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Data Driven Economic Model Predictive Control

Author

Listed:
  • Masoud Kheradmandi

    (Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada)

  • Prashant Mhaskar

    (Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada)

Abstract

This manuscript addresses the problem of data driven model based economic model predictive control (MPC) design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

Suggested Citation

  • Masoud Kheradmandi & Prashant Mhaskar, 2018. "Data Driven Economic Model Predictive Control," Mathematics, MDPI, vol. 6(4), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:4:p:51-:d:139274
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