no code implementations • 10 Mar 2024 • Ming Li, Zhiyong Sun, Patrick J. W. Koelewijn, Siep Weiland
Finally, we demonstrate the efficacy of our method through a collision avoidance example, investigating the essential properties including safety, robustness, and smoothness under various tunable scaling terms.
no code implementations • 5 Mar 2024 • Ming Li, Zhiyong Sun, Siep Weiland
This paper revisits a classical challenge in the design of stabilizing controllers for nonlinear systems with a norm-bounded input constraint.
no code implementations • 15 Feb 2024 • Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth
Namely, the universal shifted concept, which considers stability and performance w. r. t.
no code implementations • 16 Aug 2023 • Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth
Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach.
no code implementations • 4 Apr 2023 • Ming Li, Zhiyong Sun, Zirui Liao, Siep Weiland
Model predictive control (MPC) with control barrier functions (CBF) is a promising solution to address the moving obstacle collision avoidance (MOCA) problem.
no code implementations • 23 May 2022 • Jan Decuyper, Koen Tiels, Siep Weiland, Mark C. Runacres, Johan Schoukens
Multivariate functions emerge naturally in a wide variety of data-driven models.
no code implementations • 18 May 2021 • Jan Decuyper, Koen Tiels, Siep Weiland, Johan Schoukens
Usually a generic basis function expansion is used, e. g. a polynomial basis, and the parameters of the function are tuned given the data.
no code implementations • 20 Apr 2021 • Patrick J. W. Koelewijn, Roland Tóth, Henk Nijmeijer, Siep Weiland
The Linear Parameter-Varying (LPV) framework has been introduced with the intention to provide stability and performance guarantees for analysis and controller synthesis for Nonlinear (NL) systems via convex methods.