Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective

Bose, B.K.

doi:10.1109/tie.2006.888683

Cited by 427 publications

(172 citation statements)

References 57 publications

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“…Considering the system dynamic equation represented by equation (6), if the DISMC scheme is designed as in equation (20), which consists of equations (21) and (22), and with the adaptation laws shown in equations (23) and (24), the stability of the control system can be guaranteed, that is…”

Section: Dismc Schemementioning

confidence: 99%

Design and implementation of double-integral sliding-mode controller for brushless direct current motor speed control

Chen

Kuo

2017

Advances in Mechanical Engineering

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This article presents a double-integral sliding-mode controller with an adaptive proportional-integral-derivative observer for brushless direct current motor speed controller. First, an integral sliding-mode control is designed based on the motor dynamic model and system uncertainties. Accordingly, a novel double-integral sliding-mode controller is proposed to enhance the steady-state performance by employing the double-integral sliding surface with its inherent integral control feature. In addition, the control gains of the double-integral sliding-mode controller can be online adjusted using an adaptive proportional-integral-derivative observer. Thus, the proposed double-integral sliding-mode controller possesses the merits of integral sliding-mode control, proportional-integral-derivative, and adaptive law. An experimental setup including a digital signal processor is applied to verify the brushless direct current motor control system using different control scheme. The measured results show that satisfactory acceleration/deceleration speed tracking and load disturbance speed regulation characteristics are obtained via conventional proportional-integral-derivative controller and the developed integral sliding-mode control scheme under the various testing cases. Moreover, the maximum tracking error can be further reduced more than 50% by adopting the proposed double-integral sliding-mode controller with adaptive proportional-integral-derivative observer.

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Section: Dismc Schemementioning

confidence: 99%

Design and implementation of double-integral sliding-mode controller for brushless direct current motor speed control

Chen

Kuo

2017

Advances in Mechanical Engineering

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“…Recently, several ANN layouts have been developed to solve different tasks [12], such as: times series prediction, complex dynamical system emulation [13], speech generation, handwritten digit recognition, and image compression, due to their ability to learn from extended time series of historical measurements with acceptable error levels compared to other statistical and physical forecasting models [14]. Currently, ANN employment in forecasting is quite straightforward due to the widespread development of specific software applications [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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Abstract:The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network's main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.

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“…These schemes include the H ∞ method [2], neural network [3], fuzzy logic control [4], evolutionary algorithms [5], and hybrid control system [6], which show robustness and better performance in the control of induction and permanent magnet synchronous motors. However, the classical proportional integral derivative (PID) controller has attracted much attention because of its simplicity and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

Steady-State Integral Proportional Integral Controller for PI Motor Speed Controllers

Hoo

Haris²,

Chung³

et al. 2015

Journal of Power Electronics

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The output of the controller is said to exceed the input limits of the plant being controlled when a control system operates in a non-linear region. This process is called the windup phenomenon. The windup phenomenon is not preferable in the control system because it leads to performance degradation, such as overshoot and system instability. Many anti-windup strategies involve switching, where the integral component differently operates between the linear and the non-linear states. The range of state for the non-overshoot performance is better illustrated by the boundary integral error plane than the proportional-integral (PI) plane in windup inspection. This study proposes a PI controller with a separate closed-loop integral controller and reference value set with respect to the input command and external torque. The PI controller is compared with existing conventional proportional integral, conditional integration, tracking back calculation, and integral state prediction schemes by using ScicosLab simulations. The controller is also experimentally verified on a direct current motor under no-load and loading conditions. The proposed controller shows a promising potential with its ability to eliminate overshoot with short settling time using the decoupling mode in both conditions.

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Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective

Cited by 427 publications

References 57 publications

Design and implementation of double-integral sliding-mode controller for brushless direct current motor speed control

Design and implementation of double-integral sliding-mode controller for brushless direct current motor speed control

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

Steady-State Integral Proportional Integral Controller for PI Motor Speed Controllers

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