control of robot manipulators actuated by permanent magnet synchronous motors

Khorashadizadeh, Saeed; Sadeghijaleh, M.

doi:10.1016/j.compeleceng.2018.09.010

Cited by 39 publications

(28 citation statements)

References 14 publications

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“…Function approximation methods have been explored to overcome uncertainties in robust control of nonlinear systems [17][18][19][20][21][22][23][24]. In [25], an adaptive dynamic sliding-mode control system (ADSMCS) with recurrent radial basis function network (RRBFN) for indirect field-orientation control induction motor (IM) drive which guarantees the robustness in the presence of parameter uncertainties and load disturbances has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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In this work, an adaptive dynamic sliding mode control approach is proposed for robotic systems via uncertainty estimators with exponential convergence rate. The uncertainties are estimated using various uncertainty estimators such as the Fourier series expansion, Legendre polynomials and adaptive fuzzy systems. Also, for each uncertainty estimator, the approximation error is compensated. The adaptation laws are derived using a stability analysis. Moreover, the asymptotic convergence of the tracking error and the boundedness of all closed-loop signals are guaranteed. The novelty of this paper is proposing a positive exponential function for the convergence rate of the adaptation rules to prevent from initial high voltages originated from large initial tracking errors. Another novelty of this paper is presenting a robust control term for the truncation error that improves the accuracy of the control system. Analysis of simulations reveals the effectiveness of the proposed method in terms of fast disturbance rejection and negligible tracking error.

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Section: Introductionmentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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“…Among these estimators, orthogonal polynomials such as Chebyshev, Legendre, Laguerre are very simple mathematical functions with the flexibility to represent very general nonlinear relationships over an interval (Smyth 2014;Kreyszig 2007). From control point of view, the orthogonal polynomials have been applied for estimation of uncertain terms without using the regressor matrix in the chaotic systems (Zirkohi 2019), robotic manipulators (Huang and Chien 2010; Chien and Huang 2012; Ahmadi and Fateh 2018), synchronous motors (Khorashadizadeh and Sadeghijaleh 2018), etc. The uncertainty estimators based on orthogonal polynomials show less computational burden in comparison with the fuzzy and neural estimators (Zirkohi 2019).…”

Section: Introductionmentioning

confidence: 99%

Flatness-Based Decentralized Adaptive Backstepping Control of Cascaded DC–DC Boost Converters Using Legendre Polynomials

Shoja-Majidabad

2020

J Control Autom Electr Syst

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In this paper, decentralized adaptive backstepping control of cascaded DC-DC boost converters is studied in the presence of load and voltage uncertainties and interactions between the converters. First, a cascaded boost converter average model that covers both continuous and discontinuous conduction modes is extracted. Afterward, flatness property is applied to transfer the cascaded system states into a semi-canonical form. Further, a novel decentralized backstepping controller is proposed to track the reference voltages. In this control strategy, Legendre polynomials are utilized to estimate the uncertainties and interactions adaptively. The control method is simple and robust with less computational burden due to the polynomial estimator's application. Various simulations are performed for the cascaded DC-DC boost converters to indicate the effectiveness and performance of the proposed controller under the load and supply voltage changes.

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“…In [10], an observer-based robust control strategy for DC-driven flexible joint robots without speed sensors is proposed. An adaptive fuzzy control strategy for a PMSM-driven rigid-joint robot is proposed in [11], but this article did not make a specific analysis of the controller performance. A global proportion-integral-derivate (PID) control strategy for a PMSM-driven rigid-joint robot and a global stability proof method of PID control are proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking of Flexible-Joint Robots Actuated by PMSM via a Novel Smooth Switching Control Strategy

Wang

et al. 2019

Applied Sciences

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This paper presents the trajectory tracking control of a flexible-joint manipulator driven by permanent magnet synchronous motor (PMSM). Combining the PMSM electrical equation and mechanical equation of robotic manipulators, a novel smooth switching control scheme is proposed. Firstly, the position loop controller of the system is designed, with an improved hierarchical sliding mode control (IHSMC) algorithm proposed to further the response speed of the system, additionally, a robust interconnection and damping assignment passivity-based controller (IDA-PBC) is designed to improve the steady state performance of the system. Then, the IDA-PBC control strategy is leveraged to design the current loop controller of the system, on which basis a hybrid controller with smooth switching is designed. Furthermore, Gaussian function is applied as the smooth switching function of the hybrid controller to promote the switching performance. As a result, the hybrid controller has both good dynamic and steady performance. The simulation results verify the effectiveness of the algorithms.

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Adaptive fuzzy tracking control of robot manipulators actuated by permanent magnet synchronous motors

Cited by 39 publications

References 14 publications

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

Flatness-Based Decentralized Adaptive Backstepping Control of Cascaded DC–DC Boost Converters Using Legendre Polynomials

Trajectory Tracking of Flexible-Joint Robots Actuated by PMSM via a Novel Smooth Switching Control Strategy

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