Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
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Updated
Oct 31, 2024 - Julia
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
Automata on arbitrary networks, with Python
Non-Linear Dynamic Systems
Parallel Data Assimilation Framework
Obtaining the best coefficients of Inverse Dynamics Controller, for a dynamical system, with Optimization Algorithms.
In this project, an observer in the form of a stable neural network is proposed for any nonlinear MIMO system. As a result of experience, this observer utilizes a nonlinear in parameter neural network (NLPNN) which unlike LPNN, supports systems with higher degree of nonlinearity with no pre-knowledge of its dynamics. The learning rule for this n…
ODESCA is a MATLAB tool for the creation and analysis of dynamic systems described by ordinary differential equations
Computing Irreversible Evolutions
Model-based Calibration of Multiple Injections for a CI engine
ForSolver - linear and nonlinear solvers
This repository includes different versions of the prescribed-time controller as Simulink blocks and MATLAB script codes for engineering applications.
This repository includes some examples for the suboptimal active disturbance rejection controller (S-ADRC). The files are written in MATLAB and Simulink.
Reproducible code for our paper, "On Causal Discovery with Convergent Cross Mapping"
Repository for my Nonlinear Dynamics files
MATLAB toolbox for analysing controllability and accessibility of nonlinear systems.
C++11 implementation of numerical algorithms described in Numerical Analysis by Richard L. Burden and J. Douglas Faires
In this project a rather brilliant observer called Thau observer or Lipschitz observer is proposed and designed to estimate the states of a special form of nonlinear systems. All the details regarding the observer design and its simulation are given in "Kian Khaneghahi - Fault Midterm - Q4.pdf" report file.
Kalman filtering is a powerful technique for estimating the state of nonlinear mechatronics systems from noisy measurements. Kalman filters have a wide range of applications in robotics, vehicle control, and aircraft control.
This project is about implementing the classic sliding mode method for controlling a third degree nonlinear system stated in the report that has 3 uncertain parameters. The first chapter is about designing the sliding mode controller and its parameters. And finally, the second chapter is devoted to simulation and its results. The results show th…
Multiphysics modeling of nonlinear contact with Hexagon software
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