Tobias Sutter

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Prof. Dr.
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Sutter
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Tobias
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tobias.sutter@unisg.ch
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    Computing Optimal Joint Chance Constrained Control Policies
    (2025-02-26)
    Niklas Schmid
    ;
    Marta Fochesato
    ;
    Sarah H.Q. Li
    ;
    Tobias Sutter  
    ;
    John Lygeros
    We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard dynamic programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably optimal and easy-to-compute policies are still missing. We fill this gap by augmenting the dynamics via a binary state, allowing us to characterize the optimal policies and develop a dynamic programming-based solution method.
    Type:journal article
    Journal:IEEE Transactions on Automatic Control
    Volume:70
    Issue:7
    DOI:10.1109/TAC.2025.3546078
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123579
    Scopus© Citations 2
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    Joint Chance Constrained Optimal Control via Linear Programming
    (2024)
    Niklas Schmid
    ;
    Marta Fochesato
    ;
    Tobias Sutter  
    ;
    John Lygeros
    We establish a linear programming formulation for the solution of joint chance constrained optimal control problems over finite time horizons. The joint chance constraint may represent an invariance, reachability or reach-avoid specification that the trajectory must satisfy with a predefined probability. For finite state and action spaces, the solution is exact and our method computationally superior to approaches in the literature. For continuous state or action spaces, our linear programming formulation enables basis function approximations.
    Type:journal article
    Journal:IEEE Control Systems Letters
    Volume:8
    DOI:10.1109/LCSYS.2024.3403470
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123580
    Scopus© Citations 2
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    A Pareto Dominance Principle for Data-Driven Optimization
    (2024-09)
    Tobias Sutter  
    ;
    Bart P. G. Van Parys
    ;
    Daniel Kuhn
    We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be expressed as the minimizer of a surrogate optimization model constructed from the data. The quality of a data-driven decision is measured by its out-of-sample risk. An additional quality measure is its out-of-sample disappointment, which we define as the probability that the out-of-sample risk exceeds the optimal value of the surrogate optimization model. The crux of data-driven optimization is that the data-generating probability measure is unknown. An ideal data-driven decision should therefore minimize the out-of-sample risk simultaneously with respect to every conceivable probability measure (and thus in particular with respect to the unknown true measure). Unfortunately, such ideal data-driven decisions are generally unavailable. This prompts us to seek data-driven decisions that minimize the in-sample risk subject to an upper bound on the out-of-sample disappointment—again simultaneously with respect to every conceivable probability measure. We prove that such Pareto dominant data-driven decisions exist under conditions that allow for interesting applications: The unknown data-generating probability measure must belong to a parametric ambiguity set, and the corresponding parameters must admit a sufficient statistic that satisfies a large deviation principle. If these conditions hold, we can further prove that the surrogate optimization model generating the optimal data-driven decision must be a distributionally robust optimization problem constructed from the sufficient statistic and the rate function of its large deviation principle. This shows that the optimal method for mapping data to decisions is, in a rigorous statistical sense, to solve a distributionally robust optimization model. Maybe surprisingly, this result holds irrespective of whether the original stochastic optimization problem is convex or not and holds even when the training data are not independent and identically distributed. As a byproduct, our analysis reveals how the structural properties of the data-generating stochastic process impact the shape of the ambiguity set underlying the optimal distributionally robust optimization model.
    Type:journal article
    Journal:Operations Research
    Volume:72
    Issue:5
    DOI:10.1287/opre.2021.0609
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123578
    Scopus© Citations 10
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    Efficient Learning of a Linear Dynamical System With Stability Guarantees
    (2023-05)
    Wouter Jongeneel
    ;
    Tobias Sutter  
    ;
    Daniel Kuhn
    Type:journal article
    Journal:IEEE Transactions on Automatic Control
    DOI:10.1109/TAC.2022.3213770
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123582
    Scopus© Citations 9
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    Signals and Systems II
    (2016-06-01)
    Tobias Sutter  
    <jats:p>This article discusses the concept of flipped classroom. The flipped classroom concept aims to make the most of the personal contact between students and instructor, using online tools to carry some basic material that can be consulted at all times and releasing contact hours for a more creative, dynamic exchange. Creating intelligent, yet empathetic, digital technology to guide students along a personal, enjoyable, and, most importantly, effective learning experience is difficult and expectedly so. Beyond their use as teaching and learning media, online tools also serve to monitor class progress, both at the level of individual students and at the level of the class. This information allows the instructor to tailor subsequent material to the current state of learning of the class as a whole. The researchers hope that with a blended learning approach, and with digital solutions for lecture preparation, classroom engagement, and personalized learning, each student has the opportunity to obtain the best learning experience for him or herself.</jats:p>
    Type:journal article
    Journal:Mechanical Engineering
    DOI:10.1115/1.2016-jun-7
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123572
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