Adaptive Regulation to Invariant Sets
Ivan Tyukin, Denis Efimov, Cees van Leeuwen
Abstract
A new framework for adaptive regulation to invariant sets is proposed. Reaching the target dynamics (invariant set) is to be ensured by state feedback while adaptation to parametric uncertainties is provided by additional adaptation algorithm. We show that for a sufficiently large class of nonlinear systems it is possible to adaptively steer the system trajectories to the desired non-equilibrium state without requiring knowledge or existence of a specific strict Lyapunov function.
Create a lesson
Related papers
Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates
David Martínez-Rubio, Cristóbal Guzmán
The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings
David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán et al.
Complexity Of Output Feedback Stabilization
Amir Ali Ahmadi, Abraar Chaudhry, Ijay Narang et al.
Convergence rate of the moment-SOS hierarchy for univariate polynomial optimization
Didier Henrion, Mohab Safey El Din
Geometry and Convergence of Quadratically Regularized Optimal Transport I
Alberto González-Sanz, Marcel Nutz
Constraint Qualifications and Gradient Flows for Block Vanishing Constraint Problems
Julian Niederer, Christoph Hansknecht, Andreas Potschka et al.