Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees
| Autor: | Michael Maiworm, Daniel Limon, Rolf Findeisen |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática, Ministerio de Economía y Competitividad (MINECO). España |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Scheme (programming language)
FOS: Computer and information sciences 0209 industrial biotechnology Mathematical optimization Computer Science - Machine Learning Computer science General Chemical Engineering Recursive updates Biomedical Engineering Stability (learning theory) Aerospace Engineering Gaussian processes 02 engineering and technology Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Industrial and Manufacturing Engineering Machine Learning (cs.LG) symbols.namesake 020901 industrial engineering & automation Machine learning 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Predictive control Gaussian process computer.programming_language Mechanical Engineering Online learning Input-to-state stability Process (computing) Constraint satisfaction 621.3 Model predictive control Control and Systems Engineering If and only if symbols 020201 artificial intelligence & image processing computer |
| Popis: | Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process-based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input-to-state stability with respect to the model-plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance. 29 pages, 13 figures, 3 tables, 1 algorithm, revision submitted to International Journal of Robust and Nonlinear Control |
| Databáze: | OpenAIRE |
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