## Abstract Model predictive control algorithms achieve offset‐free control objectives by adding integrating disturbances to the process model. The purpose of these additional disturbances is to lump the plant‐model mismatch and/or unmodeled disturbances. Its effectiveness has been proven for parti
Tuning SISO offset-free Model Predictive Control based on ARX models
✍ Scribed by Jakob Kjøbsted Huusom; Niels Kjølstad Poulsen; Sten Bay Jørgensen; John Bagterp Jørgensen
- Book ID
- 118479054
- Publisher
- Elsevier Science
- Year
- 2012
- Tongue
- English
- Weight
- 996 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0959-1524
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
The standard way to achieve offset-free tracking in MPC is to add the disturbance dynamics to the prediction model and then use an observer to estimate the real disturbance. Existing algorithms only consider piecewise constant signals, while in practice it is often desirable to have a wider choice o
Wiener models, consisting of a linear dynamic element followed in series by a static nonlinear element, are considered to be ideal for representing a wide range of nonlinear process behavior. They are relatively simple models requiring little more effort in development than a standard linear model,