Research on Intelligent Control Strategies of Mechanical and Electrical Systems Based on Deep Reinforcement Learning
The application of Deep Reinforcement Learning(DRL)in the field of mechanical and electrical system control aims to overcome the limitations of traditional control methods in dealing with complex tasks and adapting to dynamic environments.Based on this,the mechanical electrical system is modeled in detail,and the DRL algorithm applicable to this system is designed and optimized to establish the architecture of the intelligent control system.The experimental environment is designed to simulate real operating conditions to ensure the quality and accuracy of the data,and the results are explained in depth through data analysis.It is found that DRL shows obvious advantages in improving control accuracy,adaptability and intelligence,especially in handling complex control tasks in dynamic and uncertain environments,however,it also faces challenges such as the differences between experimental and practical application environments,algorithm stability and dependency.
deep reinforcement learningintelligent controlsystem modelingexperimental design
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