Hybrid Stochastic And Fpga Implementation Of Machine Parameters Estimator For Motor Drive Control Application

This paper introduces a new torque and flux estimator based on stochastic arithmetic for induction motor drives. It can be implemented via a single Field Programmable Gate Array (FPGA) device. Compared with current DSP-based torque and flux estimators, the proposed estimator contributes a low cost, high performance and high flexibility solution. The main advantages of FPGA include supporting parallel computation, low cost, re-configurability, and direct hardware implementation; however, its application in motor drives is limited due to the low number of available logic gates on a typical FPGA chip. The stochastic approach presented in this paper provides a solution to this problem. Compared with the conventional FPGA implementation approaches, the stochastic implementation can significantly reduce the number of digital logic gates in the controller design especially in motor control applications involving complicated algorithms. In this paper, the principles of stochastic arithmetic are elucidated as well as its application to the torque and flux estimator for a vector controlled 1 hp induction machine. The simulation and experimental results are also demonstrated.