A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques

Abstract

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

“…The second area of development involves the algorithms applied to the objects. The trends observed in the papers published in the journals and proceedings of conferences are focused on artificial intelligence [4][5][6]. A group of the applications is focused on neural networks [7][8][9][10][11].…”

“…In engineering applications, the model is capable of data representation (often in contrast to algorithmic methods), even if the signals in the processing are incomplete, nonlinear, uncertain, or disturbed (e.g., during measurement). Gradient methods are the most efficient for this purpose (3) and (4). Updates of network values are searched according to the assumption of finding the minimum value of the proposed objective function (5) and (6).…”

“…However, two factors can be consider to be the 'game changers' that have moved neural networks from theory to the implementation. One factor is related to calculating the partial derivative of the cost function according to the weights: the information used in the training process in Equations (3) and (4). The technique of backpropagation has allowed for the collection of an error value at each point of the network [19].…”

Tensor Product Alternatives for Nonlinear Field-Oriented Control of Induction Machines

“…To compensate for the effects of nonlinearity, an additional decoupling algorithm is required, and the control performance depends on how accurately this algorithm works [11,12]. In contrast to scalar control, FOC provides better dynamics and very good properties at both low and high speeds [13]. It is also particularly important that the torque ripple is low over the entire speed range.…”

Performance Analysis of Sensorless Induction Motor Drive using Improved Control Techniques

“…These drives are suffered from loss of stability and reliability at zero and very low frequencies [5]. There are numerous advanced control techniques have been suggested for IM in different topologies in the recent past [6]. Indirect Field oriented control (IFOC) Vector-control drives are preferred over scalar-controlled drives due to higher accuracy and better stability [7].…”