相关论文: Robust Learning Model Predictive Control for Perio…
Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a…
Learning-based control methods typically assume stationary system dynamics, an assumption often violated in real-world systems due to drift, wear, or changing operating conditions. We study reinforcement learning for control under…
Active building energy management holds potential to reduce global energy-related emissions and support flexible operations of future low-carbon systems. This requires to integrate diverse objectives and engage multiple stakeholders.…
The large-scale integration of intermittent renewable energy resources introduces increased uncertainty and volatility to the supply side of power systems, thereby complicating system operation and control. Recently, data-driven approaches,…
Buildings account for a substantial portion of global energy consumption. Reducing buildings' energy usage primarily involves obtaining data from building systems and environment, which are instrumental in assessing and optimizing the…
Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized…
The residential electrical energy scheduling of solar Photovoltaics (PV) is an important research area of the modern green buildings. On the demand side, factors such as building load, and the renewable PV energy resources are integrated…
Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into electricity prosumers. Existing studies have developed some advanced building side controls that enable renewable energy…
The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore…
Large-scale integration of intermittent renewable energy sources calls for substantial demand side flexibility. Given that the built environment accounts for approximately 40% of total energy consumption in EU, unlocking its flexibility is…
Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM)…
This paper discusses learning a structured feedback control to obtain sufficient robustness to exogenous inputs for linear dynamic systems with unknown state matrix. The structural constraint on the controller is necessary for many…
This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach refines the parameter estimates…
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. This importance is magnified during anomalous periods, such as…
Energy consumption in residential and commercial buildings has increased dramatically worldwide in the last decade, due to the constant population and economic growth, the proliferation of electronic and consumer appliances. This has…
Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst-case…
This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages, and renewables. The overall goal is to provide a comprehensive and…
This paper studies the robust optimal control design for uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (robust-ADP). The objective is to fill up a gap in the past literature of ADP where dynamic…
Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called {\it controllers}. However, the real systems represented by networks contain unreliable components and modern robust…
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is…