www.tjprc.org editor@tjprc.org MODEL ORDER REDUCTION AND LINEAR QUADRATIC REGULATOR CONTROLLER DESIGN, ON A LARGE SCALE LINEAR TIME INVARIANT SYSTEM N. V. A. RAVIKUMAR 1 & G. SARASWATHI 2 1 Research Scholar, Department of EEE, JNTUK, Kakinada & Sr.Assistant. Professor, Department of Power Engineering, GMR Institute of Technology, Rajam Andhra Pradesh,India 2 Professor, University College of Engineering, Vizianagaram, JNTUK, Kakinada India ABSTRACT The main objective of this paper, aims to apply model order reduction on a large scale system and design a Linear Quadratic Regulator (LQR) based controller, to analyse the performance indices, in the time and frequency domains. Control aspects of large scale systems (models with very high order) are a major concern, in the field of control systems. The order of the designed controller must be close to the order of the large scale system, or even more in most cases. As the order of the controller increases the control aspects of the system, it becomes even more complex. Evidently, there are many model order reduction techniques, that reduce the order of the higher order system, without losing the predominant characteristics. A linear quadratic regulator based design is an optimization tool, to derive an optimal controller by minimizing the cost function, based on the two weighting matrices Q and R, which weigh the state vector and the system input, respectively. The step, impulse and the frequency responses of the system with LQR controller are simulated in MatLab. In this paper, a single-input-single-output system (SISO) is considered, nevertheless due to the compatibility of LQR controller, with the state space equations, this study may be extended to multi-input- multi-output (MIMO) systems, provided the model order reduction techniques are chosen appropriately. KEYWORDS: Higher Order System, Reduced Order Model, Linear & Quadratic Regulator Received: Nov 02, 2017; Accepted: Nov 22, 2017; Published: Dec 02, 2017; Paper Id.: IJMPERDDEC201761 INTRODUCTION Complex industrial systems like power systems, chemical systems, and smart grid in recent times are large in nature and technically speaking, they are also multi input multi output systems, single or multi-objective systems and have numerous variables waiting for control. Since, the order of the system is very high, this leads to the complexities in their control. Needless to mention, these large scale systems will be controlled by a few or many controllers at a particular level in the hierarchy and the so obtained control actions may be coordinated at a higher level for further action. The controllers used in large scale systems may either operate in coordination or in a conflicting manner. Hence, the design of controllers for such higher order systems tend to become tedious, time consuming and also leads to a steep rise in the economical considerations. In order to effectively reduce these effects encountered by large scale systems, it is advisable to reduce the order of the system and hence design a lower order controller, the order of which is same as the reduced order model, provided the key properties of the higher order system are retained. One of the most important properties employed in model order reduction is to Original Article International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249-6890; ISSN (E): 2249-8001 Vol. 7, Issue 6, Dec 2017, 533-542 © TJPRC Pvt. Ltd.