College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
Date
6-2026
Primary Advisor
Siavash Farzan, College of Engineering, Electrical Engineering Department
Abstract/Summary
This project investigates the modeling, control, and state estimation of a Crazyflie 2.0 quadrotor using both a custom nonlinear simulation and the NVIDIA Isaac Lab robotics simulation framework. A twelve-state rigid-body dynamic model was developed to represent the translational and rotational motion of the vehicle, and classical cascaded PID control was implemented for position and attitude stabilization. To improve state awareness in the presence of sensor noise, Kalman Filter (KF) and Extended Kalman Filter (EKF) algorithms were integrated for state estimation. Initial controller development and verification were performed using a custom MATLAB/Python simulation environment based on numerical integration of the nonlinear equations of motion. The controller was then adapted to Isaac Lab, which provides a high-fidelity physics engine, realistic sensor models, and more accurate contact and rigidbody dynamics. Simulation results demonstrate stable hover and position tracking performance, with the cascaded PID controller successfully regulating the vehicle’s altitude, attitude, and horizontal position across step-response tests in all coordinate axes. The EKF substantially reduced the effect of injected sensor noise, enabling stable closed-loop flight under non-ideal measurement conditions. The results highlight the effectiveness of classical control techniques for small unmanned aerial vehicles and demonstrate the utility of high-fidelity simulation environments for controller validation and state-estimation development. This work establishes a foundation for future research involving advanced control methods, autonomous navigation, and hardware implementation on physical Crazyflie platforms.
URL: https://digitalcommons.calpoly.edu/eesp/728