Methodology for Detecting Model–Plant Mismatches Affecting Model Predictive Control Performance

Abstract

The model quality for a model predictive control (MPC) is critical for the control loop performance. Thus, assessing the effect of model–plant mismatch (MPM) is fundamental for performance assessment and monitoring the MPC. This paper proposes a method for evaluating model quality based on the investigation of closed-loop data and the nominal output sensitivity function, which facilitates the assessment procedure for the actual closed-loop performances. The effectiveness of the proposed method is illustrated by a multivariable case study, considering linear and nonlinear plants.

Perspectives and challenges in performance assessment of model predictive control

“…The complete mathematical description as well as the theorem proof can be found in Botelho et al Equation shows that it is possible to estimate the nominal closed‐loop output from the controller model as well as plant input and output data. Since y 0 is an estimate of the output process in the absence of a model‐plant mismatch or unmeasured disturbance, it could be considered a benchmark for controller‐model output response.…”

Performance Assessment of Predictive Control—A Survey

“…Consecutive approaches try to incorporate a prediction error over a given horizon, e.g., Zhao et al [144] suggested to monitor a multi-step prediction error. Detailed MPC embedded model analysis is suggested in [145,146]. The authors used nominal sensitivity function providing a complete diagnosis of the model, highlighting not only the effect of the model uncertainties in the corresponding system outputs, but also how a single output impacts the other variables.…”

“…• industrial validation of the multivariate MPC performance assessment at para-xylene production and poly-propylene splitter column processes in [163]; • kerosene and naphtha hydrotreating units in [164]; and • model assessment performed on an industrial predictive controller applied to a propylene/propane separation system [165,166], using the methodology proposed by Botelho et al [145,146].…”

Autocovariance-based plant-model mismatch estimation for linear model predictive control

“…subject to constraints (16), (18), and the following additional equality and inequality constraints:…”

“…Limitations include the need for sufficient external excitation and inability to distinguish between mismatches in individual parameters. Botelho et al [16] also worked in the closed loop; they computed the nominal outputs from a plant under no mismatch via the closed loop sensitivity function, and then use e.g. the variance as a benchmark against which to compare plant output variance.…”