Adaptive hybrid control using a recurrent neural network for a linear synchronous motor servo-drive system

Lin, Chih‐Hong; Chou, Wen-Der; Lin, Faa-Jeng

doi:10.1049/ip-cta:20010367

Cited by 42 publications

(16 citation statements)

References 22 publications

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“…Through these we can obtain the improved Elman network learning algorithm below: (3) 0 ( ), 1, 2,..., ;…”

Section: B Improved Elman Neural Network`s Structure and Algorithmmentioning

confidence: 99%

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The Application of Improved Elman Neural Network in the Exchange Rate Time Series

Hua

2010

2010 International Conference on Artificial Intelligence and Computational Intelligence

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In this paper, we select the Elman neural network method to improve because of its good non-linear effect of disturbance elimination, and present a new exchange rate time series prediction method. We point out a new improved Elman neural network model firstly, and then predict the time series of RMB exchange rate against U. S. dollar. Through the forecasting process, we determine the input variables for the network structure, and determine the neural network's critical parameters to forecasting. The results show that the improved Elman network can obtain better results during the forecasting process.

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“…Through these we can obtain the improved Elman network learning algorithm below: (3) 0 ( ), 1, 2,..., ;…”

Section: B Improved Elman Neural Network`s Structure and Algorithmmentioning

confidence: 99%

“…Currently, the most widely types used in neural network are feed-forward neural networks and dynamic recurrent neural networks [3] . Feed-forward neural network is a static mapping; it is not suitable for dynamic mapping.…”

Section: Introductionmentioning

confidence: 99%

The Application of Improved Elman Neural Network in the Exchange Rate Time Series

Hua

2010

2010 International Conference on Artificial Intelligence and Computational Intelligence

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“…If the system dynamics and external disturbance are well known, the optimal control law can be defined as follows [21]:…”

Section: Problem Formulationmentioning

confidence: 99%

Development of robust intelligent tracking control system for uncertain nonlinear systems using H∞ control technique

Peng

2011

Applied Soft Computing

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“…The control law of the control system is designed as [45] u a D u re C u rc (10) where u re is the recurrent Chebyshev NN control, which is the major tracking controller. It is used to imitate an ideal control law.…”

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confidence: 99%

Adaptive recurrent Chebyshev neural network control for PM synchronous motor servo‐drive electric scooter with V‐belt continuously variable transmission

Lin

2014

Adaptive Control & Signal

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Because of unknown nonlinearity and time-varying characteristics of electric scooter with V-belt continuously variable transmission (CVT) driven by permanent magnet synchronous motor (PMSM), its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, an adaptive recurrent Chebyshev neural network (NN) control system is proposed to control for PMSM servo-drive electric scooter with V-belt CVT under lumped nonlinear external disturbances in this study. The adaptive recurrent Chebyshev NN control system consists of a recurrent Chebyshev NN control and a compensated control with estimation law. In addition, the online parameters tuning methodology of the recurrent Chebyshev NN and the estimation law of the compensated controller can be derived by using the Lyapunov stability theorem. Moreover, the two optimal learning rates of the recurrent Chebyshev NN based on a discrete-type Lyapunov function are proposed to guarantee the convergence of tracking error. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.

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Adaptive hybrid control using a recurrent neural network for a linear synchronous motor servo-drive system

Cited by 42 publications

References 22 publications

The Application of Improved Elman Neural Network in the Exchange Rate Time Series

The Application of Improved Elman Neural Network in the Exchange Rate Time Series

Development of robust intelligent tracking control system for uncertain nonlinear systems using H∞ control technique

Adaptive recurrent Chebyshev neural network control for PM synchronous motor servo‐drive electric scooter with V‐belt continuously variable transmission

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