Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

Orłowska-Kowalska, Teresa; Dybkowski, M.; Szabat, K.

doi:10.1109/tie.2009.2036023

Cited by 194 publications

(58 citation statements)

References 24 publications

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“…During last decades lots of adaptation mechanisms (based on NN, fuzzy systems, PID etc.) have been proposed for MRAC [20][21][22][23][24]. The main contribution of this paper is the proposal of a new adaptive approach for current control of the IBC with PFC in wind power generation system.…”

Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Control of Interleaved Boost Converter with Power Factor Correction in Wind Energy Systems

Karık

Yıldız

Kaytez

2017

ijisae

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Power converters are generally utilized to convert the power from the wind sources to match the load demand and grid requirement to improve the dynamic and steady-state characteristics of wind generation systems and to integrate the energy storage system to solve the challenge of the discontinuous character of the renewable energy. In the low-voltage wind energy systems, interleaved boost converters (IBC) are often used to operate high currents in the system. IBCs are extremely sensitive to the constantly changing loading conditions. These situations require a robust control operation which can ensure a sufficient performance of the IBC over a large-scale changing load. Neural networks (NN) have emerged over the years and have found applications in many engineering fields, including control. In this paper, the adaptive control of interleaved boost converter with power factor correction (PFC) is investigated for gridconnected synchronous generator of wind energy system. For this purpose, a model reference adaptive control (MRAC) based on NN is proposed. Analysis results show that the proposed control strategy for the IBCs achieves near unity power factor (PF) and low total harmonic distortion (THD) in a wide operating range.

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Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Control of Interleaved Boost Converter with Power Factor Correction in Wind Energy Systems

Karık

Yıldız

Kaytez

2017

ijisae

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“…A neural network is applied to reconcile load speed and torsional torque to decrease the vibration in a two-inertia system [5]. A method based on a sliding-mode fuzzy controller is also applied to suppress the vibration in a two-inertia system [6]. Saarakkala proposed a model-based two-degrees-of-freedom state-space speed-controller design for a two-mass mechanical system [18] and presented a discrete-time polynomial method for parameter identification of two-mass mechanical loads [19].…”

Section: Introductionmentioning

confidence: 99%

Low-frequency Vibration Suppression Control in a Two-mass System by Using a Torque Feed-forward and Disturbance Torque Observer

Li¹,

Xu²,

Wu³

2016

Journal of Power Electronics

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Given that elastic connection is often used between motor drives and load devices in industrial applications, vibration often occurs at the load side. Vibration suppression is a crucial problem that needs to be addressed to achieve a high-performance servo-control system. Scholars have presented many strategies to suppress vibration. In this study, we propose a method to diminish vibration by using a torque feed-forward and disturbance torque observer. We analyze the system performance and explain the principle of the proposed vibration suppression method based on the transfer functions of the system. The design of controller parameters is another important issue in practical applications. We accordingly provide a succinct outline of the design specifications based on the coefficient diagram method. Furthermore, we build a model under the Simulink environment and conduct experiments to validate the proposed method. Results show that speed and position vibrations are successfully suppressed by the proposed method.

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“…Many neural based techniques have been used to retrieve the speed of induction motors, ranging from open-loop techniques [31] to MRAS (Model Reference Adaptive Systems) using the MLP trained with the Back-Propagation (BPN) algorithm [32]- [37] or sliding-mode neuro-fuzzy speed controller [38]. A classical off-line use of the MLP for speed computation has been adopted in [39].…”

Section: Introductionmentioning

confidence: 99%

Linear Neural Networks applied to Power Converters and AC Electrical Drives

Cirrincione¹,

Cirrincione²,

Pucci³

et al. 2013

REPQJ

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This contribution wants to show some recent applications of neural networks in the field of power electronics, with particular emphasis on the sensorless control of AC drives and examines improvements that can be attained when using linear neural networks. At first it presents some theoretical aspects of linear neural networks, particularly the TLS EXIN neuron. Then it describes some original applications in electrical drives and power quality as follows: 1) least square and neural identification of electrical machines 2) neural sensorless control of AC drives 3)neural enhanced single-phase DG system with APF capability Simulation and experimental results are provided to validate the theories.

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Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

Cited by 194 publications

References 24 publications

A Robust Adaptive Control of Interleaved Boost Converter with Power Factor Correction in Wind Energy Systems

A Robust Adaptive Control of Interleaved Boost Converter with Power Factor Correction in Wind Energy Systems

Low-frequency Vibration Suppression Control in a Two-mass System by Using a Torque Feed-forward and Disturbance Torque Observer

Linear Neural Networks applied to Power Converters and AC Electrical Drives

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