Data-based composite control design with critic intelligence for a wastewater treatment platform
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Data-based composite control design with critic intelligence for a wastewater treatment platform Ding Wang1,2,3
· Mingming Ha4 · Junfei Qiao1,2,3 · Jun Yan5 · Yingbo Xie1,2,3
© Springer Nature B.V. 2020
Abstract In this paper, by integrating neural network approximators, a data-based composite control technique is developed with critic learning implementation and wastewater treatment verification. The iterative adaptive critic framework is established involving dual heuristic dynamic programming (DHP), so as to obtain an intelligent optimal controller. Besides, a steady control input is computed with the help of the neural identifier. Then, by combining the DHP controller and the steady control input, an effective composite control strategy is derived and applied to the proposed wastewater treatment platform. Through conducting experiments, it is observed that the dissolved oxygen concentration and the nitrate level can be maintained at setting points successfully, which results in an intelligent wastewater treatment system. Keywords Adaptive critic · Data-based composite control · Intelligent systems · Optimal feedback · Wastewater treatment
This work was supported in part by Beijing Natural Science Foundation under Grant JQ19013; in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant DGECR-2018-00022; in part by the National Natural Science Foundation of China under Grant 61773373, Grant 61890930-5, and Grant 61533017; in part by the National Key Research and Development Project under Grant 2018YFC190 0800-5; and in part by the Youth Innovation Promotion Association of the Chinese Academy of Sciences. No conflict of interest exits in this manuscript and it has been approved by all authors for publication.
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Ding Wang [email protected]
1
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
3
Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing 100124, China
4
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
5
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
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D. Wang et al.
1 Introduction Under the situation of global environmental degradation, freshwater resources shortage has become a widely serious phenomenon and wastewater recycling has been regarded an effective channel to address this issue (Iratni and Chang 2019; Li et al. 2018; Han et al. 2019). As one of the core attentions for treating wastewater, ensuring effluent quality is actually an optimization problem to a large extent. Hence, there is a great need of developing advanced optimal control techniques during wastewater treatment processes. Nevertheless, due to the complexities of such practical processes, including nonlinearities, uncertainties, and the actuality of unk
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