Adaptive backstepping control using recurrent neural network for linear induction motor drive

Lin, Faa-Jeng; Wai, Rong‐Jong; Chou, Wen-Der; Hsu, S.-P.

doi:10.1109/41.982257

Cited by 100 publications

(10 citation statements)

References 26 publications

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“…Linear induction motor (LIM), developed Linear induction motor (LIM), developed to achieving linear propulsion, has many advantages such as simple structure, alleviation of gear between motor and the motion devices, reduction of mechanical losses and the size of motion devices, high-speed operation, silence and high-starting thrust force [1][2][3][4]. Geometrically, the linear induction motor was inspired from the conventional rotational induction motor structure (RIM).…”

Section: Introductionmentioning

confidence: 99%

A New Speed Control Approach of Linear Induction Motor Based on Robust RST Controller and Model Reference Adaptive System Estimator

Khettache

Abdessemed

2023

IJE

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In this paper, a new model of linear induction motor including the impact of the end-effect on the motor performances is proposed. Moreover, a new strategy of control approach based on the Field-Oriented Control (FOC) technique is suggested and investigated. The proposed approach can provide a robust control strategy and overcome the limitations imposed by FOC technique, which suffers with some drawbacks in linear induction motor (LIM) such as sensitivity to parameter variations and imperfect dynamic tracking performance. In this context, the developed technique combines the benefits provided by the both approaches polynomial (RST) regulator and Model Reference Adaptive System (MRAS) observer, in order to achieve a robust controller by minimizing the external disturbances effects and reducing the influence of parameter variations. Moreover, it is revealed that the proposed conrol approach enables an improved rotor speed response with a reduced number of overshoot values as function of mass variations, where the reccorded maximum overshoot value is 8%. Besides, the devopped controller demonstrates a reducded rise and settling time values under wide applied external force conditions. This confirms that the proposed MRAS-RST technique offers a good dynamic response against the parameter variations. The accuracy and control performance of the proposed technique is checked and validated using Matlab/Simulink environment software tool. Simulation results show the effectiveness of the proposed estimator with improved better robustness for RST controller for different reference tracking and disturbance rejection parameters. These significant results make the proposed approach a promising technique dedicated to the design of high-performance controller, which is highly suitable for industrial and electrical applications.

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Section: Introductionmentioning

confidence: 99%

A New Speed Control Approach of Linear Induction Motor Based on Robust RST Controller and Model Reference Adaptive System Estimator

Khettache

Abdessemed

2023

IJE

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“…Scientific literature about linear induction Motor (LIM) is huge 22‐24 . The feature of LIMs to develop a direct linear motion without any gearbox for the motion transformation (from rotating to linear) has been the key issue for their study 25,26 . The counterpart of this potential advantage is the increase of complexity of the machine model, which presents the so‐called end effects and border effects.…”

Section: Introductionmentioning

confidence: 99%

Dual layer barrier functions based adaptive higher order sliding mode control

Obeid

Laghrouche

2021

Intl J Robust & Nonlinear

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This article presents an adaptive discontinuous higher order sliding mode control (HOSMC) strategy for a disturbed chain of integrators of order n. This strategy employs a barrier function‐based dual layer adaptation and it ensures the convergence of the sliding variable and its (n − 1) first derivatives to zero without requiring any information on the bounds of the disturbances or their derivatives. It consists in applying a barrier function‐based adaptive super‐twisting methodology compensating Lipschitz disturbances with some error, and a growing gain‐based adaptive HOSMC which just needs to counteract this error. Real‐time simulation platform is used to test the efficacy of the proposed strategy to control a linear induction motor considering dynamic end effects.

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“…[5] Generally, according to structures, neural network (NN) can be categorized into two types, i.e., feed-forward neural network (FNN) [1,2,10,13,14] and recurrent neural network (RNN). [6,9,11,[15][16][17][18]20] We know that FNN can only represent static mappings and its approximation performance is easily influenced by training data because the scheme of weights update does not depend on internal network information. However, RNN can memorize the past knowledge in virtue of its delay feedback loops.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control for uncertain discrete-time systems with unknown disturbance based on RNN

Cui

Guo

Feng

et al. 2016

AIR

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A new robust adaptive control algorithm is developed for a class of uncertain discrete-time SISO systems. Different from the existing investigated systems, the concerned discrete system here is with both uncertain smooth nonlinear functions and unknown disturbance. On the basis of the idea of neural network (NN) approximation, a novel recurrent neural network (RNN) is first proposed and used to approximate a backstepping control law following the transformation of the original system into a predictor form. According to Lyapunov stability theorem, a new on-line tuning law for parameters of RNN is obtained. Meanwhile, in order to achieve satisfying robust tracking performance, a novel controller is constructed by virtue of the approximation error of RNN. It has been proved that all the concerned signals are uniformly ultimately bounded. In addition, a very small tracking error can be obtained through appropriate selection of control parameters. Finally, we give a simulation example to demonstrate the validness of the newly proposed control algorithm for the investigated systems.

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Adaptive backstepping control using recurrent neural network for linear induction motor drive

Cited by 100 publications

References 26 publications

A New Speed Control Approach of Linear Induction Motor Based on Robust RST Controller and Model Reference Adaptive System Estimator

A New Speed Control Approach of Linear Induction Motor Based on Robust RST Controller and Model Reference Adaptive System Estimator

Dual layer barrier functions based adaptive higher order sliding mode control

Adaptive control for uncertain discrete-time systems with unknown disturbance based on RNN

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