Dynamic Modeling, Predictive Control and Performance Monitoring
A Data-driven Subspace Approach
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Beschreibung
A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor.
Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.
Provides the reader with control design methods derived directly from process data Simplifies control design and avoids the modelling errors consequent on parameterization
Autor*in
Biao Huang
Themen in »Dynamic Modeling, Predictive Control and Performance Monitoring«
Benchmarking Control Control Engineering Control Theory Model Predictive Control Monitoring Performance Monitoring Process Control Subspace System Identification model modeling complexity