Zheng-Meng/Tracking-Control
Published in Nature Communications: Model-free tracking control of complex dynamical trajectories with machine learning.
This project helps researchers and engineers precisely guide complex, dynamic systems to follow desired paths, even when the system's underlying physics are not fully known. You input data describing the desired path (like a perfect circle or a chaotic Lorenz attractor) and the project outputs the necessary control signals to make the real system track that path very closely. This is for scientists or engineers working with intricate physical or simulated systems.
Use this if you need to accurately control a complex system to follow a specific trajectory without needing a detailed mathematical model of the system itself.
Not ideal if you are looking for a simple PID controller or if you already have a perfect mathematical model of your system.
Stars
34
Forks
7
Language
MATLAB
License
MIT
Category
Last pushed
Nov 24, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Zheng-Meng/Tracking-Control"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
StanfordASL/Trajectron
Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic...
StanfordASL/Trajectron-plus-plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting...
uber-research/LaneGCN
[ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting
agrimgupta92/sgan
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks",...
devendrachaplot/Neural-SLAM
Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"