3,602 research outputs found
Input-to-state stability of unbounded bilinear control systems
We study input-to-state stability of bilinear control systems with possibly
unbounded control operators. Natural sufficient conditions for integral
input-to-state stability are given. The obtained results are applied to a
bilinearly controlled Fokker-Planck equation.Comment: 20 pages, completely new version based on the few preliminary ideas
in v1. Compared to v1, the results have been significantly generalized and
extende
Input-to-state stability of infinite-dimensional control systems
We develop tools for investigation of input-to-state stability (ISS) of
infinite-dimensional control systems. We show that for certain classes of
admissible inputs the existence of an ISS-Lyapunov function implies the
input-to-state stability of a system. Then for the case of systems described by
abstract equations in Banach spaces we develop two methods of construction of
local and global ISS-Lyapunov functions. We prove a linearization principle
that allows a construction of a local ISS-Lyapunov function for a system which
linear approximation is ISS. In order to study interconnections of nonlinear
infinite-dimensional systems, we generalize the small-gain theorem to the case
of infinite-dimensional systems and provide a way to construct an ISS-Lyapunov
function for an entire interconnection, if ISS-Lyapunov functions for
subsystems are known and the small-gain condition is satisfied. We illustrate
the theory on examples of linear and semilinear reaction-diffusion equations.Comment: 33 page
Infinite-dimensional input-to-state stability
In this talk we discuss infinite-dimensional versions of well-known stability notions relating the external input and the state of a linear system governed by the equation Here, and are unbounded operators. For instance, the system is called \textit{-input-to-state stable} if is bounded as a mapping from to the state space for all . In particular, we elaborate on the relation of this notion to \textit{integral input-to-state} stability and \textit{(zero-class) admissibility} with a special focus on the case .\\ This is joint work with B.~Jacob, R.~Nabiullin and J.R.~Partington
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification
The goal of this paper is to analyze Long Short Term Memory (LSTM) neural
networks from a dynamical system perspective. The classical recursive equations
describing the evolution of LSTM can be recast in state space form, resulting
in a time-invariant nonlinear dynamical system. A sufficient condition
guaranteeing the Input-to-State (ISS) stability property of this class of
systems is provided. The ISS property entails the boundedness of the output
reachable set of the LSTM. In light of this result, a novel approach for the
safety verification of the network, based on the Scenario Approach, is devised.
The proposed method is eventually tested on a pH neutralization process.Comment: Accepted for Learning for dynamics & control (L4DC) 202
Input to State Stability of Bipedal Walking Robots: Application to DURUS
Bipedal robots are a prime example of systems which exhibit highly nonlinear
dynamics, underactuation, and undergo complex dissipative impacts. This paper
discusses methods used to overcome a wide variety of uncertainties, with the
end result being stable bipedal walking. The principal contribution of this
paper is to establish sufficiency conditions for yielding input to state stable
(ISS) hybrid periodic orbits, i.e., stable walking gaits under model-based and
phase-based uncertainties. In particular, it will be shown formally that
exponential input to state stabilization (e-ISS) of the continuous dynamics,
and hybrid invariance conditions are enough to realize stable walking in the
23-DOF bipedal robot DURUS. This main result will be supported through
successful and sustained walking of the bipedal robot DURUS in a laboratory
environment.Comment: 16 pages, 10 figure
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