Reinforcement Learning Based Data-driven Control for Distributed Parameter Systems
Abstract：Most of practical industrial processes are distributed parameter system (DPS), which are essentially described by a set of complex nonlinear partial differential equations. Due to the infinite-dimensional nature of the DPSs, the direct application of the control theories and methods for lumped parameter systems to them is impossible. Moreover, with the fast developments of science technologies, many industrial processes become more and more complicated due to their large scale and complex manufacturing techniques, equipment and procedures. Therefore, the accurately modeling and identification of these processes are often costly to conduct, or the established models are too complicated to support controller design. To overcome these difficulties, this project aims at studying reinforcement learning methods for data-driven control problem of the DPSs, and establishing its theories for performance and stability analysis. The effectiveness and the practical feasibility of the methods will be verified with computer simulations. Through the research of this project, some novel and effective methods and theories will be provided for control design of DPSs, which are extremely important for the development of data-driven control theories and meaningful in both scientific researches and real engineering applications.
Keywords: Data-driven control; distributed parameter systems; reinforcement learning; partial differential equation
Contact:
LUO Biao
E-mail: biao.luo@ia.ac.cn
The State Key Laboratory of Management and Control for Complex Systems