A New Approach to Off-Line Robust Model Predictive Control for Polytopic Uncertain Models

Ma, Xianghua; Bao, Hanqiu; Zhang, Ning

doi:10.3390/designs2030031

Designs

2018

DOI: 10.3390/designs2030031

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A New Approach to Off-Line Robust Model Predictive Control for Polytopic Uncertain Models

Xianghua Ma

Hanqiu Bao

Ning Zhang

Abstract: Concerning the robust model predictive control (MPC) for constrained systems with polytopic model characterization, some approaches have already been given in the literature. One famous approach is an off-line MPC, which off-line finds a state-feedback law sequence with corresponding ellipsoidal domains of attraction. Originally, each law in the sequence was calculated by fixing the infinite horizon control moves as a single state feedback law. This paper optimizes the feedback law in the larger ellipsoid, for… Show more

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Cited by 2 publications

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“…To tackle this issue, robust techniques such as robust MPC [108,109] and stochastic MPC [110,111] could be investigated for the optimal control of MicroCSP and building HVAC system. • Machine Learning-Based Control: In recent years, artificial intelligence (AI) has infiltrated all areas, and.…”

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Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

et al. 2021

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Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost.

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Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

et al. 2021

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End-to-End Deep Reinforcement Learning Control for HVAC Systems in Office Buildings

Zhong

Zhang

et al. 2022

Designs

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The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in office buildings, and its operation is critical for indoor thermal comfort. While previous studies have indicated that reinforcement learning control can improve HVAC energy efficiency, they did not provide enough information about end-to-end control (i.e., from raw observations to ready-to-implement control signals) for centralized HVAC systems in multizone buildings due to the limitations of reinforcement learning methods or the test buildings being single zones with independent HVAC systems. This study developed a model-free end-to-end dynamic HVAC control method based on a recently proposed deep reinforcement learning framework to control the centralized HVAC system of a multizone office building. By using the deep neural network, the proposed control method could directly take measurable parameters, including weather and indoor environment conditions, as inputs and control indoor temperature setpoints at a supervisory level. In some test cases, the proposed control method could successfully learn a dynamic control policy to reduce HVAC energy consumption by 12.8% compared with the baseline case using conventional control methods, without compromising thermal comfort. However, an over-fitting problem was noted, indicating that future work should first focus on the generalization of deep reinforcement learning.

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A New Approach to Off-Line Robust Model Predictive Control for Polytopic Uncertain Models

Cited by 2 publications

References 18 publications

Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

End-to-End Deep Reinforcement Learning Control for HVAC Systems in Office Buildings

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