data-driven

Data-Driven Predictive Control

Image Recoloring Based on Object Color Distributions (Eurographics 2019)

This repository contains data and code related to my new research project on approximating the Energy-Regulation Feasible Region of Virtual Power Plants using a Data-driven Inverse Optimization Approach.

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

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…

Data Driven Reachability Analysis from Noisy Data

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

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

HAPOD - Hierarchical Approximate Proper Orthogonal Decomposition

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.

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

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

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

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

Code for the paper "Convergent Methods for Koopman Operators on Reproducing Kernel Hilbert Spaces".

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!)

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

A unified MATLAB framework for data-driven Model Predictive Control (MPC), implementing and benchmarking multiple paradigms—including regression-based, Kalman-filter-based, optimization-based, and neural-network-based approaches—for nonlinear systems without explicit model identification.

This project investigates the relationship between LPBF manufacturing parameters (laser power & scan speed) and acoustic signals. Using data-driven and machine learning methods, we extract acoustic waveform features and build regression models to predict unknown power and speed values. (NUS ME5106 Mini Project 3)

Training stiff NODE in data-driven wastewater process modelling