[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Reinforcement learning for batch process control: Review and perspectives
Batch or semi-batch processing is becoming more prevalent in industrial chemical
manufacturing but it has not benefited from advanced control technologies to a same degree …
manufacturing but it has not benefited from advanced control technologies to a same degree …
Fusion of machine learning and MPC under uncertainty: What advances are on the horizon?
This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …
learning and model predictive control under uncertainty. The paper is organized as a …
[HTML][HTML] Experimental and developed DC microgrid energy management integrated with battery energy storage based on multiple dynamic matrix model predictive …
This study presents the energy management and control strategy in the islanded DC
microgrid structure in the presence of renewable energy sources (RES) and battery storage …
microgrid structure in the presence of renewable energy sources (RES) and battery storage …
Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
Performance-oriented model learning for control via multi-objective Bayesian optimization
The closed-loop performance of model-based controllers, such as model predictive control,
largely depends on the quality of their underlying model of system dynamics. Inspired by the …
largely depends on the quality of their underlying model of system dynamics. Inspired by the …
Safety-aware cascade controller tuning using constrained Bayesian optimization
This article presents an automated, model-free, data-driven method for the safe tuning of PID
cascade controller gains based on Bayesian optimization. The optimization objective is …
cascade controller gains based on Bayesian optimization. The optimization objective is …
Bayesian optimization with reference models: A case study in MPC for HVAC central plants
We present a framework for exploiting reference models in Bayesian optimization (BO). Our
approach is motivated by a model predictive control (MPC) tuning application for central …
approach is motivated by a model predictive control (MPC) tuning application for central …
COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information
Many engineering problems involve the optimization of computationally expensive models
for which derivative information is not readily available. The Bayesian optimization (BO) …
for which derivative information is not readily available. The Bayesian optimization (BO) …
Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty
The performance of optimization‐and learning‐based controllers critically depends on the
selection of several tuning parameters that can affect the closed‐loop control performance …
selection of several tuning parameters that can affect the closed‐loop control performance …