Projects

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Inspection UAV System Development

Industry Collaboration Research Project, China Steel Corporation, Dept. of Green Energy & System Integration Research & Development, National Cheng Kung University, Dept. of Aeronautics and Astronautics, Intelligent Embedded Control Laboratory, 2023

This research considered an indoor UAV environmental inspection in heavy industry. The feedback position, velocity, and orientation rely on the vSLAM sensor to online estimate the pose of UAV in space. The following video demonstrates the quadrotor position control experiments using the vSLAM sensor.

Inversion-Based Trajectory Control for 3-DoF Quadrotor

Graduate Course Project, National Cheng Kung University, ME 8602 Feedforward Control (Instructor: Szu-Chi Tien), 2021

This research report propose a feedback-feedforward control architecture for a 3-DoF nonlinear quadrotor model which the relative degree is not well-defined and the system subject to the parameter uncertainties. The simulation results reveals that the proposed controller is capable of tracking the desired trajectory in the presence of modeling uncertainties.

System Parameter Identification of Quadrotor

Graduate Course Project, National Cheng Kung University, ES 7140 Applied System Identification (Instructor: Jer‑Nan Juang), 2021

This project uses the first-order integral method the identify the system parameter for a nonlinear quadrotor model. The simulation result indicates that the presented method can well identify the moment of inertia provide that the thrust model of the propellers is known.

Real-Time Parameter Estimation and Prediction-Based Control for Aircraft Pitch Dynamics

Graduate Course Project, National Cheng Kung University, ME 8601 Adaptive Control (Instructor: Ming-Shaung Ju), 2021

This project utilizes the recursive integral-operator-based least-squares (RLS) algorithm to estimate the system parameters for a aircraft pitch dynamics (textbook example). The simulation results show that the proposed RLS estimator enable to estimate the system parameters. By introduced the predicted states as the feedback states, the control performance is further improved.