Methodologies for FPGA Implementation of Finite Control Set Model Predictive Control for Electric Motor Drives

Abstract

Model predictive control is a popular research focus in electric motor control as it allows designers to specify optimization goals and exhibits fast transient response. Availability of faster and more affordable computers makes it possible to implement these algorithms in real-time. Real-time implementation is not without challenges however as these algorithms exhibit high computational complexity. Field-programmable gate arrays are a potential solution to the high computational requirements. However, they can be time-consuming to develop for. In this thesis, we present a methodology that reduces the size and development time of field-programmable gate array based fixed-point model predictive motor controllers using automated numerical analysis, optimization and code generation. The methods can be applied to other domains where model predictive control is used. Here, we demonstrate the benefits of our methodology by using it to build a motor controller at various sampling rates for an interior permanent magnet synchronous motor, tested in simulation at up to 125 kHz. Performance is then evaluated on a physical test bench with sampling rates up to 35 kHz, limited by the inverter. Our results show that the low latency achievable in our design allows for the exclusion of delay compensation common in other implementations and that automated reduction of numerical precision can allow the controller design to be compacted.