Titel Deelnemers "Korte inhoud" "Model Predictive Control in the Chemical Process Industry hosted by Industrial Controllers (Model preditieve regeling in de chemische procesindustrie op industriële regelaars)" "Bart Huyck" "Linear Model Predictive Controllers (MPC) have been used in the chemical process industry for over forty years. They are currently implemented in a growing number of branches of the industry. The theoretical background of linear MPC is thoroughly investigated and recent evolutions focus on the speeding-up of algorithms to solve the quadratic optimization problem (QP), the heart of these controllers. Hence, MPC can be employed for fast systems as well as for embedded applications.The aim of this PhD is to employ the recent online QP algorithms in order to minimize the use of computational power and memory to solve the MPC problem. Typical controllers used in industry, i.e., Programmable Automation Controllers (PACs) and Programmable Logic controllers (PLCs) have limited computational power and available memory. An in-depth investigation was performed to see if it is possible to run MPC hosted by a PLC. To this end, two practical set-ups were used, i.e., a mini set-up consisting of a fan and heating resistor, and a pilot-scale distillation column set-up. The complete chain starting from black-box linear system identification, simulations, hardware-in-the-loop experiments, up to experiments on the actual set-ups were carried out. On the mini set-up it has been proven that a PLC is able to solve the quadratic optimization problem accompanied by a model predictive controller. For the pilot-scale distillation column, it has been demonstrated that a PLC is not powerful enough to solve the QP problem with recent QP solvers. Nevertheless, a classical algorithm successfully passed hardware-in-the-loop simulations and experiments on the set-up. For PAC devices on the other hand, it has been verified that, on this pilot scale set-up, all investigated QP solvers can easily solve the optimization problem part of the MPC algorithm." "Analysis of the impact of predictive models on the quality of the model predictive control for an experimental building" "Arash Erfani Beyzaee, Staf Roels, Dirk Saelens" "To increase energy efficiency of the building sector, many measures have been suggested which often require a predictive model of the building to function. Developing these models is one of the crucial challenges hampering pervasive use of these measures. Therefore, this study aims at assessing the impact of using different predictive models in an energy optimization application for an experimental building. First step in achieving this goal is developing various data-driven models for the investigated building in this study. Afterwards, a framework has been developed in which the performance of predictive models in the optimization strategy namely Model Predictive Control (MPC) could be evaluated. The results reveal that common indicators in the literature do no suffice to score the performance of models used in MPC, but another state of-the-art indicator; multi-step ahead prediction error is more suitable for evaluating predictive models deployed in MPC." "Design choices for the prediction and optimization stage of finite-set model based predictive control" "Thomas Vyncke, Steven Thielemans, Tom Dierickx, Ruben Dewitte, Michiel Jacxsens, Jan Melkebeek" "The interest in applying model-based predictive control (MBPC) for power-electronic converters has grown tremendously in the past years. This is due to the fact that MBPC allows fast and accurate control of multiple controlled variables for hybrid systems such as a power electronic converter and its load. As MBPC is a family of possible controllers rather than one single controller, several design choices are to be made when implementing MBPC. In this paper several conceptual possibilities are considered and compared for two important parts of online Finite-Set MBPC (FS-MBPC) algorithm: the cost function in the optimizations step and the prediction model in the prediction step. These possibilities are studied for two different applications of FS-MBPC for power electronics. The cost function is studied in the application of output current and capacitor voltage control of a 3-level flying-capacitor inverter. The aspect of the prediction model is studied for the stator flux and torque control of an induction machine with a 2-level inverter. The two different applications illustrate the versatility of FS-MBPC. In the study concerning the cost function firstly the comparison is made between quadratic and absolute value terms in the cost function. Comparable results are obtained, but a lower resource usage is obtained for the absolute value cost function. Secondly a capacitor voltage tracking control is compared to a control where the capacitor voltage may deviate without cost from the reference up to a certain voltage. The relaxed cost function results in better performance. For the prediction model both a classical, parametric machine model and a back propagation artificial neural network are applied. Both are shown to be capable of a good control quality, the neural network version is much more versatile but has a higher computational burden. However, the number of neurons in the hidden layer should be sufficiently