Multi-Objective Optimization of Structural Parameters for Dual-Oscillator Ultrasonic Motors

A dual-resonant ultrasonic motor is designed with an independent dual-oscillator configuration. The performance of this motor depends on both the structural parameters of the dual oscillators and the vibration mode combinations (i.e., vibration mode matching) between the stator and rotor. However, due to the relatively complex theoretical model of the dual-oscillator ultrasonic motor, two key performance indicators—maximum output torque and maximum speed—exhibit a mutually exclusive relationship. This poses significant challenges to the accurate characterization and optimization of the motor’s performance. To address this issue, this study first adopts a back propagation (BP) neural network fitting approach to establish the complex functional relationship between the optimization parameters and performance objectives, thereby achieving a scientific fitting of the motor’s theoretical model. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II)—well-suited for handling mutually exclusive multi-objective optimization problems—is employed to optimize the motor’s performance. Finally, based on the obtained Pareto optimal solution set, the oscillator’s structural parameters that meet the performance requirements are determined.