Frequency matching optimization model of ultrasonic scalpel transducer based on neural network and reinforcement learning

Aiming at the problem that the excitation frequency and resonant frequency of the transducer cannot keep synchronous, the output amplitude decreases and the vibration is unstable. In this study, the working principle of piezoelectric transducers is firstly analyzed by the equivalent circuit method and the instantaneous characteristic variables (installing preload, assembly preload, load, and other factors) that affect the frequency matching obtained to establish the equivalent relationship with the resonant frequency. Secondly, in order to make the synchronization between excitation frequency and resonance frequency, the key instantaneous characteristic variables are extracted based on Pearson correlation coefficient. Thirdly, the mathematical model of the mapping relationships between instantaneous characteristic variables and resonance frequency is established with the radial basis function neural network (RBFNN). Fourthly, for the purpose of the adaptation to the characteristics of dynamic load and real-time frequency modulation in the operation of ultrasonic scalpels, the reinforcement learning (Q-learning algorithm) and the weight vector of RBFNN are used to define the eligibility trace, which is used to dynamically adjust the RBFNN frequency matching optimization model in real time and maintain the “constant” amplitude output and stable harmonic response process. Finally, the experimental results show that, compared with the traditional methods, the frequency matching optimization model of ultrasonic scalpel transducer based on RBF neural network and Q-Learning reinforcement learning is effective, and the vibration amplitude of the transducer is increased by . The amplitude fluctuation is stable at . It can provide decision-making guidance for relevant engineering fields.