There are 2 repositories under lyapunov topic.
Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
Tools for the exploration of chaos and nonlinear dynamics
This is our standard library for nonlinear analysis. Many of these functions are the same we use in our services. We do have additional methods that are not public but could be made available in a future release. If you are interested in learning more, attending our workshops or webinars or using our data analysis services please contact bmchnonan@unomaha.edu.
procedural textures for blender (open shading language)
Lyapunov based controller design for trajectory tracking of an under-actuated autonomous underwater vehicle(AUV)
Files for my Nonlinear Systems and Controls class.
Fractal images with Python
PyTorch implementation of "Learning Stable Deep Dynamics Models" (https://papers.nips.cc/paper/9292-learning-stable-deep-dynamics-models), with extensions to controlled dynamical systems.
Official source code of Arrhythmia Detection
Lyapunov exponent of maps and ODE in Python 3, example with Henon Map and Lorenz System
Finding evidence for the existence of Strange, non-chaotic attractors in the Quasi-periodically driven duffing oscillator.
Python package to compute Lyapunov exponents, covariant Lyapunov vectors (CLV) and adjoints of a dynamical systems.
Evaluate the Lyapunov Spectrum of a dynamical system described by ODEs (in Python)
The package presents the Trajectory Tracking using Lyapunov-based Nonlinear control and Localization using Extended Kalman Filter
command line tool that generates ppm images to visualize dynamic system functions
LQG controller to control the dual pendulum cart
A Python package to simulate and measure chaotic dynamical systems.
P4 (Polynomial Planar Phase Portraits) software for phase portrait computation and representation in the plane or other projections such as the Poincaré Sphere.
Calculating Lyapunov indicators with multiprocessing in Python
LATEX report of my literature study into stable variable impedance learning.
A cutting-plane method to synthesize Lyapunov functions for neural network uncertain systems.
This my master's degree graduation thesis, it's about DC motor speed control using the sliding mode method, the motor it's controlled based on three models which are cascade and reduced, and complete model. the method has proved that it's robust against dc motor parameters changing and able to track a reference speed.
Fortran90 examples of Dynamic Systems
Nonlinear dynamical systems simulations using Julia with Matlab interoperability
Codigo Fortran que implementa el metodo de Benettin para realizar el calculo de los exponentes de Lyapunov y posteriormente seleccionando un parámetrodell modelo realizar un espectro de los Exponentes de Lyapunov.