[Objective] Aiming to the problem of design weighting factor in model predictive control

non-dominated sorting genetic algorithm II (NSGA-II) and Bayesian optimization algorithm are used to design weighting factors in this paper. [Methods] Based on model predictive torque control (MPTC) for permanent magnet synchronous motor (PMSM)

NSGA-II and Bayesian optimization algorithm were used to design weighting factors in two scenarios

which were without/with considering switching frequency control

respectively. When without considering switching frequency control

only one weighting factor needed to be designed

when with considering switching frequency control

two weighting factors needed to be designed. And based on the weighting factors designed by two algorithms

a comparison of the two algorithms in terms of control performance

execution time and memory occupancy was carried out. [Results] The results showed that both weighting factors design algorithms were feasible for PMSM MPTC system with/without considering switching frequency control. The weight factors obtained by NSGA-II that minimize the fitness function value were essentially equivalent to the optimal weight factors derived from the Bayesian optimization algorithm

and control performances were basically equivalent. The control performance of Bayesian optimization algorithm was relatively superior. [Conclusion] NSGA-II can provide a set of Pareto optimal solutions suitable for diverse application scenarios. Characterized by high computational complexity

extended processing times

and substantial memory requirements

it is well-suited for dynamically evolving operational environments. And the Bayesian optimization algorithm is easy to implement and doesn’t need too much resource

has better optimization effect and higher optimization efficiency in complex scenarios involving multiple control objectives.