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Online Regulation of Dynamical Systems to Solutions of Constrained Optimization Problems
This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem.
Singular Perturbation via Contraction Theory
In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems.
Perception-Based Sampled-Data Optimization of Dynamical Systems
Moreover, we show that the interconnected system tracks the solution trajectory of the underlying optimization problem up to an error that depends on the approximation errors of the neural network and on the time-variability of the optimization problem; the latter originates from time-varying safety and performance objectives, input constraints, and unknown disturbances.
