Improved FOC of Induction Motor with Online Neural Network

Neural Networks, 2006. IJCNN'06. …, 2006

The paper deals with the design of speed and flux state-space controller for induction motor drive. Linear Quadratic Regulator theory is employed. Optimal controller gain matrix is calculated for a set of operating points. The artificial neural network (ANN) is trained to provide gain matrix for any operating point. Proposed control scheme has been extensively tested in simulation. Results show promissing robustness against machine parameters variations. A nonlinear and non-stationary plant control task has been solved with the help of Linear Time-Invariant (LTI) system theory and ANNbased function approximator.

With the improvement in the technology of Microprocessor and Power Electronics, Induction motor drives with digital control have become more popular. Artificial intelligent controller (AIC) could be the best candidate for Induction Motor control. Over the last two decades researchers have been working to apply AIC for induction motor drives. This is because that AIC possesses advantages as compared to the conventional PI, PID and their adaptive versions. The main advantages are that the designs of these controllers do not depend on accurate system mathematical model and their performances are robust. In recent years, scientists and researchers have acquired significant development on various sorts of control theories and methods. Among these control technologies, intelligent control methods, which are generally regarded as the aggregation of Fuzzy Logic Control, Neural Network Control , Genetic Algorithm, and Expert S ystem, have exhibited particular superiorities. The artificial neural network controller introduced to the system for keeping the motor speed to be constant when the load varies. The speed control scheme of vector controlle d induction motor drive involves decoupling of the speed and ref speed into torque and flux producing components. The performance of artificial neural network based controller's is compared with that of the conventional proportional integral controller. The dynamic modeling of Induction motor is done and the performance of the Induction motor drive has been analyzed for constant and variable loads. By using neuro controller the transient response of induction machine has been improved greatly and the dynamic response of the same has been made faster.

Electric Power Systems Research, 1994

In this paper, a high-performance speed control approach using artificial neural networks (ANNs) for the field-oriented induction motor is proposed. By using the proposed approach, the speed of an induction motor can be controlled to follow an arbitrarily selected speed trajectory. In particular, an accurate tracking of the speed can still be obtained when uncertainties in the motor and its load exist. The uncertainties include both the unknown load on the motor and the variation of the motor rotor resistance due to temperature change. Two ANNs have been designed to handle these two types of uncertainties. A series of computer simulations has been performed to evaluate the proposed control system. Simulated results show that the proposed control system can achieve satisfactory performances in the tracking of the speed trajectory and the adaptability to rotor resistance parameter variation.

IEEE Transactions on Industry Applications, 2001

This paper presents an artificial-neural-network-based direct-self-control (ANN-DSC) scheme for an inverter-fed three-phase induction motor. In order to cope with the complex calculations required in direct self control (DSC), the proposed artificial-neural-network (ANN) system employs the individual training strategy with fixed-weight and supervised models. A computer simulation program is developed using Matlab/Simulink together with the Neural Network Toolbox. The simulated results obtained demonstrate the feasibility of ANN-DSC. Compared with the classical digital-signal-processor-based DSC, the proposed ANN-based scheme incurs much shorter execution times and, hence, the errors caused by control time delays are minimized.

2010

The present paper proposes an adaptive structure, completely based on the artificial intelligence concepts, for speed control of an induction motor, without any identification of the motor dynamic. Approach with reference model has been chosen, and a neuro-fuzzy controller assures excellent qualities in terms of tracking, and disturbance rejection with high robustness. A neural adaptive mechanism is synthesized to correct the law generated by the controller to provide a compensation signal. This last, added to the controller output, generate the appropriate adapted law. The effectiveness and feasibility of the structure developed is verified by several simulation tests with different conditions operating.

Induction motors are the workhorses of industries. Indirect vector control scheme has been preferred due to its superior dynamic performance. Since the conventional PI controller has bounded operating limits and poor transient response, a search for an alternative controller arises. Recently, Artificial Neural Network (ANN) is gaining momentum as a controller for non linear systems. Herein an artificial neural network controller has been designed for a vector controlled induction motor drive. The complete drive system is modeled in Matlab / Simulink. The drive results have been analyzed for both steady state and dynamic conditions. The results are presented with the traditional PI controller and the proposed ANN controller. It is evident from the results that the proposed ANN controller gives promising results.

