Learning of synchronous pulse patterns for electrical drives

Synchronous optimal pulse patterns are a well-known synchronous modulation scheme usually applied to electrical drives at medium and high speeds. These pulse patterns are typically designed via offline optimisation and rely on simplified models, both in their design phase and during their deployment within the control of electrical drives, causing tracking deviations and suboptimal operation due to model bias. In this paper, a model-free learning approach for designing optimal synchronous pulse patterns is proposed. The learning problem is formulated as a derivative-free black-box optimisation problem, with an optimisation objective function that incorporates desired torque tracking and power loss minimisation criteria. The learning is done in a data-driven fashion via iterative direct interactions with the real-world drive system, where a candidate pulse pattern is evaluated by deploying it on the drive system to measure its steady-state response which is used to evaluate the objective function. Simulative analysis of the proposed learning scheme for a permanent magnet synchronous motor (PMSM) is presented. Experimental results show that learned pulse patterns from a real-world PMSM drive can achieve improved steady-state torque tracking accuracy compared with pulse patterns designed with a state-of-the-art method based on model knowledge.