Aiming at the characteristics of large inertia,time lag and nonlinear parameter change in the fermentation process of cow manure,a temperature control method based on deep deterministic strate-gy gradient to improve Smith fuzzy PID is proposed.Firstly,to address the issue that traditional fuzzy PID cannot effectively control time-delay systems,a Smith predictive fuzzy PID controller is established.Secondly,use the DDPG algorithm to improve the temperature controller and conduct offline training on the designed intelligent agent.Finally,the designed controller is experimentally validated through simula-tion.The results show that the Smith PID controller improved by DDPG can eliminate the influence of time delay on temperature control,reduce overshoot and errors,and avoid system instability caused by dynamic deviation of controlled object parameters over time.
temperature controlSmith estimatorreinforcement learningneural networktime lag system
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