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Author：

Li, X. (Li, X..) | Wang, D. (Wang, D..) | Zhao, M. (Zhao, M..) | Qiao, J. (Qiao, J..)

Indexed by：

EI Scopus SCIE

Abstract：

Wastewater　treatment　is　important　for　maintaining　a　balanced　urban　ecosystem.　To　ensure　the　success　of　wastewater　treatment,　the　tracking　error　between　the　crucial　variable　concentrations　and　the　set　point　needs　to　be　minimized　as　much　as　possible.　Since　the　multiple　biochemical　reactions　are　involved,　the　wastewater　treatment　system　is　a　nonlinear　system　with　unknown　dynamics.　For　this　class　of　systems,　this　paper　develops　an　online　action　dependent　heuristic　dynamic　programming　(ADHDP)　algorithm　combining　the　temporal　difference　with　λ　[TD(λ)],　which　is　called　ADHDP(λ).　By　introducing　the　TD(λ),　the　future　n-step　information　is　considered　and　the　learning　efficiency　of　the　ADHDP　algorithm　is　improved.　We　not　only　give　the　implementation　process　of　the　ADHDP(λ)　algorithm　based　on　neural　networks,　but　also　prove　the　stability　of　the　algorithm　under　certain　conditions.　Finally,　the　effectiveness　of　the　ADHDP(λ)　algorithm　is　verified　through　two　nonlinear　systems,　including　a　wastewater　treatment　system　and　a　torsional　pendulum　system.　Simulation　results　show　that　the　ADHDP(λ)　algorithm　has　higher　learning　efficiency　compared　to　the　general　ADHDP　algorithm.　©　2024　Elsevier　Ltd

Keyword：

Reinforcement learning Temporal difference with λ Wastewater treatment processes Online control Action dependent heuristic dynamic programming

Author Community：

  • [ 1 ] [Li X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Li X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Li X.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Li X.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 7 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 8 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 9 ] [Zhao M.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 10 ] [Zhao M.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 11 ] [Zhao M.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 12 ] [Zhao M.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 13 ] [Qiao J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 14 ] [Qiao J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 15 ] [Qiao J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 16 ] [Qiao J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China

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Source ：

Engineering Applications of Artificial Intelligence

ISSN： 0952-1976

Year： 2024

Volume： 133

8 . 0 0 0

JCR@2022

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 1

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 8

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