锅炉汽轮机系统经验导向单评价Q-learning负荷控制OA

To address the challenges encountered in load control of boiler-turbine systems,such as the complexities in establishing precise mathematical models,asymmetric characteristics of valve constraints,and the limited methods for extracting operational experience data,this paper proposes an experience-guided critic-only Q-learning method for boiler-turbine systems adaptive load tracking control.A constraint transformation function is introduced to map asymmetrically constrained inputs to the median of the control range,effectively addressing the asymmetry issue,while reshaping the performance index function into a form without additional penalty terms.To reduce the online computational load,a lightweight critic-only network Q-learning algorithm is proposed to achieve fast learning of the improved performance index function.By updating strategies from previous episodes,an experience-guided relationship is established among multi-episode datasets online.Subsequently,a novel model for recurrent multi-episode training is formulated,aimed at op-timizing data mining efficiency and expediting algorithmic convergence.The effectiveness and superiority of the proposed control algorithm are verified by simulation on the 160 MW boiler-turbine system.