Maiworm, Michael , Limón, Daniel , MANZANO CRESPO, JOSÉ MARÍA, Findeisen, Rolf

Si

IFAC PAPERSONLINE

Conference Paper

Científica

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✗

0.298

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056821769&doi=10.1016%2fj.ifacol.2018.11.047&partnerID=40&md5=0e7bbfb5c9186a5b1e8ebd31189a0b0a

01/01/2018

000451092800070

2-s2.0-85056821769

10.1016/j.ifacol.2018.11.047

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

Convergence of numerical methods; Gaussian distribution; Gaussian noise (electronic); Model predictive control; Predictive control systems; Stability; Gaussian Processes; learning; Output feedback; Predictive control; robust; Feedback

• JOSÉ MARÍA MANZANO CRESPO