Stability of Gaussian Process Learning Based Output Feedback Model Predictive Control⁎. Issue 20 (2018)

• Record Type: Journal Article
• Authors: Maiworm, Michael; Limon, Daniel; Maria Manzano, Jose; Findeisen, Rolf
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 20(2018)
• Page Start: 455
• Page End: 461
• Publication Date: 2018
• Subjects: predictive control -- Gaussian processes -- learning -- stability -- robust -- output feedback; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.11.047
• Languages: English
• ISSNs: 2405-8963

Abstract

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.