Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes

This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work’s novelty lies on integrating derivative-free optimization schemes and multi-fidelity Gaussian processes within a Bayesian optimization framework. The proposed scheme uses two Gaussian processes for the stochastic system, one emulates the (known) process model, and another, the true system though measurements. In …