Automatic Generation of Bounds for Polynomial Systems with Application to the Lorenz System

12/21/2017
by   Klaus Röbenack, et al.
0

This study covers an analytical approach to calculate positively invariant sets of dynamical systems. Using Lyapunov techniques and quantifier elimination methods, an automatic procedure for determining bounds in the state space as an enclosure of attractors is proposed. The available software tools permit an algorithmizable process, which normally requires a good insight into the systems dynamics and experience. As a result we get an estimation of the attractor, whose conservatism only results from the initial choice of the Lyapunov candidate function. The proposed approach is illustrated on the well-known Lorenz system.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/26/2023

Differential Elimination and Algebraic Invariants of Polynomial Dynamical Systems

Invariant sets are a key ingredient for verifying safety and other prope...
research
12/06/2019

Learning to Correspond Dynamical Systems

Correspondence across dynamical systems can lend us better tools for lea...
research
06/16/2020

Learning Dynamics Models with Stable Invariant Sets

Stable invariant sets are an essential notion in the analysis and applic...
research
02/07/2019

Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems

We study the performance of sparse regression methods and propose new te...
research
06/21/2021

Learn Like The Pro: Norms from Theory to Size Neural Computation

The optimal design of neural networks is a critical problem in many appl...
research
01/31/2011

Boolean network robotics: a proof of concept

Dynamical systems theory and complexity science provide powerful tools f...
research
06/26/2022

Learning neural state-space models: do we need a state estimator?

In recent years, several algorithms for system identification with neura...

Please sign up or login with your details

Forgot password? Click here to reset