In this paper, a self-regulated double hidden layer output feedback neural network (DHLFNN) is presented to control an active power filter (APF) system as a current controller, which is conducive to the improvement of the response characteristic and power quality. First, a global sliding mode controller is introduced because it is effective in achieving overall robustness during the system response. A new output feedback neural structure that has two hidden layers is proposed to make the parameters adaptively adjust themselves and stabilize to their best values. A higher accuracy and stronger generalization ability can be also obtained by reducing the number of network weights and accelerating the network training speed owing to the strong fitting and presentation ability of two-layer activation functions. Furthermore, the designed feedback loops of the neural network play a significant role in possessing associative memory and rapid system convergence. This proposed double hidden layer output feedback neural based global sliding mode controller is simulated on the model of APF and the results show the excellent static and dynamic properties. Experimental results under three cases and comparisons are provided using a fully digital control system to validate the superior performance of the proposed DHLFNN controller.

Double Hidden Layer Output Feedback Neural Adaptive Global Sliding Mode Control of Active Power Filter

Display Omitted An hybrid approach for vector control of the three-phase induction motor is proposed.The proposed approach combines in a different manner that existing in literature the two techniques: the artificial neuronal network (ANN) and the fuzzy logic (FL).The performances of the proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated by simulations; and performances proofs are further verified by comparative results to others controls in different operating conditions.The results show the feasibility and good performance obtained by the proposed control: improved response time and mostly enhanced the robustness of the system. Furthermore, it reinforces the principle of vector control, procuring a good decoupling between the electromagnetic torque and rotor flux. Nowadays, the microcomputer performs calculations at an incredibly high rate of billions of instructions per second. That represents an exponential increase in the processing speed since the early days of the computer development, eventhough such growth did not show complex reasoning that even the simple biological organisms can make. The artificial intelligence techniques as an attempt to work about those limitations, are a promising alternative.Each intelligent technique has its particular strengths and weaknesses and cannot be universally implemented to any problem. Mixed together, these techniques can improve the solutions quality and allow application to various tasks. It is the reason why the AI is used increasingly in order to solve complex problems in engineering. Where, it is still necessary to make progress in the controller tuning.The idea proposed in this paper is simple and original. It is the result of a study that compared the performances of two controls based on the artificial intelligence techniques: the artificial neural networks and the fuzzy logic. The control proposed in this paper combines in a different manner these two techniques in the form of a hybrid control. The aim is to benefit from performances of each of these techniques, by using them in the same control block at the most suitable place.The performances of the this proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated and compared to those obtained from the controls based on artificial neural networks; fuzzy logic and conventional techniques. The results of simulation show the feasibility and the good performances achieved by the proposed control.

Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic

https://www.econstor.eu/bitstream/10419/243633/1/1671087801.pdf

Optimization and control of water pumping PV systems using fuzzy logic controller

It is important to have accurate knowledge on global solar radiation for optimum design of solar energy conversion systems. However, global solar radiation measurement is very rare in meteorological stations in all around the world. Hence, modeling global solar radiation is an crucial issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. This paper presents a detailed description and analysis of various global solar radiation modeling methods. The efficiency and accuracy of ten models from different functions to estimate daily solar radiation in EMR are investigated. Also an optimized model based on Artificial Neural Network (ANN) method and Angstrom-Prescott model for the estimation of daily global solar radiation are presented. The essence of this paper is to investigate the performance of the ANN model and Angstrom-Prescott model in order to ensure the most feasible solution for estimating daily global solar radiation for Eastern Mediterranean Region (EMR) of Turkey. 11 years solar radiation data from 4 stations are utilized in training and testing of developed ANN model and parametric model which is based on Angstrom-Prescott method. In order to ensure a simple application of the model that most accurately predicts the desired target value, a new graphical user interface is developed with MATLAB GUI.

Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey

In the direct torque control (DTC) of induction motor (IM) drive systems utilizing model predictive torque control (MPTC) in the inner loop and classical proportional integral (PI) control in the outer loop is prone to weaknesses, such as uncertainties, external disturbances, parameter variations, and nonlinear dynamics. A high-performance control system for speed and torque is required to guarantee a smooth and robust control architecture that can withstand such unpredictable effects. In this study, we propose an integral super twisting sliding mode control (ISTSMC) to address the shortcomings of the classical PI used in the outer loop of DTC drive systems. A sliding mode speed observer is also designed and utilized to overcome problems associated with mechanical sensors. The robustness of the proposed control strategy and fast dynamic speed response are compared favorably with a benchmark PI controller and conventional sliding mode control (SMC). The ability of the proposed system to regulate both speed and torque was evaluated through simulations, under various fault perturbations, parameter variations, and load disturbances, conducted in MATLAB/ Simulink. Various performance indices were used to demonstrate the superior performance of the proposed strategy compared to a benchmark PI system and conventional SMC-based MPTC approaches.

