A Stochastic-Based FPGA Controller for an Induction Motor Drive With Integrated Neural Network Algorithms

Abstract

This paper applies stochastic theory to the design and implementation of field-oriented control of an induction motor drive using a single field-programmable gate array (FPGA) device and integrated neural network (NN) algorithms. Normally, NNs are characterized as heavily parallel calculation algorithms that employ enormous computational resources and are less useful for economical digital hardware implementations. A stochastic NN structure is proposed in this paper for an FPGA implementation of a feedforward NN to estimate the feedback signals in an induction motor drive. The stochastic arithmetic simplifies the computational elements of the NN and significantly reduces the number of logic gates required for the proposed NN estimator. A new stochastic proportional-integral speed controller is also developed with antiwindup functionality. Compared with conventional digital controls for motor drives, the proposed stochastic-based algorithm enhances the arithmetic operations of the FPGA, saves digital resources, and permits the NN algorithms and classical control algorithms to be easily interfaced and implemented on a single low-complexity, inexpensive FPGA. The algorithm has been realized using a single FPGA XC3S400 from Xilinx, Inc. A hardware-in-the-loop (HIL) test platform using a Real Time Digital Simulator is built in the laboratory. The HIL experimental results are provided to verify the proposed FPGA controller.