This repository contains simulation codes generated for the paper titled "Comparative Analysis of Data-Driven Predictive Control Strategies," published in the 9th International Conference on Control, Instrumentation, and Automation in 2023. The paper is authored by Ali Rezaei & Ali Khaki-Sedigh.

This repository provides the codes for designing a data-driven control architecture for preserving the safety and tracking performance of constrained cyber-physical systems under networked attacks. (coming soon!)

Daline: A Data-driven Power Flow Linearization Toolbox for Power Systems Research and Education

This repository provides the codes for simulating a data-driven safety preserving control architecture for constrained cyber physical systems under cyber attacks.

Training stiff NODE in data-driven wastewater process modelling

Data-driven stabilization of positive linear systems. Full-state feedback and Linf sample noise by default.

Data Driven Reachability Analysis from Noisy Data

Data-Driven Predictive Control

MATLAB code for our paper on data driven set-based estimation using matrix zonotopes with set containment guarantees.

This code studies a linearized PF model through a data-driven approach.

Image Recoloring Based on Object Color Distributions (Eurographics 2019)

This paper presents a data-driven control design framework to achieve robust tracking control without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages the differential flatness property of linearized systems and online estimation and compensation of disturbances by active disturbance rejecti…

HAPOD - Hierarchical Approximate Proper Orthogonal Decomposition

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

Comparing the data-driven controller design methods both in matlab and in python.

Supplementary materials for Siggraph 2020 technical paper Fabrication-in-the-Loop Co-Optimization of Surfaces and Styli for Drawing Haptics

Noise-level estimation using minima controlled recursive averaging approach and denoising using Stein's unbiased risk estimates in STFT domain.