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.
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Stability of Gaussian Process Learning Based Output Feedback Model Predictive Control
Semantic Scholar · Engineering · 2018
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.