Data-Driven Adaptive Optimal Control for Flotation Processes with Delayed Feedback and Disturbance

Zhongmei Li, Mengzhe Huang, Weihua Gui, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

Considering the presence of time-delay in actuators and nonvanishing disturbance, a data-driven control technique is developed based on adaptive dynamic programming (ADP) to solve the reagents control problem for flotation processes. First, the reagents control problem is converted into an optimal regulator control problem. Second, a policy iteration (PI) algorithm has been adopted to find desirable adaptive suboptimal controllers. Unlike conventional controllers (PID, MPC) design, the proposed method can achieve the desired control performance by only employing online production data without the complete knowledge of the underlying flotation process dynamics. Specifically, the flotation indexes (tailing grade and concentrate grade) are forced to track the desired values with disturbance rejection and keep reagents consumption to a minimum. The convergence and stability of the proposed data-driven optimal control method are given, and the efficacy is validated in the simulation environment with industrial data.

Original languageEnglish (US)
Article number8894357
Pages (from-to)163138-163149
Number of pages12
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • Adaptive dynamic programming (ADP)
  • disturbance rejection
  • flotation processes
  • inputs time-delay
  • reagents control

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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