In this paper, design of current controller for a two quadrant DC motor drive was proposed with the help of model order reduction technique. The calculation of current controller gain with some approximations in the conventional design process is replaced by proposed model order reduction method. The model order reduction technique proposed in this paper gives the better controller gain value for the DC motor drive. The proposed model order reduction method is a mixed method, where the numerator polynomial of reduced order model is obtained by using stability equation method and the denominator polynomial is obtained by using some approximation technique preceded in this paper. The designed controllers responses were simulated with the help of MATLAB to show the validity of the proposed method.
DC motors used in many applications such as steel rolling mills, electric trains and robotic manipulators require current and speed controllers to perform tasks. Major problems in applying a conventional control algorithm in a controller design are the effects of nonlinearity in a DC motor. The nonlinear characteristics such as friction and saturation could degrade the performance of conventional controllers. Many advanced model-based control methods such as variable structure control and model reference adaptive have been developed to reduce these effects. However, the performance of these methods depends on the accuracy of system models and parameters. In this paper current controller of two quadrant DC motor drive is considered. The linear operation of DC motor drive was taken in to account in the design stage of current controller. In conventional design methods, some of simplification processes are considered to design the controller parameter values but where as in this proposed method, the model order reduction technique was introduced for the controller parameter design values.
Both in systems and control engineering and in numerical analysis, a wealth of model order reduction techniques have been developed. Balanced truncation, Krylov subspace methods, proper orthogonal decomposition and other SVD-based methods are just a few classes of methods that have been developed.
The computation of equivalent linear system models of large linear dynamic systems is a topic of considerable practical interest. This interest is motivated by the reduced complexity obtained by reducing the large linear sub-network in a linear (or nonlinear) network. Ideally, linear analysis on these sub-networks is performed by first computing a state space model or equivalent transfer function form, followed by the application suitable analysis method. However, the applicability of this method is limited since typical dynamic systems are represented by very large scale matrices that require specialized large-scale eigen analysis programs and computer resources. To avoid this practical limitation, modelorder reduction methods are widely used in the solution of such systems. The basic idea behind model-order reduction is to replace the original system equations with a much smaller state-space or transfer function dimension. In particular, the identified reduced order model frequency characteristics must approximate those of the full order model.
In the analysis of many systems for which the physical laws are well known, one is frequently confronted with problems arising from the high dimensions of descriptive state model, the famous curse of dimensionality. The reduction of such high order systems (also termed as large scale systems) into low order models is one of the important problems in control and system theory system and is considered important in analysis, synthesis and simulation of practical systems. The exact analysis of high order systems is both tedious and costly.
To overcome the stability problem Hutton & Friedland [1] and Appiah [2] gave different methods, called stability based reduction methods which make use of some stability criterion. Other approaches in this direction include the methods such as Shamash [3] and Gutman, Mannerfelt & Molandor [4] which do not make use of any stability criterion but always lead to the stable reduced order models for stable systems. Bosley and Lees [5] and others have proposed a method of reduction based on the fitting of the time moments of the system and its reduced model but these methods have a serious disadvantage that the reduced order model may be unstable even though the original high order system is stable. Some combined methods are also given for example Shamash [6], Chen, Chang and Han [7] and Wan [8] in which the denominator of the reduced order model is derived by some stability criterion method while the numerator of the reduced model is obtained by some other methods. [9].
In this paper, a new model order reduction method is proposed and its helps in finding the current controller gain value. Simulation results were shows the validity of the proposed method. The proposed model order reduction method is a mixed method, where the numerator polynomial of reduced order model is obtained by using the stability equation method and numerator polynomial is obtained by the method proposed in the paper [10].
The control schematic of a two-quadrant convertercontrolled separately-excited DC motor drive is shown in figure 1. The motor drive shown is a speed controller system. The thyristor bridge converter gets its ac supply through a three phase transformer and fast acting ac contactors. The dc output is fed to the armature of the dc motor. The field is separately excited, and the filed supply can be kept constant or regulated, depending on the need for the field weakening mode of operation. The DC motor has a tachogenerator whose output is utilized for closing the speed l
This content is AI-processed based on open access ArXiv data.
