相关论文: Nonlinear Moving Horizon Estimation and Model Pred…

Even though energy efficient climate control of buildings using model predictive control (MPC) has been widely investigated, most MPC formulations ignore humidity and latent heat. The inclusion of moisture makes the problem considerably…

In this paper, we study a problem of controlling cooling facilities and computational equipments for energy-efficient operations of data centers. Although a plethora of approaches have been proposed in previous literatures, there is a lack…

We propose a moving horizon estimation scheme for estimating the states and time-varying parameters of nonlinear systems. We consider the case where observability of the parameters depends on the excitation of the system and may be absent…

In this paper, we conduct a set of experiments to analyze the limitations of current MBRL-based HVAC control methods, in terms of model uncertainty and controller effectiveness. Using the lessons learned, we develop MB2C, a novel MBRL-based…

In this paper, we consider the problem of controlling power consumption dynamics of residential heating, ventilation and air conditioning (HVAC) units so that they follow the grid-side power specifications. In order to do so, we design a…

Model predictive control has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial…

A stochastic model predictive controller (SMPC) of air conditioning (AC) system is proposed to improve the energy efficiency of electric vehicles (EV). A Markov-chain based velocity predictor is adopted to provide a sense of the future…

In this paper, we design real-time decentralized and distributed control schemes for Heating Ventilation and Air Conditioning (HVAC) systems in energy efficient buildings. The control schemes balance user comfort and energy saving, and are…

A novel centralized model predictive control (MPC) is proposed for comfort and energy management in a residential building. The residential setup used here is equipped with a photovoltaic (PV) solar system and a stationary home battery…

We develop a tracking model predictive control (MPC) scheme for nonlinear systems using the linearized dynamics at the current state as a prediction model. Under reasonable assumptions on the linearized dynamics, we prove that the proposed…

Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints. Nonetheless, in many applications, a major challenge arises, that is finding the optimal solution within a single sampling instant…

This study proposes a machine learning-based Model Predictive Control (MPC) approach for controlling Air Handling Unit (AHU) systems by employing an Internet of Things (IoT) framework. The proposed framework utilizes an Artificial Neural…

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation are vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We…

In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamics by the posterior means of the learned Gaussian processes…

The Predicted Mean Vote (PMV) index is a widely accepted method in the building automation sector because it can precisely estimate indoor thermal comfort levels depending on a variety of environmental parameters. This study suggests an…

An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem…

Making the control of building heating systems more energy efficient is crucial for reducing global energy consumption and greenhouse gas emissions. Traditional rule-based control methods use a static, outdoor temperature-dependent heating…

Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task, requiring the modeling of complex nonlinear relationships among HVAC load, indoor temperatures, and outdoor environments. This…

Model Predictive Control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for buildings make data-driven modelling an attractive alternative in…

This paper considers the on-line implementation of the modulating function method, for parameter and state estimation, for the model of an air-handling unit, the central element of HVAC systems. After recalling the few elements of the…