Embedded Firmware Control Systems Toolbox (Pure C and GNU Octave)

A MATLAB package for modelling multivariate stimulus-response data

Subspace methods for MIMO system identification

The usage of MATLAB System Identification Toolbox and PID parameters adjustment

An open-source linear control toolbox for MATLAB.

This project deals with system identification and machine learning of large-scale deformable mirrors used in adaptive optics. I have submitted two papers that deal with this important problem. The approaches can be generalized two other problems of estimating large-scale system with the dynamics described by partial differential equations.

emgr -- EMpirical GRamian Framework

This repository is for our paper: "The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control." It contains all the CAD files we used to build the pendulum hardware, their corresponding user's manual, and data set we collected from our hardware, which is useful for Machine Learning and AI community.

Implementation of Fast Orthogonal Search (FOS) Algorithm in MATLAB

Matlab implementation of online and window dynamic mode decomposition algorithms

Blind System Identification and Equalization Toolbox for MATLAB

System identification for Monash UAS aircraft

SON-EM - Algorithm for parameter estimation of hybrid time-varying parameter systems using Sum of Norms regularization and Expectation Maximization

An algorithm for RLS model parameters identification using MATLAB.

Code repo for the paper https://arxiv.org/abs/1809.05021

Implementation of the algorithm described in the following paper. Korenberg, M., Billings, S.A. and Liu, Y.P. (1987) An Orthogonal Parameter Estimation Algorithm for Nonlinear Stochastic Systems

Implementation of a recurrent neural network based Observer/Identifier for highly Non-Linear Dynamical Systems, using the Lorenz Attractor as a test case.

Hierarchical Bayesian approaches for robust inference in ARX models

A collection of Matlab routines for illustrating methods for identifying Radial Basis Function (Neural) Network models for NARX-type nonlinear dynamical systems from data, incorporating prior information about the system's fixed points.

This work presents the application of machine learning models in order to obtain a sparse governing equation of complex fluid dynamics problems.