High-Efficiency Energy Consumption Optimization Control of Motors Combined with Improved Reinforcement Learning and Adaptive Variational Bayesian Method

In order to solve the uncertainty problems such as long response time faced by motors in high-efficiency energy consumption optimization control, this paper adopts a control strategy combining improved reinforcement learning (RL) with adaptive variational Bayesian method. Dynamic reward function and multi-strategy training mechanism are introduced, and adaptive variational Bayesian method is used to estimate the model parameters and external disturbances of the system. Finally, based on simulation experiments and actual application cases, motor energy efficiency tests are carried out under different load and environmental conditions. The response time of the joint optimization strategy does not change much, increasing from 2.5 seconds to 3.7 seconds, which is always lower than PID control. The motor energy efficiency optimization control strategy combining improved RL with adaptive variational Bayesian method successfully solves the nonlinear and uncertainty problems in motor control.