Neural Network-based Sliding Mode Control for Permanent Magnet Synchronous Motor

Huang, Kaiqi; Zuo, Shilin; Guangdong, Guangzhou

doi:10.2174/1874129001509010314

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Time-varying delays are added as described by scheme described in Section 6.1 (see Results show that a neural network based on a sliding mode control scheme achieves better control performance compared to the conventional sliding mode control methods. [46][47][48] Besides, in Huang and Zuo 49 and Cibiraj and Varatharajan 50 is demonstrated, the use of artificial neural networks can significantly reduce the effects of chattering phenomena presented by sliding mode methodologies itself.…”

Section: Test 621 Descriptionmentioning

confidence: 99%

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Rios

Alanis

Arana‐Daniel

et al. 2020

Intl J Robust & Nonlinear

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This work presents a neural observer-based controller for uncertain nonlinear discrete-time systems with unknown time-delays. The proposed neural observer does not need previous knowledge of the model about the system under consideration, neither the value of its parameters, delays, nor their explicit estimations. The proposed neural observer is based on a neural network composed of two recurrent high order neural networks (RHONNs) for nonmeasurable state variables, one in a parallel configuration, and for measurable state variables one in a series-parallel configuration. The neural network is trained on-line with an extended Kalman filter algorithm. The proposed RHONN observer provides a mathematical model for the system. Based on such a resulting mathematical model, a control law is designed using discrete-time sliding mode block control. Applicability is presented using real-time results that show the performance of the proposal using a linear induction motor prototype as the selected system; this prototype is under the presence of varying time-delays. A Lyapunov analysis is included to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed RHONN observer-controller scheme for uncertain nonlinear discrete-time systems with unknown delays. K E Y W O R D S discrete-time sliding modes, Lyapunov stability, neural block control, neural state estimation, real-time, time-delay systems 8402

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Section: Test 621 Descriptionmentioning

confidence: 99%

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Rios

Alanis

Arana‐Daniel

et al. 2020

Intl J Robust & Nonlinear

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“…The PUMA 560 manipulator, Figure 2, powered by DC motors is modeled by the following non linear dynamic system [18,19,21], (15) where, denotes actuators torques vector, actuators friction torques, and denote joints angular positions, velocities and accelerations vectors. Symbols and are notation for the n(n-l )/2-vector of velocity products and the n-vector of squared velocities.…”

Section: Robot Manipulator Mathematical Modelmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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