/pdf/integral-super-twisting-sliding-mode-based-sensorless-52qie7mkho.pdf

Integral Super Twisting Sliding Mode Based Sensorless Predictive Torque Control of Induction Motor

Direct torque control (DTC) of induction machines presents an acceptable tracking scheme for both electromagnetic torque and stator flux. However, conventional DTC scheme, based on hysteresis comparators and the switching table, suffers from large torque and flux ripples, requiring high switching frequency operation for the voltage source inverter. In this paper, a modified DTC scheme based on fuzzy logic rules is proposed. A fuzzy controller (FC) is designed in order to give the appropriate inverter voltage vector. The FC could judge the deviation degree of the torque and flux errors to select the optimum voltage vector according to the fuzzy logic inference. The effectiveness of the proposed FC-based DTC for induction machine (IM) drive is verified at several operating conditions and highlighted by comparing to the conventional DTC. The obtained results illustrate low switching frequency, considerable ripples mitigation of torque, flux and the stator currents and improving the system performance.

Fuzzy logic-based direct torque control for induction machine drive

The two-phase induction machine (TPIM) is widely used in many light-duty applications were three-phase supply is not readily available also a two phases symmetrical or unsymmetrical can be shift auxiliary winding Therefore, in the indirect field oriented control, the rotor circuit time constant is an important parameter Incorrect estimation of the rotor time constant also leads to incorrect flux angle calculations and can cause significant performance deterioration if no means for compensation or identification is applied The magnetic saturation, the operating temperature and difficulties in using sensors for speed measurement are one of the sources of parameters variations, caused by non-linear nature of the magnetizing curve and the variations of the rotor resistance The classical indirect field-oriented control is highly sensitive to the inductance values decrease when increasing the saturation level To solve this problem, the extended Kalman Filter associated to the two-phase PWM inverter generates a voltage wave form are used to estimate the rotor resistance, the main inductance and the rotor speed The proposed method has shown a good performance in both the transient and steady state operations even in the presence of noise, and also at either variable speed operation in the field-weakening region or in the load variation

http://ieeexplore.ieee.org/iel7/6468782/6490559/06490624.pdf

Application of EKF to parameters estimation for speed sensorless vector control of two-phase induction motor

In addition to simplicity, the DTC of the IM allows a good torque control in steady-state and transient operating conditions. However, high torque ripple is produced, which is reflected in speed estimation responses. It also increases acoustical noise and makes the voltage source inverter operated in high and variable switching frequency, requiring a high sampling frequency. In this paper, a simple effective ANN-based DTC of the induction machine is proposed. Neural networks with a simple architecture are designed and implemented in influential points of the direct torque control block of the three phase induction machine in order to improve its dynamic performance while preserving the DTC structure simplicity as much as possible. In particular, this paper proposes to reduce, on the one hand, the torque ripple and commutation frequency. Neural comparators are used to select the appropriate bandwidth for the torque and flux hysteresis controllers. Their aim is to optimize the ripple level in the developed torque and flux. On the other hand, a neural speed controller is designed in order to improve the system ability to respond rapidly to changes in process variables and mitigate the effects of external perturbations. The performance of the proposed control are tested in simulation and highlighted by comparing to the conventional DTC control based on conventional hysteresis comparators and conventional PI speed controller. The obtained results show the feasibility and good performances of the proposed control.

ANN-based DTC scheme to improve the dynamic performance of an IM drive

The simplicity and the effectiveness are the main merits of the proposed control. The paper presents simulation and experimental results of implementing a simple and an effective ANN-based DTC for the IM drive. Neural networks with a simple architecture are designed and implemented in the influential points of the DTC scheme of the three-phase IM in order to improve its performances while preserving the DTC structure simplicity. In the conventional DTC the band limits of flux and torque hysteresis comparators are defined beforehand and remain fixed whatever the operating regime. In the proposed control, neural controllers can give the appropriate logic outputs for the torque and flux by adjusting online the bandwidth on the basis of their inputs and feedback outputs. Thus, reduce the ripples content of the torque, flux and the stator currents which are basically affected by the width of this band. Hence to improve motor dynamic performance under transient and steady state conditions. The proposed control performances are highlighted by comparing to the conventional DTC control. Simulation and experimental results show the feasibility, the easiness of implementation and qualitative improvement in performances of the proposed control.

K. Bouhoune

Papers

Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic

Fuzzy logic-based direct torque control for induction machine drive

Application of EKF to parameters estimation for speed sensorless vector control of two-phase induction motor

ANN-based DTC scheme to improve the dynamic performance of an IM drive

Simple and Efficient Direct Torque Control of Induction Motor Based on Artificial Neural Networks