Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives

El-Sousy, Fayez F. M.

doi:10.1016/j.asoc.2014.02.027

Cited by 72 publications

(26 citation statements)

References 67 publications

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“…For simulation, set b 2 = 3, b 3 = 15, 2 (x 2 ) = 0.2314x 1 + 0.6918x 2 − 0.6245|x 1 |x 2 + 0.0095|x 2 |x 2 + 0.0214x 3 1 + 0.8, c w = 5, and x(0) = [ ∕6, 0, 0] T . For simulation, set b 2 = 3, b 3 = 15, 2 (x 2 ) = 0.2314x 1 + 0.6918x 2 − 0.6245|x 1 |x 2 + 0.0095|x 2 |x 2 + 0.0214x 3 1 + 0.8, c w = 5, and x(0) = [ ∕6, 0, 0] T .…”

Section: Illustrative Resultsmentioning

confidence: 99%

Composite learning adaptive backstepping control using neural networks with compact supports

Yang

Pratama

2019

Adaptive Control & Signal

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The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, where raised-cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed-loop system is established under a weak excitation condition termed interval excitation.The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised-cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach.

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Section: Illustrative Resultsmentioning

confidence: 99%

Composite learning adaptive backstepping control using neural networks with compact supports

Yang

Pratama

2019

Adaptive Control & Signal

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“…It is not an easy task to determine an appropriate number of fuzzy rules. To overcome this problem, some researchers focused on the learning which consists of both structure learning and parameter learning [21][22][23][24][25][26][27]. The structure learning is responsible for on-line rule generation and the parameter learning is designed to achieve favorable learning performance.…”

Section: Introductionmentioning

confidence: 99%

On-line constructive fuzzy sliding-mode control for voice coil motors

Hsu

Wong

2016

Applied Soft Computing

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“…Permanent magnet synchronous motor (PMSM) has the advantages of compact size, superior power density, high airgap flux density, high torque/inertia ratio, high torque capability, high efficiency, and free maintenance, so it is widely used in high-performance applications such as industrial robot, semiconductor manufacturing equipment, and machine tools [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A New Open Loop Approach for Identifying the Initial Rotor Position of a Permanent Magnet Synchronous Motor

Zhu

Xiong

Liu

et al. 2017

Journal of Control Science and Engineering

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The precision of initial rotor position detection is critical for the start and running performance of permanent magnet synchronous motor (PMSM). This work describes a new open loop approach for identifying the initial position of a PMSM with an incremental encoder, even when a constant load torque is being applied. By giving a testing current with high frequency to the stator winding, the initial rotor position of a PMSM can be detected with reasonable accuracy. The rotor almost does not move during the process of identification. The FFT algorithms are used to remove the phase bias effects in identification. Our approach is quicker and simpler than the conventional approaches.

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Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives

Cited by 72 publications

References 67 publications

Composite learning adaptive backstepping control using neural networks with compact supports

Composite learning adaptive backstepping control using neural networks with compact supports

On-line constructive fuzzy sliding-mode control for voice coil motors

A New Open Loop Approach for Identifying the Initial Rotor Position of a Permanent Magnet Synchronous Motor

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