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

TL;DR

This paper experimentally compares PID, LMPC, and NMPC control algorithms on a quadruple tank system, showing that model predictive controls outperform PID in tracking performance, with LMPC and NMPC performing similarly.

Contribution

It provides a comprehensive experimental comparison of PID, LMPC, and NMPC on a physical quadruple tank system using identified nonlinear models.

Findings

LMPC and NMPC outperform PID in tracking accuracy

LMPC and NMPC have similar performance levels

Model predictive controls better handle time-varying setpoints

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.