{"id":630,"date":"2021-09-01T18:26:55","date_gmt":"2021-09-01T18:26:55","guid":{"rendered":"https:\/\/elo-x.eu\/?p=630"},"modified":"2024-05-01T21:19:54","modified_gmt":"2024-05-01T21:19:54","slug":"andrea-ghezzi","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=630","title":{"rendered":"Andrea Ghezzi"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tAndrea Ghezzi \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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SYSCOP Laboratory<\/b><\/span><\/p><\/div>

University of Freiburg<\/b><\/span><\/p><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Andrea Ghezzi was born in Bergamo, Italy, in 1996. He obtained his Master degree cum laude in Automation and Control Engineering from Politecnico di Milano with a thesis about stochastic MPC with constraint prioritization. From December 2020 to September 2021 he was a researcher at Data Science R&D Department of Tenaris, in Dalmine, Italy, working on virtual sensing for improving the steelmaking process. From October 2021 he joined SYSCOP Laboratory at University of Freiburg, carrying out a PhD under the supervision of Prof. Dr. Moritz Diehl. He is a Marie-Curie fellow of the ELO-X project.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Project description<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Andrea works mainly on algorithms for solving mixed-integer nonlinear problems (MINLPs), with a focus on problems arising from the transcription of optimal control problems via direct methods.  Besides, he is interested in nonlinear model predictive control and its deployment on embedded hardware.<\/p>\n

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead more about this project<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1.<\/div>

Dong, Shiying; Ghezzi, Andrea; Harzer, Jakob; Frey, Jonathan; Gao, Bingzhao; Chen, Hong; Diehl, Moritz<\/p>

Real-Time NMPC With Convex--Concave Constraints and Application to Eco-Driving<\/a> Journal Article<\/span> <\/p>

In: <\/span>IEEE Transactions on Control Systems Technology, <\/span>2024<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{dong2024real,
\r\ntitle = {Real-Time NMPC With Convex--Concave Constraints and Application to Eco-Driving},
\r\nauthor = {Shiying Dong and Andrea Ghezzi and Jakob Harzer and Jonathan Frey and Bingzhao Gao and Hong Chen and Moritz Diehl},
\r\ndoi = {10.1109\/TCST.2024.3494993},
\r\nyear  = {2024},
\r\ndate = {2024-11-15},
\r\njournal = {IEEE Transactions on Control Systems Technology},
\r\nabstract = {In this brief, we propose a novel real-time numerical algorithm for solving nonlinear model predictive control (NMPC) with convex\u2013concave constraints, which arise in various practical applications. Instead of requiring full convergence for each problem at every sampling time, the proposed algorithm, called real-time iteration sequential convex programming (RTI-SCP), solves only one convex subproblem but iterates as the problem evolves. Compared with previous methods, the RTI-SCP adopts a more refined approach by linearizing only the concave components of the constraints. It retains and efficiently utilizes all the underlying convex structures, thereby transforming subproblems into structured forms that can be solved using the existing tools. In addition, to the best of our knowledge, the widely investigated eco-driving control strategy for autonomous vehicles is now formulated for the first time into a convex\u2013concave programming problem with strong theoretical properties. Eventually, the experimental results demonstrate that the proposed strategy can improve computational efficiency and overall control performance, and it is suitable for real-time implementation.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
In this brief, we propose a novel real-time numerical algorithm for solving nonlinear model predictive control (NMPC) with convex\u2013concave constraints, which arise in various practical applications. Instead of requiring full convergence for each problem at every sampling time, the proposed algorithm, called real-time iteration sequential convex programming (RTI-SCP), solves only one convex subproblem but iterates as the problem evolves. Compared with previous methods, the RTI-SCP adopts a more refined approach by linearizing only the concave components of the constraints. It retains and efficiently utilizes all the underlying convex structures, thereby transforming subproblems into structured forms that can be solved using the existing tools. In addition, to the best of our knowledge, the widely investigated eco-driving control strategy for autonomous vehicles is now formulated for the first time into a convex\u2013concave programming problem with strong theoretical properties. Eventually, the experimental results demonstrate that the proposed strategy can improve computational efficiency and overall control performance, and it is suitable for real-time implementation.<\/div>
Close<\/a><\/p><\/div>