FPGA Based EKF Estimator for DTC Induction Motor Drives Abstract – A Field Programmable Gate Array based Extended Kalman Filter estimator employed in Direct Torque Control system for Induction Motors is presented in this paper. The implemented algorithm of Extended Kalman Filter estimates the required state space variables of Induction Motor for determining the switching pattern of Voltage Scours Inverter. The implementation on FPGA including functional simulations, as well as the hardware in loop tests is presented. Key words – FPGA; DTC Controller; Sensorless Control, EKF; Induction Motors I. INTRODUCTION Over recent years, several researches have been conducted with the aim of proposing alternative solutions to the Field Oriented Control (FOC) of PWM inverter-fed drives for Induction Motors. The main goal of these studies, in essence, was to reducing the complexity while maintaining the accuracy and effectiveness of the control system. Among those solutions, the Direct Torque and Flux (DTFC) control has gained a wide interest satisfying above mentioned conditions as alternative to the (FOC) control systems [1], [2]. Alongside with the increasing interests in the simplified control strategies there is also a growing demand to minimizing the cost of the hardware of control systems highlighting the importance of sensorless controllers. Several sensorless control strategies to estimate the rotor speed (thereby position) of Induction motors eliminating the needs to use their corresponding mechanical sensors have been also developed. Among these methods, the Extended Kalman Filter appears to be an efficient candidate as a robust online estimation towards random noise environment, [2], [3], [4]. It is evident, by considering the successively improving reliability and performance of digital technologies, that even the most complicated control methods are achievable by means of nowadays technologies. High speed Digital Signal Processors (DSP), for instance, because of their software flexibility and ability to perform very complex calculations, have been of the highest interests for those control systems requiring intensive mathematical computations. However, due to the their inflexible architecture, this kind of DSP have been proven to fail to offer sufficiently short execution time which is vital for stability of the controllers dealing with the rapid systems, i.e. the systems with very small time constants. The Field Programmable Gate Arrays (FPGA) technology has, fortunately, appeared to be the solution for overcoming the above mention problem. This technology, providing a flexible architecture, makes it possible to designate the appropriate duties to be shared by hardware and software facilities so as the main goal of minimizing the overall execution time and/or the resource usage to be achieved. The application of FPGA covers a vast area such as signal processing, mathematical computation, control systems, target tracking, navigation and robotics [5], [6], [7]. The implementation of an FPGA, however, faces a major drawbacks which are the complicated and intensive operations such as multiplication and division that demands high computational resources [6], [8]. An FPGA-based EKF has been implemented in this study to be used in DTC controller for Induction Motor Drives. Unfortunately, the whole DTC controller was not implemented due to the lack of laboratory facilities such as Induction Motor-Load, VSI inverter, so that the application was limited to the implementation of EKF estimators using Xilinx ML506 Evaluation Platform. The obtained results from MATLAB complete DTC simulation with those obtained from FPGA were compared to examine the effectiveness of the EKF estimator thus implemented. Yadollah Sabri , Virginie Fresse Hubert Curien Laboratory UMR CNRS 5516 Jean Monnet University- University de Lyon 18 Rue du Professeur Benoît Lauras 42000 Saint Etienne, France e-mail: yadollah.sabri@gmail.com virginie.fresse@univ-stetienne.fr Rachid Beguenane, Francis Okou Department of Electrical and Computer Engineering of RMCC Stn Forces K7K 7B4 Kingston, Canada Rachid.Beguenane@rmc.ca