Damian Frick


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Frick

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Damian

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Publications 1 - 10 of 16
  • Ferreau, Hand Joachim; Almer, Stefan; Verschueren, Robin; et al. (2017)
    IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
    Starting in the late 1970s, optimization-based control has built up an impressive track record of successful industrial applications, in particular in the petrochemical and process industries. More recently, optimization methods for automatic control are more and more deployed on so-called embedded hardware to cater for application-specific needs such as guaranteed communication latency, low energy consumption or cost effectiveness. This development greatly broadens the scope of applications to which optimization methods can be applied to sectors such as robotics, automotive, aerospace or power electronics. However, it also poses additional challenges regarding both the algorithmic concepts and their actual implementations for a given computing hardware. This survey paper discusses key challenges for using embedded optimization methods and summarizes their main use cases in current industrial practice. Motivated by this discussion, a number of dedicated embedded optimization algorithms and their actual implementations are reviewed. The presentation is organized according to the mathematical structure of the embedded optimization problem, ranging from convex quadratic programming over more general convex and nonconvex problems to formulations comprising discrete optimization variables.
  • Frick, Damian; Schulthess, Felix (2011)
  • Pawlus, Witold; Frick, Damian; Morari, Manfred; et al. (2015)
    Proceedings of the 41st Annual Conference of the IEEE Industrial Electronics Society (IECON2015)
  • Frick, Damian; Wood, Tony A.; Ulli, Gian; et al. (2017)
    IEEE Control Systems Letters
  • Tanaskovic, Marko; Zhao, Chen; Percacci, Federico; et al. (2018)
    Proceedings oft the 2018 IEEE 9th International Symposium on Sensorless Control for Electrical Drives (SLED 2018)
  • Torrisi, Giampaolo; Grammatico, Sergio; Frick, Damian; et al. (2017)
    2017 IEEE 56th Annual Conference on Decision and Control (CDC)
    In this paper, we analyze first-order methods to find a KKT point of the nonlinear optimization problems arising in Model Predictive Control (MPC). The methods are based on a projected gradient and constraint linearization approach, that is, every iteration is a gradient step, projected onto a linearization of the constraints around the current iterate. We introduce an approach that uses a simple ℓ p merit function, which has the computational advantage of not requiring any estimate of the dual variables and keeping the penalty parameter bounded. We then prove global convergence of the proposed method to a KKT point of the nonlinear problem. The first-order methods can be readily implemented in practice via the novel tool FalcOpt. The performance is then illustrated on numerical examples and compared with conventional methods.
  • Almér, Stefan; Frick, Damian; Torrisi, Giampaolo; et al. (2019)
    2019 12th Asian Control Conference (ASCC)
  • Hohm, Tim; Egli, Matthias; Gaehwiler, Samuel; et al. (2008)
    Lecture Notes in Computer Science ~ Artificial evolution : 8th international conference : revised selected papers
  • Frick, Damian; Domahidi, Alexander; Vukov, Milan; et al. (2012)
Publications 1 - 10 of 16