International Journal of Electrical and Computer Engineering (IJECE), 2019

This research aims to propose the optimal control method combined with the neuron network for an induction motor. In the proposed system, the induction motor is a nonlinear object which is controlled at each working point. At these working-points, the state equation of the induction motor is linear, so it is possible to apply the linear quadratic regular algorithm for the induction motor. Therefore, the parameters of the state feedback controller are the functions. The output-input relationships of these functions are set through the neural network. The numerical simulation results show that the quality of the control system of the induction motor is very high: The response speed always follows the desired speed with the short transition time and the small overshoot. Furthermore, the system is robust in the case of changing the load torque, and the parameters of the induction motor are incorrectly defined. 1. INTRODUCTION The induction motor is applied widely in practical industrial [1] because of the low weight per power-unit, high robustness, high reliability, and low cost [2]. There are many methods for controlling the induction motor with the high performance, such as the scalar control [3], the indirect field orientation control [4], the direct torque control [5], and the field orientation control [6, 7]. It is difficult to control the induction motor because of its nonlinearities, so some nonlinear methods have been applied to control the induction motor. For example, the control method is based on a flatness principle [8, 9], but the disadvantage of this method is the need of knowing exactly the parameters of the induction motor. Another method is an accurate linearization method [10, 11], the purpose of this method is to convert the input-output relationship of induction motor into a linear one by separating the nonlinear components in the inner loop. The disadvantage of this method is that if the nonlinear components are removed incorrectly, it will adversely affect the control results and reduce the sustainability of the system. Other nonlinear control methods such as bakstepping [12-14], sliding mode control [15-16], the disadvantage of these methods is that there are harmonic oscillations. A method promises high-quality control and efficiency that is the control method based on Linear Quadratic Regular (LQR) algorithm. The LQR method is used to control a lot of objects in practice as wind power generator [17], converter [18], quad-rotor [19], two-wheels self-balancing mobile robot [20]. The LQR method is built by using a mathematical algorithm to minimize the cost function with weighting factors defined by the designer. The cost-function is often defined as a sum of the deviations of response output and their desired values [21]. To apply the LQR method for the induction motor, the controller is designed for the induction motor at the different working-points. At each different working point, the state feedback matrix of the controller is

International Journal of Engineering …, 2010

This paper presents a new method for the implementation of a direct rotor flux control (DRFOC) of induction motor (IM) drives. It is based on the rotor flux components regulation. In fact, the d and q axis flux components feed a proportional integral (PI) controller. The outputs of which are the targets start voltages (vdsref and vqsref). While, the synchronous speed is depicted at the output of rotor speed controller. In order to accomplish variable speed operation, conventional PI lie controller is commonly used. This controller provides limited good performances over a wide range of operation even under ideal field oriented conditions. An alternate approach is to use the so called neural network controller. The overall investigated system is implemented using dSpace system based on digital signal processor (DSP). Simulation and experimental results have been presented for one kw IM drives to confirm the validity of the proposed algorithms.

Proceedings of World Academy …, 2005

In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed control algorithm is robust to parameter variations and external influences. It also assures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the designed controller of induction motor drives which considered as highly non linear dynamic complex systems and variable characteristics over the operating conditions. Keywords⎯ Induction motor, fuzzy-logic control, neural network control, indirect field oriented control. I. INTRODUCTION n lieu of the advances in power electronics and microprocessors, digitally controlled induction motor drives have become increasingly popular. In many industrial drives advanced digital control strategies for the control of field-oriented induction motor drives with a conventional speed PID controller, have gained the widest acceptance in high performance AC servo systems, if the load changes are small and the operating conditions do not force the system too far away from the linear equilibrium point. However, in certain applications, such as steel wills, paper wills, robotics, machine tools, the drive operates under a wide range of lead change characteristics and the system parameters vary substantially. To overcome this drawback, the control algorithm should include a complicated computation process to eliminate the variations in the load disturbance

2006 IEEE International Power and Energy Conference, 2006

When working close to their nominal operating point, induction motors are highly-efficient. However, at light load, efficiency is greatly reduced if the magnetic flux is maintained at nominal value. It is necessary to adjust the flux according to the load level in order to improve the efficiency. This paper proposes a new approach based on Artificial Neural Network (ANN) to operate the machine at optimum condition. The artificial neural network is used to predict the applied voltage and frequency as the load torque and motor speed changes. The training and test patterns required to develop the ANN model were collected by conducting experiments on a 5 H.P Induction motor. A comparison of the experimental data and the ANN-based simulation results has shown a very good concordance between them.