Implementation and resource analysis of FPGA-based multiphase motor control architectures

Abstract

Small-scale computer numerical control (CNC) machines often lack the rigidity needed to achieve high precision, thus limiting part quality in comparison to larger CNC machines. Combining advanced feedback control methods with additional sensor feedback is a potential solution to improve quality with smaller CNC machines. This thesis explores the utility of using advanced feedback control methods such as field-oriented control (FOC), cascaded proportional integral derivative (PID) control, and active disturbance rejection with modern digital hardware as a potential framework to close this performance gap. Two multiphase motor control systems are examined. The first system is a central processing unit (CPU)- based system integrated with a custom printed circuit board (PCB). The second uses a Field Programmable Gate Array (FPGA). The FPGA-based system provides an important advantage over the CPU-based system, which is low deterministic computational latency. This thesis also examines the utilization of FPGA resources in multiple control architectures. Increasing parallelization of the feedback control algorithm results in increased FPGA resource utilization, but reduces time delay for computation. Integrating FPGA-based motor control into small-scale CNC machining provides a promising pathway to implement advanced control methods to improve small-scale CNC machining precision.