Model-based control algorithms for the quadruple tank system: An experimental comparison

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Abstract

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad's IMC tuning rules using a transfer function representation of the linearized model. Norms of the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance.
Original languageEnglish
Title of host publicationProceedings of the Foundations of Computer Aided Process Operations / Chemical Process Control
Number of pages6
Publication date2023
Publication statusPublished - 2023
EventFOCAPO / CPC 2023
- Hilton San Antonio Hill Country, San Antonio , United States
Duration: 8 Jan 202312 Jan 2023

Conference

ConferenceFOCAPO / CPC 2023
LocationHilton San Antonio Hill Country
Country/TerritoryUnited States
CitySan Antonio
Period08/01/202312/01/2023

Keywords

  • Quadruple Tank System
  • PID Control
  • Linear MPC
  • Nonlinear MPC
  • SysID
  • Experimental Comparison

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