An adaptive H∞ controller design for permanent magnet synchronous motor drives

Lee, Tzann Shin; Lin, Chih Hong; Lin, Faa Jeng

doi:10.1016/j.conengprac.2004.04.001

Cited by 41 publications

(24 citation statements)

References 17 publications

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“…If the system starts with initial conditions eð0Þ ¼ 0;wð0Þ ¼ 0;mð0Þ ¼ 0 and rð0Þ ¼ 0; the L 2 tracking performance in Eq. (25) can be rewritten as sup…”

Section: Intelligent Power Controllermentioning

confidence: 99%

“…0; the L 2 tracking performance in Eq. (25) can be achieved for a prescribed attenuation level d. Moreover, if the approximation error e [ L 2 that is R T 0 e 2 dt\1; it implies that lim t!1 e j j ¼ 0 [23]. In the following, the design steps of the intelligent power converter are summarized as follows:…”

Section: Proof Consider a Lyapunov Function In The Following Formmentioning

confidence: 99%

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Design of intelligent power controller for DC–DC converters using CMAC neural network

Hsu

2007

Neural Comput & Applic

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DC-DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC-DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.

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“…If the system starts with initial conditions eð0Þ ¼ 0;wð0Þ ¼ 0;mð0Þ ¼ 0 and rð0Þ ¼ 0; the L 2 tracking performance in Eq. (25) can be rewritten as sup…”

Section: Intelligent Power Controllermentioning

confidence: 99%

Section: Proof Consider a Lyapunov Function In The Following Formmentioning

confidence: 99%

Design of intelligent power controller for DC–DC converters using CMAC neural network

Hsu

2007

Neural Comput & Applic

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“…The permanent-electro magnetic platens are widely used in industrial application in order to block pieces for all machining operations on: milling machines, grinding machines, turning machines, and, more generally speaking, machining centres [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Industrial low cost temperature measurement in permanent electro-magnetic platens

Faranda

Lazzaroni

2013

Measurement

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“…The smaller L 2 -norm means the larger degree of robustness. In the past decade, combining the robust control with adaptive fuzzy control or adaptive neural control, some robust fuzzy-based or neuralnetwork-based adaptive control approaches have been proposed to attenuate the effects of approximation error to a prescribed level [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive recurrent neural network control using a structure adaptation algorithm

Hsu

2007

Neural Comput & Applic

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This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L 2 control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.

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An adaptive H∞ controller design for permanent magnet synchronous motor drives

Cited by 41 publications

References 17 publications

Design of intelligent power controller for DC–DC converters using CMAC neural network

Design of intelligent power controller for DC–DC converters using CMAC neural network

Industrial low cost temperature measurement in permanent electro-magnetic platens

Adaptive recurrent neural network control using a structure adaptation algorithm

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