Chattering-free robust adaptive sliding-mode control for switched reluctance motor drive

Namazi, Mohammad Masoud; Rashidi, Amir; Saghaian-Nejad, Sayed Morteza; Ahn, Jin-Woo

doi:10.1109/itec-ap.2016.7513001

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Due to the abovementioned reasons, researchers have conducted extensive researches on motor control approaches such as robust control [7, 8], fuzzy control [9, 10], neural network control [11, 12] and sliding mode control (SMC) [13, 14]. Owing to its suppression to the uncertainty factors and strong robustness to the disturbance, the SMC method has led to significant performance improvement in the PMLSM control [15]. However, the large switching control gain in SMC may lead to chattering problem, which is the most notable disadvantage of SMC [16].…”

Section: Introductionmentioning

confidence: 99%

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Zhao

Wang

Huang

2020

IET Electric Power Applications

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In this study, an intelligent second‐order sliding mode control (SMC) method combining second‐order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high‐performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.

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Section: Introductionmentioning

confidence: 99%

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Zhao

Wang

Huang

2020

IET Electric Power Applications

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“…For example, [31,32] integrate HOSMC with gain adaptation techniques. RASMC, which replaces discontinuous sign function by continuous tanh function with incorporation of ASMC, is introduced in [33,34]. Reference [35] proposed modified robust adaptive control, while the modification occurred in error dynamic definition not in switching function.…”

Section: Introductionmentioning

confidence: 99%

Chattering-Free Robust Adaptive Sliding Mode Speed Control for Switched Reluctance Motor

Namazi¹,

Koofigar²,

Ahn³

2020

Control Theory in Engineering [Working Title]

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This study describes an adaptive sliding mode control (ASMC) for the control of switched reluctance motor (SRM). The main objective is to minimize torque ripples with controller effort smoothness while the system is under perturbation by structured uncertainties, unknown parameters, and external disturbances. The control algorithm employs an adaptive approach to remove the need for prior knowledge within the bound of perturbations. This is suitable for tackling the chattering problem in the sliding motion of ASMC. In order to achieve control effort smoothness and more effective elimination of chattering, the algorithm then incorporates proper modifications in order to build a chattering-free robust adaptive sliding mode control (RASMC) using Lyapunov stability theory. A final advantage of the algorithm is that system stability and error convergence are guaranteed. The effectiveness of the proposed controller in improving robustness and minimizing ripples is demonstrated by numerical simulation. Experimental validation is used to demonstrate the efficiency of the proposed scheme. The results indicate that RASMC provides a superior performance with respect to speed tracking and disturbance rejection over the conventional sliding mode control (CASMC) in the face of uncertainties in model and dynamic loads.

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“…In recent years, soft-computing methods such as artificial neural networks (NN) and fuzzy logic systems (FLS) have been successfully applied to overcome the practical problems met in the implementation of sliding mode controllers [1,2,[6][7][8][9][10][11][12][13][14][15][16][17]. In the application of NN-based controllers to improve conventional SMC drawbacks, few main ideas were considered.…”

Section: Introductionmentioning

confidence: 99%

“…The first one attempts to exploit NN learning capacities for online estimating the equivalent control or modeling errors [1,2,6,11,12], the neural net role's is then to compensate model nonlinear terms and disturbances effects; if this compensation term is sufficiently precise, the switched control, responsible of chattering phenomenon, goes to zero. The second idea tries to online determining the adequate switching control gain, just needed to overcome disturbances effects, for reducing the chattering phenomenon amplitude [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Sliding Mode Control of PUMA 560 Robot Manipulator

Medjebouri

Mehennaoui

2016

JAMRIS

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The classical sliding mode control (SMC) is a robust control scheme widely used for dealing with nonlinear systems uncertainties and disturbances. However, the conventional SMC major drawback in real applications is the chattering phenomenon problem, which involves extremely high control activity due to the switched control input. To overcome this handicap, a pratical design method that combines an adaptive neural network and sliding mode control principles is proposed in this paper. The controller design is divided into two phases. First, the chattering phenomenon is removed by replacing the sign function included in the switched control by a continuous smooth function; basing on Lyapounov stability theorem. Then, an adaptive linear neural network, that has the role of online estimate the equivalent control in the neighborhood of the sliding manifold, is developed when the controlled plant is poorly modeled. Simulation results show clearly the satisfactory chattering free tracking performance of proposed controller when it is applied for the joints angular positions control of a 6-DOF PUMA 560 robot arm.

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Chattering-free robust adaptive sliding-mode control for switched reluctance motor drive

Cited by 6 publications

References 17 publications

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Chattering-Free Robust Adaptive Sliding Mode Speed Control for Switched Reluctance Motor

Adaptive Neuro-Sliding Mode Control of PUMA 560 Robot Manipulator

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