SWQ21's Stars
HiroIshida/robust-tube-mpc
Example implementation for robust model predictive control using tube
ai-winter/matlab_motion_planning
Motion planning and Navigation of AGV/AMR:matlab implementation of Dijkstra, A*, Theta*, JPS, D*, LPA*, D* Lite, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, Voronoi, PID, LQR, MPC, APF, RPP, DWA, DDPG, Bezier, B-spline, Dubins, Reeds-Shepp etc.
jmwang0117/HE-Drive
HE-Drive: Human-Like End-to-End Driving with Vision Language Models
wujingda/Human-in-the-loop-Deep-Reinforcement-Learning
(Engineering) Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving
ZehuaJia/Tube-based-MPC-for-nonlinear-systems
The reproduction of tube-based MPC for nonlinear systems
alexliniger/AdversarialRoadModel
Discriminating kernel algorithm implementation and neural network approximation, as presented in "Safe Motion Planning for Autonomous Drivingusing an Adversarial Road Model"
lucattycord/uav-tube-mpc
JianZhou1212/learning-based-rigid-tube-rmpc
This is the MATLAB code for tube robust MPC with uncertainty quantification
monimoyb/RMPC_SimpleTube
A simple robust MPC for linear systems with model mismatch: Balancing conservatism vs computational complexity
OscarHuangWind/Safe-Human-in-the-Loop-RL
[T-ITS'24] A safety-aware human-in-the-loop Reinforcment Learning (SafeHiL-RL) approach for end-to-end autonomous driving.
OscarHuangWind/Learning-from-Intervention
[ICRA 2024] Learning from Human Guidance: Uncertainty-aware deep reinforcement learning for autonomous driving.
martindoff/DC-TMPC
DC-TMPC: A tube-based MPC algorithm for systems that can be expressed as a difference of convex functions.
martindoff/Radial-basis-TMPC
Learning-based robust tube based MPC of nonlinear systems via difference of convex radial basis functions
TUDelft-DataDrivenControl/Automatica2024_CL-DeePC
Code for submission to 2024 submission to Automatica titled "Closed-loop Data-enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control"
ZQuang2202/Reinforcement-learning-based-control-for-BTs
This is a official code implementation for Nonlinear RISE based Integral Reinforcement Learning algorithms for perturbed Bilateral Teleoperators with variable time delay (Neurocomputing Journal).
Schulze18/Stochastic-Tube-MPC-Driver-Assistant
zhang-zengjie/dl-vehicle-mpc
Using deep learning to predict the motion of a MPC-controlled vehicle
gtfactslab/Banks_ICRA_2021
This code supplements the ICRA 2021 submission "Physical Human-UAV Interaction via Differentially Flat OutputGeneration using Admittance Control"
XingyanMao/Improved-tube-mpc-for-string-stability
The simulation of Distributed Tube Model Predictive Control for String Stability of Heterogeneous Vehicle Platoons
kirankanchu/RL_Nash_Games
Reinforcement Learning Policy Iterations for Nash Differential Games
Dralucal/Automatic_Chaos
Code related to the Automatica Paper "On the Application of Galerkin Projection based Polynomial Chaos in Linear Systems and Control"
Guitao-Yang/Distributed-UIO-Design
This repository includes simulation codes that are used for the Automatica paper "State estimation using a network of distributed observers with unknown inputs".
hriday3196/DRL_Controllers
This repository contains exploratory work comprising the usage of different reinforcement learning algorithms along with different control algorithms to get improved response of Buck Boost Converter
JianZhou1212/learning-based-homothetic-tube-mpc
stesfazgi/CLL_IRL
Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
cdqu/D3IOC-Direct-data-driven-inverse-optimal-control
ChuangqiLee/llama-driver
Using the open source large model llama-8B, we make a decision scheme for assisted autonomous driving and try to personalize it by making data sets and federated learning.
FahimShakib/Model-reduction-by-MM-with-preservation-of-global-stability-for-a-class-of-nonlinear-models
Code corresponding to the publication https://doi.org/10.1016/j.automatica.2023.111227
gtfactslab/Cao_OptimisticControl
This code supplements the Automatica submission "An Optimistic Approach to Cost-Aware Predictive Control".
maobinlu/Supplement
Supplement to Submission IEEE-TAC 23-2094 Titled “Data-Driven Learning Control for Discrete-Time Non-Zero-Sum Game Output Regulation by Internal Model”