A Stochastic Approach to Digital Control Design and Implementation in Power Electronics

This dissertation uses the theory of stochastic arithmetic as a solution for the FPGA implementation of complex control algorithms for power electronics applications. Compared with the traditional digital implementation, the stochastic approach simplifies the computation involved and saves digital resources. The implementation of stochastic arithmetic is also compatible with modern VLSI design and manufacturing technology and enhances the ability of FPGA devices New anti-windup PI controllers are proposed and implemented in a FPGA device using stochastic arithmetic. The developed designs provide solutions to enhance the computational capability of FPGA and offer several advantages: large dynamic range, easy digital design, minimization of the scale of digital circuits, reconfigurability, and direct hardware implementation, while maintaining the high control performance of traditional anti-windup techniques. A stochastic neural network (NN) structure is also proposed for FPGA implementation. Typically NNs are characterized as highly parallel algorithms that usually occupy enormous digital resources and are restricted to low cost digital hardware devices which do not have enough digital resource. The stochastic arithmetic simplifies the computation of NNs and significantly reduces the number of logic gates required for the proposed the NN estimator. In this work, the proposed stochastic anti-windup PI controller and stochastic neural network theory are applied to design and implement the field-oriented control of an induction motor drive. The controller is implemented on a single field-programmable gate array (FPGA) device with integrated neural network algorithms. The new proposed stochastic PI controllers are also developed as motor speed controllers with anti-windup function. An alternative stochastic NN structure is proposed for an FPGA implementation of a feed-forward NN to estimate the feedback signals in an induction motor drive. Compared with the conventional digital control of motor drives, the proposed stochastic based algorithm has many advantages. It simplifies the arithmetic computations of FPGA and allows the neural network algorithms and classical control