high. All studied aspects were verified with experimental results and these validate the simulation results. Even more important is the fact that these experiments prove the feasibility of implementing online finite-set MBPC in an FPGA for both applications." "Simulation-based weight factor selection and FPGA prediction core implementation for finite-set model based predictive control of power electronics" "Thomas Vyncke, Steven Thielemans" "Learning-based risk-averse model predictive control for adaptive cruise control with stochastic driver models" "Mathijs Schuurmans, Panos Patrinos" "Plug-and-play control: integrating distributed model predictive control in today's industry" "Pieter Maelegheer, Clara-Mihaela Ionescu, Robain De Keyser" "In search of optimal building behavior models for model predictive control in the context of flexibility" "Arash Erfani Beyzaee, Staf Roels, Dirk Saelens" "Model predictive control (MPC) is an advanced control technique. It has been deployed to harness the energy flexibility of a building. MPC requires a dynamic model of the building to achieve such an objective. However, developing a suitable predictive model is the main challenge in MPC implementation for flexibility activation. This study focuses on the application of key performance indicators (KPIs) to evaluate the suitability of MPC models via feature selection. To this end, multiple models were developed for two houses. A feature selection method was developed to select an appropriate feature space to train the models. These predictive models were then quantified based on one-step ahead prediction error (OSPE), a standard KPI used in multiple studies, and a less-often KPI: multi-step ahead prediction error (MSPE). An MPC workflow was designed where different models can serve as the predictive model. Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation. Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model." "Building models for Model Predictive Control of office buildings with Concrete Core Activation" "Maarten Sourbron, Clara Verhelst, Lieve Helsen" "Model Predictive Control (MPC) is a good candidate to exploit the energy cost savings potential of Concrete Core Activation (CCA), while guaranteeing thermal comfort. A bottleneck for practical implementation is the selection and identification of the building control model. Using grey box models, this paper studies the impact of model structure and identification data set on the MPC control performance for an office building with CCA. Results for a one-year simulation show: (1) a 2nd-order model achieves equal control performance as a 4th-order one, (2) inclusion of solar or internal gains in the identification data set improves the model accuracy in general, especially for the 4th-order models, (3) MPC with a 2nd-order model reduces electricity consumption by 15\% compared to a reference controller, hereby deploying information about past operative temperature prediction errors and this without the need for solar or internal gains predictions." "Comparison of Model Predictive Control Performance Using Grey-Box and White-Box Controller Models of a Multi-zone Office Building" "Damien Picard, Maarten Sourbron, Filip Jorissen, Lieve Helsen" "Model Predictive Control (MPC) is a promising control method to reduce the energy use of buildings. Its commercialization is, however, hampered by the difficulty of obtaining a reliable controller model. This paper compares two approaches to obtain such controller model: (1) a white-box model approach for which a detailed first-principles building model is linearized, and (2) a system identification method using a grey-box model approach. The MPC performance using both model approaches is evaluated on a validated 12 zones model of an existing office building. The results indicate that the MPC performance is very sensitive to the prediction accuracy of the controller model. This paper shows that both approaches can lead to an efficient MPC as long as very accurate identification data sets are available. For the considered simulation case, the white-box MPC resulted in a better thermal comfort and used only 50% of the energy used by the best grey-box MPC." "Identification of Multi-Zone Grey-Box Building Models for Use in Model Predictive Control" "Javier Arroyo, Lieve Helsen" "Predictive controllers can greatly improve the performance of energy systems in buildings. An important challenge of these controllers is the need of a building model accurate and simple enough for optimization. Grey-box modeling stands as a popular approach, but the identification of reliable grey-box models is hampered by the complexity of the parameter estimation process, specifically for multi-zone models. Hence, single-zone models are commonly used, limiting the performance and applicability of the predictive controller. This paper investigates the feasibility of identification of multi-zone grey-box building models and the benefits of using these models in predictive control. For this purpose, the parameter estimation process is split by individual zones to obtain an educated initial guess. A virtual test case from the BOPTEST framework is contemplated to assess the simulation and control performance. The results show the relevance of modelling thermal interactions between zones in the multi-zone building."