Project description DEENESFRITPL Reliable predictive control with real-time applications Model predictive control (MPC) is widely used in process industries to control constrained systems with multiple inputs and outputs. MPC is commonly used in a two-layer architecture, allowing for the upper layer to provide the optimal operating point. However, studies have shown that a significant improvement in the economic performance of the plant can be obtained if both layers are combined together. One of the issues preventing the adoption of the aforementioned control scheme is the presence of plant–model mismatch. The EU-funded ReConDa project uses the ideas that form the linear control theory to handle the structural plant–model mismatch in a robust non-linear model predictive control (NMPC) framework. As a result, a safe, reliable and resource-efficient operation is established. Show the project objective Hide the project objective Objective Model predictive control (MPC) is widely used in process industries to control constrained systems with multiple input and outputs. Traditionally, the MPC is used in a two layer architecture where the upper layer gives the economic optimal operating point and the MPC is used in the lower layer tracks the optimal operating point. Recent studies shows that a significant improvement in the economic performance of the plant can be obtained if both the layers are combined together. One of the pressing issues preventing the process industries from adopting the aforementioned control scheme is the presence of plant-model mismatch. The work on this project uses ideas form the linear control theory to handle the structural plant-model mismatch in a robust NMPC framework, efficiently. We develop a model-error model (MEM) which uses plant measurements to improve the knowledge of the plant. We focus on developing a systematic way of choosing the MEM structure based on the data collected from an industrial production plant and use them for monitoring and control purposes. We develop an algorithm which works in parallel with the commercially available advanced process control solutions and makes them robust to plant model mismatch. Our project builds a computationally tractable scheme for model-based NMPC robust against the plant-model mismatch. As a result, a safe, reliable and resource-efficient operation is established. The theoretical developments of the project are implemented into a software package and released as an open-source project such that the collaboration with academia and industrial stakeholders is fostered. A demonstration on an industrial production plant and a laboratory pilot plant is also planned to showcase the benefits of the developed techniques in the real-world environment. A sound dissemination plan of the project ensures that the project reaches its target audience. Fields of science natural sciencescomputer and information sciencessoftware Keywords predictive control robust control optimal control chemical process control data-based learning Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2019 - Individual Fellowships Call for proposal H2020-MSCA-IF-2019 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator SLOVENSKA TECHNICKA UNIVERZITA V BRATISLAVE Net EU contribution € 155 364,48 Address Vazovova 5 81243 Bratislava Slovakia See on map Region Slovensko Bratislavský kraj Bratislavský kraj Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
