{"id":193,"date":"2021-09-01T15:19:00","date_gmt":"2021-09-01T15:19:00","guid":{"rendered":"https:\/\/elo-x.eu\/?p=193"},"modified":"2023-11-30T17:34:26","modified_gmt":"2023-11-30T17:34:26","slug":"aalorenzo-fagiano","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=193","title":{"rendered":"Nicolas Kessler"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tNicolas Kessler \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 in Information Technology – Systems and Control<\/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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Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)<\/span><\/p><\/div>

Politecnico di Milano<\/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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Nicolas Kessler graduated in mechatronics at Karlsruhe Institute of Technology in 2021 with focus on software engineering and control theory. Originally born in Germany, he also lived in France and China. During his studies he participated at Kamaro Engineering e. V. working on modular field robots. His Master thesis was on the modeling of the space maneuvering and docking vehicles at the test facility at University W\u00fcrzburg.<\/p>

Since May 2021 he is carrying out research activity at Politecnico di Milano, Italy, for ELO-X.<\/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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The next generation of highly automated, interconnected, and collaborative industrial systems will require solutions able to: (a) monitor in real-time the system and components\u2019 behavior to estimate their conditions and spot possible faults or anomalies, and (b) manage many interacting subsystems and processes operating at different timescales, with a hierarchical topology, subject to constraints and performance requirements. These two tasks are strongly connected, since condition monitoring and fault detection must be fully integrated in the automation and control system. However, the current design approaches lack a systematic way to carry out such integration. To overcome this challenge, Nicolas will develop a design methodology for automation and control systems where learning-based solutions for condition monitoring, fault detection and recovery are implemented at multiple levels and time-scales in complex hierarchical control systems, and seamlessly integrated with optimization-based decision and control algorithms. The idea is to develop a high-level model predictive controller, running at slower frequency, that coordinates plant-wide operation and monitor its status using learning-based approaches. At lower layers, local controllers will exploit\u00a0 embedded learning and optimization to regulate single processes, monitor them and learn on-line their performance and conditions, implementing the directives received from the higher layers and sending them a feedback about the local status. Based on the overall feedback received from the lower levels, the high-level controller will readjust plant operation with the goal to maximize performance while guaranteeing safety and reliability.<\/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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\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\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>

Kessler, Nicolas; Fagiano, Lorenzo<\/p>

On gain scheduling trajectory stabilization for nonlinear systems: theoretical insights and experimental results<\/a> Journal Article<\/span> <\/p>

In: <\/span>International Journal of Robust and Nonlinear Control, <\/span>2025<\/span>.<\/p>

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

@article{kessler2024gain,
\r\ntitle = {On gain scheduling trajectory stabilization for nonlinear systems: theoretical insights and experimental results},
\r\nauthor = {Nicolas Kessler and Lorenzo Fagiano },
\r\nurl = {https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1002\/rnc.7784},
\r\ndoi = {https:\/\/doi.org\/10.1002\/rnc.7784},
\r\nyear  = {2025},
\r\ndate = {2025-01-07},
\r\njournal = {International Journal of Robust and Nonlinear Control},
\r\nabstract = {Steering a nonlinear system from an initial state to a desired one is a common task in control. While a nominal trajectory can be obtained rather systematically using a model, for example via numerical optimization, heuristics, or reinforcement learning, the design of a computationally fast and reliable feedback control law that guarantees rev{bounded deviations around} the found trajectory can be much more involved.
\r\nAn approach that does not require high online computational power and is well-accepted in industry is gain-scheduling.
\r\nThe results presented here pertain to the rev{boundedness} guarantees and rev{the set of safe initial conditions} of gain scheduled control laws, based on subsequent linearizations along the reference trajectory. The approach bounds the uncertainty arising from the linearization process, builds polytopic sets of linear time varying systems covering the nonlinear dynamics along the trajectory, and exploits sufficient conditions rev{for the existence of a robust polyquadratic Lyapunov function} to attempt the derivation of the desired gain-scheduled controller, via the solution of Linear Matrix Inequalities (LMIs). A result to estimate an ellipsoidal rev{set of safe initial conditions} is provided too. Moreover, arbitrary scheduling strategies between the control gains are considered in the analysis, and the method can be used also to check\/assess the rev{boundedness} properties obtained with an existing gain-scheduled law.
\r\nThe approach is demonstrated experimentally on a small quadcopter, as well as in simulation to design a scheduled controller for a chemical reactor model and to validate an existing control law for a gantry crane model.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Steering a nonlinear system from an initial state to a desired one is a common task in control. While a nominal trajectory can be obtained rather systematically using a model, for example via numerical optimization, heuristics, or reinforcement learning, the design of a computationally fast and reliable feedback control law that guarantees rev{bounded deviations around} the found trajectory can be much more involved.
\r\nAn approach that does not require high online computational power and is well-accepted in industry is gain-scheduling.
\r\nThe results presented here pertain to the rev{boundedness} guarantees and rev{the set of safe initial conditions} of gain scheduled control laws, based on subsequent linearizations along the reference trajectory. The approach bounds the uncertainty arising from the linearization process, builds polytopic sets of linear time varying systems covering the nonlinear dynamics along the trajectory, and exploits sufficient conditions rev{for the existence of a robust polyquadratic Lyapunov function} to attempt the derivation of the desired gain-scheduled controller, via the solution of Linear Matrix Inequalities (LMIs). A result to estimate an ellipsoidal rev{set of safe initial conditions} is provided too. Moreover, arbitrary scheduling strategies between the control gains are considered in the analysis, and the method can be used also to check\/assess the rev{boundedness} properties obtained with an existing gain-scheduled law.
\r\nThe approach is demonstrated experimentally on a small quadcopter, as well as in simulation to design a scheduled controller for a chemical reactor model and to validate an existing control law for a gantry crane model.<\/div>
Close<\/a><\/p><\/div>