AleksandarHaber

Subspace-Identification-State-Space-System-Identification-of-Dynamical-Systems-and-Time-Series-

Model-Reference-Adaptive-Control---MIT-Rule

Linear-Quadratic-Regulator-Optimal-Control-in-Cpp-From-Scratch-by-Using-Newton-Method

We implemented a solution of the Linear Quadratic Regulator (LQR) Optimal Control problem in C++. We use the Newton method to solve the Riccati equation and to compute the solution.

Extended-Kalman-Implementation-in-Python

Kalman-Filter-Implementation-in-Cpp-Using-Eigen-Library

Model-Predictive-Control-Implementation-in-Python-1

Here, we post the codes that implement the Model Predictive Controller (MPC) for linear systems.

Model-Predictive-Control-for-Linear-Systems-in-Cpp-by-Using-Eigen-Library

This repository contains C++ files that explain how to implement the Model Predictive Control (MPC) algorithm for linear systems in C++ by using the Eigen C++ matrix library.

Python-Implementation-of-Particle-Filter

Simulation-and-Animation-of-Cart-Pole-State-Space-Model-in-Python-and-Pygame

In this GitHub repository we posted Python scripts that are used to automatically derive a symbolic state-space model of a cart

Eigen-Cpp-Matrix-Library-Demonstration

This repository contains code files that demonstrate how to use the Eigen C++ Matrix library for performing the basic matrix operations, computing eigenvalues, solving linear systems, and computing matrix decompositions.

Save-and-Load-Eigen-Cpp-Matrices-Arrays-to-and-from-CSV-files

The functions provided in this C++ source files are used to save and load Eigen C++ matrices/arrays to and from CSV values.

Disciplined-Kalman-Filter-Implementation-in-Python

This code implements the Kalman filter in Python by using an object oriented approach.

Deep-Q-Learning-Network-from-Scratch-in-Python-TensorFlow-and-OpenAI-Gym

These code files implement the deep Q learning network algorithm from scratch by using Python, TensorFlow, and OpenAI Gym. The codes are tested in the OpenAI Gym Cart Pole (v1) environment.

Machine-Learning-of-Dynamical-Systems-using-Recurrent-Neural-Networks

This project deals with learning to reproduce the input-output behavior of state-space models using recurrent neural networks and the Keras machine learning toolbox.

Machine-Learning-and-System-Identification-for-Adaptive-Optics

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.

ROS_modeling_using_xacro_and_urdf

These repository contains xacro/urdf and launch files necessary to model a robot.

ROS_OpenCV_Camera

test1-git

Phase-portraits-of-dynamical-systems-and-state-space-models-in-Python

In this Python dynamical system tutorial, we explain how to construct phase portraits of dynamical systems and state-space models. The posted code will construct a phase portrait and a state-space trajectory of a dynamical system. The webpage tutorial accompanying these codes is given here: https://aleksandarhaber.com/phase-portraits-of-state-space

Simulation-of-Differential-Equations-in-Python-with-Time-Varying-Inputs-and-Parameters

Solve-Nolinear-Equations-in-Python-by-Using-fsolve

Demonstration-of-Inheritance-in-Cpp

Bagging-Classifier-in-Python

In this repository, we posted the codes that demonstrate how to implement the Bagging classifier in the Scikit-learn library and Python.

Implementation-of-Voting-Classifier-in-Scikit-learn-and-Python---Ensemble-Machine-Learning

Brief-Intro-to-Multiclass-Classification-in-Scikit-learn-and-Python

Q-Learning-Algorithm-in-Python-with-Cart-Pole-OpenAI-Gym--Gymnasium-Environment

In this repository, we post the implementation of the Q-Learning (Reinforcement) learning algorithm in Python. The codes are tested in the Cart Pole OpenAI Gym (Gymnasium) environment.

Demonstration-of-Cart-Pole-OpenAI-Gym-Reinforcement-Learning-Environment-in-Python-

This code file demonstrates how to use the Cart Pole OpenAI Gym (Gymnasium) environment in Python.

SARSA-Temporal-Difference-Learning-in-Python

These code files implement the on-policy SARSA (State-Action-Reward-State-Action) reinforcement learning algorithm in Python.

Greedy-in-the-Limit-with-Infinite-Exploration-GLIE-Monte-Carlo-Reinforcement-Learning-in-Python

The Python codes given here, explain how to implement the Greedy in the Limit with Infinite Exploration (GLIE) Monte Carlo Control Method in Python. We use the OpenAI Gym (Gymnasium) to test the Python codes. More precisely we use the Frozen Lake Environment to test the GLIE Monte Carlo Control method.

Monte-Carlo-Method-for-Estimating-State-Value-Function-in-Python-

This code implements the (first visit) Monte Carlo method for estimating the state value function in Python.