Title Stability of Gaussian Process Learning Based Output Feedback Model Predictive Control?
Authors Maiworm, Michael , Limón, Daniel , MANZANO CRESPO, JOSÉ MARÍA, Findeisen, Rolf
External publication Si
Means IFAC-PapersOnLine
Scope Conference Paper
Nature Científica
SJR Quartile 3
SJR Impact 0.29800
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056821769&doi=10.1016%2fj.ifacol.2018.11.047&partnerID=40&md5=0e7bbfb5c9186a5b1e8ebd31189a0b0a
Publication date 01/01/2018
ISI 000451092800070
Scopus Id 2-s2.0-85056821769
DOI 10.1016/j.ifacol.2018.11.047
Abstract We present an output feedback nonlinear model predictive control approach that uses a Gaussian process model for prediction. We show nominal stability assuming that the Gaussian process model is able to represent the real process and establish input-to-state stability assuming a bounded error between the real process and the Gaussian model approximation. These results are achieved using a predictive control formulation without terminal region. The approach is illustrated using a continuous stirred-tank reactor benchmark problem. © 2018
Keywords Convergence of numerical methods; Gaussian distribution; Gaussian noise (electronic); Model predictive control; Predictive control systems; Stability; Gaussian Processes; learning; Output feedback; Pr
Universidad Loyola members

Change your preferences Manage cookies