cillian's starred repositories
paper-simulation
Let's reproduce paper simulations of multi-robot systems, formation control, distributed optimization and cooperative manipulation.
Optimal-Control-via-Neural-Networks
Code repo for ICLR paper: Optimal Control Via Neural Networks: A Convex Approach
Data-driven-control
A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.
DDRTC-of-UMSs
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 rejection control (ADRC). The differentially flat output is derived directly from measured data with unknown dynamics and parameters of UMS by the flat output identification (FOID) algorithm. A reduced nominal model of UMS is proposed to simplify the process of finding flat output and trajectory planning. Technique of sparse regression is applied to identify the relationships between flat output and system states, which reduces the order of the well-known extended state observer (ESO) and thereby make the ESO more effective for both trajectory planning and tracking in terms …
Tutorial4RL
Tutorial4RL: Tutorial for Reinforcement Learning. 强化学习入门教程.
Lyapunov_Stable_NN_Controllers
Lyapunov-stable Neural Control for State and Output Feedback
LLM-Tuning
Tuning LLMs with no tears💦; Sample Design Engineering (SDE) for more efficient downstream-tuning.
PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
deeprl_network
multi-agent deep reinforcement learning for networked system control.
ros_motion_planning
Motion planning and Navigation of AGV/AMR：ROS planner plugin implementation of A*, JPS, D*, LPA*, D* Lite, Theta*, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, PSO, Voronoi, PID, LQR, MPC, DWA, APF, Pure Pursuit etc.
event-triggered-consensus
事件触发一致性及其对应的文献