There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

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Review on model predictive control: an engineering perspective

Statistics: Concepts and Applications for Science.ByDavid LeBlanc.Sudbury (Massachusetts): Jones and Bartlett Publishers. $89.95 (two‐volume set). xvii + 382 p; ill.; index. ISBN: 0–7637–4699–1. 2004.Workbook to AccompanyStatistics: Concepts and Applications for Science.ByDavid LeBlanc.Sudbury (Massachusetts): Jones and Bartlett Publishers. iii + 329 p; ill.; no index. ISBN: 0–7637–2220–0. 2004.

Study on energy-saving operation of a combined heating system of solar hot water and air source heat pump

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A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient

This paper presents modeling and control methods for an integrated thermal hydronic system (ITHS) consisting of a solar thermal collector, a thermal storage unit and a thermal booster. Control inputs are: valves activation time, pitch angle, solar pump rate and compressor rate, measurements are: temperatures at critical points, and disturbances are: ambient air temperature, source water temperature and the position of the sun relative to the plane of the solar thermal collector. In this context, the problem of maintaining temperature within the given range of the thermal storage is considered. To address this problem, the model is developed based on lumped-parameter analysis. Using the model, three controllers are tested in simulations, i.e., the on-off controller, the PI controller and the scheduling controller. The modeling will be useful in developing optimal control for high efficiency ITHS.

Modeling and control for an integrated thermal hydronic system

An optimal control strategy for a heat pump in an integrated solar thermal system

This paper proposes a method to optimize simultaneously the configuration and the controller in a solar domestic hot water system, which is an instance of an integrated thermal hydronic system. The system consists of five main components, a solar thermal collector, an air-to-water heat pump, a thermal storage tank, and two heat exchangers. The thermal energy is transferred through the fluid circulating in pipes. For this system, a state-space model is built, and an energy optimization problem is formulated. The problem is numerically solved by the gradient-descent method. Simulation results confirm the usefulness of the proposed optimization method in enhancing the overall system performance. The importance of the simultaneous optimization of both the system configuration and the controller is also demonstrated.

Simultaneous optimization of configuration and controller parameters in an integrated solar thermal hydronic system

A hybrid Gaussian process approach to robust economic model predictive control

This paper proposes a switching gain-scheduled feedback control strategy for a specific solar thermal generating system, called an Integrated Thermal Hydronic System (ITHS). The ITHS has not only a solar thermal collector as the main energy source but also a heat pump as an auxiliary source. For the ITHS, a temperature regulation problem is considered, where hot water temperature in a tank is maintained at a specified level against varying ambient air temperature, domestic hot water usage, and solar irradiation. A switching gain-scheduled linear-Quadratic-regulator (LQR) is designed to adjust fluid flow rates transferring the heat within the system. It is demonstrated that the proposed switching LQR outperforms a conventional LQR in both temperature regulation and energy consumption for ITHS operation.

Vladimir Grebenyuk

Papers

Modeling and control for an integrated thermal hydronic system

An optimal control strategy for a heat pump in an integrated solar thermal system

Simultaneous optimization of configuration and controller parameters in an integrated solar thermal hydronic system

A hybrid Gaussian process approach to robust economic model predictive control

Switching Gain-Scheduled Linear-Quadratic-Regulator Control for Integrated Solar Thermal Hydronic Systems