Intelligent Optimization Design Of ADRC And Its Application In Thermal Control System

As a newly developed control strategy,active disturbance rejection control(ADRC)can meet people's high requirements for control quality with its better adaptability and control effect.It adapts to the development of thermal systems in modern industry which gradually become large-scale and complicated.In recent years,the linearization form of ADRC called linear active disturbance rejection controller(LADRC),has been proposed to broaden the engineering application road of this control strategy.However,the existing controller tuning method depends on the dynamic characteristics of the object and the cut-and-try of individual parameters,which brings inconvenience to parameter tuning.Especially when confronting with the complex control objects,the parameter selection of the controller has a great influence on the final control effect.The above limitation hinders the wide application of LADRC in the actual thermal field to a certain extent.Aiming at the control difficulties of LADRC application in high-order thermal systems with large time-delay and coupling,an intelligent method is designed to optimize the parameters of the controller.In the aspect of intelligent algorithm,this paper makes a further study on the basis of heat transfer search algorithm(HTS)and proposes simultaneous heat transfer search(SHTS)algorithm.Owing to utilizing a 'simultaneous'parallel search method,the search space of heat transfer molecules is expanded.Consequently,the optimization efficiency and accuracy is greatly improved.Thus,an intelligent optimization of LADRC based on SHTS algorithm(SHTS-LADRC)is designed and applied to the specific thermal systems,including main steam temperature system and ball mill coal-pulverizing system.The simulation results of setpoint tracking,disturbance rejection and robustness performance tests indicate that the SHTS-LADRC method designed in this paper can find the optimal configuration of parameters more accurately and effectively according to the actual object and demand.It can also flexibly adapt to the change of working conditions and model perturbation.In the light of the convenience,accuracy and efficiency of the optimization process,the designed method is worthy of popularization value in engineering application.