Output feedback receding horizon regulation via moving horizon estimation and model predictive control

Yizhou Fang, Antonios Armaou

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This manuscript develops an algorithm that fuses Carleman moving horizon estimation (CMHE) and Carleman model predictive control (CMPC) together, to design an output feedback receding horizon controller. CMHE identifies the system states as the initial condition for CMPC to make optimal control decisions. The control decisions made by CMPC update the dynamic models used in CMHE to make more precise estimations. Modeling the nonlinear system with Carleman approximation, we estimate the system evolution for both CMHE and CMPC analytically. The Gradient vectors and Hessian matrices are then provided to facilitate the optimizations. To further reduce real-time computation, we adapt the advanced-step NMHE and advanced-step NMPC concepts to our CMHE/CMPC pair to develop an asCMHE/asCMPC pair. It pre-estimates the states and pre-designs the manipulated input sequence one step in advance with analytical models, and then it updates the estimation and control decisions almost in the real-time with pre-calculated analytical sensitivities. A nonlinear CSTR is studied as the illustration example. With CMHE/CMPC pair, the computational time is decreased to one order of magnitude smaller than standard nonlinear MHE and nonlinear MPC. With asCMHE/asCMPC pair, the real-time estimation and control decisions takes a negligible amount of wall-clock time.

Original languageEnglish (US)
Pages (from-to)114-127
Number of pages14
JournalJournal of Process Control
Volume69
DOIs
StatePublished - Sep 1 2018

Fingerprint

Model predictive control
Model Predictive Control
Output Feedback
Horizon
Feedback
Real-time
Update
Gradient vector
Hessian matrix
Evolution System
Electric fuses
Estimate
Analytical Model
Nonlinear systems
Clocks
Analytical models
Dynamic models
Dynamic Model
Optimal Control
Initial conditions

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

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title = "Output feedback receding horizon regulation via moving horizon estimation and model predictive control",
abstract = "This manuscript develops an algorithm that fuses Carleman moving horizon estimation (CMHE) and Carleman model predictive control (CMPC) together, to design an output feedback receding horizon controller. CMHE identifies the system states as the initial condition for CMPC to make optimal control decisions. The control decisions made by CMPC update the dynamic models used in CMHE to make more precise estimations. Modeling the nonlinear system with Carleman approximation, we estimate the system evolution for both CMHE and CMPC analytically. The Gradient vectors and Hessian matrices are then provided to facilitate the optimizations. To further reduce real-time computation, we adapt the advanced-step NMHE and advanced-step NMPC concepts to our CMHE/CMPC pair to develop an asCMHE/asCMPC pair. It pre-estimates the states and pre-designs the manipulated input sequence one step in advance with analytical models, and then it updates the estimation and control decisions almost in the real-time with pre-calculated analytical sensitivities. A nonlinear CSTR is studied as the illustration example. With CMHE/CMPC pair, the computational time is decreased to one order of magnitude smaller than standard nonlinear MHE and nonlinear MPC. With asCMHE/asCMPC pair, the real-time estimation and control decisions takes a negligible amount of wall-clock time.",
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Output feedback receding horizon regulation via moving horizon estimation and model predictive control. / Fang, Yizhou; Armaou, Antonios.

In: Journal of Process Control, Vol. 69, 01.09.2018, p. 114-127.

Research output: Contribution to journalArticle

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AB - This manuscript develops an algorithm that fuses Carleman moving horizon estimation (CMHE) and Carleman model predictive control (CMPC) together, to design an output feedback receding horizon controller. CMHE identifies the system states as the initial condition for CMPC to make optimal control decisions. The control decisions made by CMPC update the dynamic models used in CMHE to make more precise estimations. Modeling the nonlinear system with Carleman approximation, we estimate the system evolution for both CMHE and CMPC analytically. The Gradient vectors and Hessian matrices are then provided to facilitate the optimizations. To further reduce real-time computation, we adapt the advanced-step NMHE and advanced-step NMPC concepts to our CMHE/CMPC pair to develop an asCMHE/asCMPC pair. It pre-estimates the states and pre-designs the manipulated input sequence one step in advance with analytical models, and then it updates the estimation and control decisions almost in the real-time with pre-calculated analytical sensitivities. A nonlinear CSTR is studied as the illustration example. With CMHE/CMPC pair, the computational time is decreased to one order of magnitude smaller than standard nonlinear MHE and nonlinear MPC. With asCMHE/asCMPC pair, the real-time estimation and control decisions takes a negligible amount of wall-clock time.

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