Field-Oriented Control (FOC) is among the most popular control architectures for brushless BLDC motors, employed in several mechatronic applications. Data-driven strategies allow for model-free, optimal tuning of FOC parameters, optimizing a quantitative performance index. While fast, noniterative data-driven techniques, such as Virtual Reference Feedback Tuning (VRFT), are sensitive to the choice of the training experiment and the desired closed-loop behavior. On the other hand, iterative data-driven techniques represent a more robust approach, with less critical experiment design and the ability to account for the presence of nonlinearities. However, commonly used iterative algorithms, such as Bayesian Optimization (BO), are often computationally expensive, and require caution in the selection of the parameters to avoid instabilities in closed-loop experiments. The contribution of this work is to formulate the tuning problem of FOC parameters as a model reference optimization problem suitable to be solved with Set Membership Global Optimization-?. This novel, iterative algorithm allows one for the specification of safety constraints and is computationally more efficient than BO. An extensive experimental analysis on a real setup confirms the effectiveness of the proposed approach, and shows that a safe warm start based on VRFT yields faster convergence to the optimal parameters.
Data-Driven Optimal Tuning of BLDC Motors With Safety Constraints: A Set Membership Approach
Busetto, Riccardo;Lucchini, Alberto;Formentin, Simone;Savaresi, Sergio M.
2023-01-01
Abstract
Field-Oriented Control (FOC) is among the most popular control architectures for brushless BLDC motors, employed in several mechatronic applications. Data-driven strategies allow for model-free, optimal tuning of FOC parameters, optimizing a quantitative performance index. While fast, noniterative data-driven techniques, such as Virtual Reference Feedback Tuning (VRFT), are sensitive to the choice of the training experiment and the desired closed-loop behavior. On the other hand, iterative data-driven techniques represent a more robust approach, with less critical experiment design and the ability to account for the presence of nonlinearities. However, commonly used iterative algorithms, such as Bayesian Optimization (BO), are often computationally expensive, and require caution in the selection of the parameters to avoid instabilities in closed-loop experiments. The contribution of this work is to formulate the tuning problem of FOC parameters as a model reference optimization problem suitable to be solved with Set Membership Global Optimization-?. This novel, iterative algorithm allows one for the specification of safety constraints and is computationally more efficient than BO. An extensive experimental analysis on a real setup confirms the effectiveness of the proposed approach, and shows that a safe warm start based on VRFT yields faster convergence to the optimal parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
