© Faculty of Mechanical Engineering, Belgrade. All rights reserved FME Transactions (2020) 48, 329-341 329 Received: October 2019, Accepted: February 2020 Correspondence to: Prof. C. Bharatiraja, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology,Tamil Nadu, India E-mail: bharatiraja.c@ktr.srmuniv.ac.in doi:10.5937/fme2002329F Abu Feyo Lecturer Faculty of Electrical and Computer Engineering, Jimma Institute of Technology,Jimma University, Ethiopia Amruth Ramesh Thelkar Assistant Professor Faculty of Electrical and Computer Engineering, Jimma Institute of Technology,Jimma University, Ethiopia C Bharatiraja Associate Professor Department of Electrical and Electronics SRM Institute of Science and Technology, India Visiting Researcher, Engineering Department of Electrical, University of South Africa, South Africa Yusuff Adedayo Associate Professor Department of Electrical, Florida Campus Unisa South Africa, South Africa Reference Design and Comparative Analysis of Model Reference Adaptive Control for Steam Turbine Speed Control This paper presents the mathematical modeling of the steam turbine unit with the fuzzy controller in an isolated operating condition, especially in nuclear power plants. The water level in the steam generator is one of the main causes for the shutdown of the reactor. This problem has been of immense concern during the past years as the steam generator and governor speed control is a highly nonlinear system showing inverse response dynamics. In the present research a simulink model of turbine with fuzzy controller is designed. The results are compared and analyzed with MRAC design technique to determine better methodological solution for the steam turbine speed control and achieved, time to the adaption effectiveness of 0.3 to 2.5% with and without GDB. Improved set point tracking and control with respect to turbine speed are demonstrated for the cases when governor dead-band is present and absent. Keywords Steam turbine, Governor Dead Band (GDB), Model Reference Adaptive Control (MRAC), Lyapunov rule, Fuzzy Logic Control (FLC).. 1. INTRODUCTION The steam turbines may the used for the applications to drive an electric generator or equipment such as boiler feedwater pumps, process pumps, air compressors, paper mills and refrigeration chillers. The Rankine cycle and thermodynamic principles are the fundamental basis for steam turbines, in conventional power generating stations where water is first pumped to elevated pressure, which is medium to high pressure, depending on the type of turbine unit and then most frequently superheated. For the commercial and industrial applications, the pressu- rized steam is expanded to lower pressure in a multistage turbine, then exhausted either to a condenser at vacuum conditions or into an intermediate temperature steam distribution system and condensate to utilized back. The measured stimulus and response samples of turbine or plants, used for system identification, involve building a mathematical model of dynamic system. System identification is a process of acquiring, formatting, processing and identifying mathematical models based on raw data from the real-world system. System identification of an interacting series process for real-time model predictive control is done and many critical challenging problems are occurring due to the non-linear behaviour in most of the nuclear power plant and chemical industries. Therefore, traditional control techniques are needed for most of the chemical process industries. Conical tanks exhibit non-linear behaviour which is suitable for food process industries, concrete mixing industries, hydrometallurgical industries and waste water treatment industries. [1,2]. The industrial processes are complex in nature. It is difficult to develop a closed loop control model. Also, the human operator is often required to provide online adjustment, which makes the process performance greatly dependent upon the experience of the individual operator. It would be extremely useful if some kind of systematic methodology can be developed for the process control model that is suited to this kind of industrial process. There are some variables in conti- nuous DCS (distributed control system) that suffer from several unexpected disturbances during the operation (noise, parameter variation, model uncertainties), so the human supervision (adjustment) is necessary. If the operator has a little experience, the system may be damaged or operated at lower efficiency. One such system is the control of steam turbine speed using PI controller as the main controller for controlling the process variable. The process is exposed to unexpected conditions and the controller fails to maintain the process variable in a satisfied manner and retuning of the controller becomes necessary. In a nuclear power plant, the water level in the steam generator is one of the main causes that shutdown the reactor, this problem has been of great concern for many years as the steam gene- rator and governor speed control is a highly nonlinear system showing inverse response dynamics. The speed governor dead band has significant effect on the dynamic performance of load frequency system[3,4]. To cope with the above listed problems, much research has been devoted with various control tech- niques. Conventional controllers are widely used in industries due to low cost, ease of implementation and tuning. The performance of these conventional contro- llers has been enhanced by tuning various methods viz Zeigler - Nichol’s and simplex method. Even though, but the conventional PID controllers with fixed gain are