Part No. QLabs Virtual Aero 2
QLabs Virtual Aero 2 is a digital twin platform for aerospace and control systems education, enabling realistic labs with MATLAB/Simulink for remote learning.
QLabs Virtual Aero 2
Virtual platform for distance and blended undergraduate aerospace and controls courses
QLabs Virtual Aero 2 is a fully instrumented, dynamically accurate virtual twin of an Aero 2 system. It behaves in the same way as the physical hardware and can be measured and controlled using MATLAB® Simulink® and other development environments. QLabs Virtual Aero 2 can enrich your lectures and activities in traditional labs, or bring credible, authentic model-based lab experiences into your distance and blended aerospace and control systems course.
QLabs Virtual Aero 2 is available as a 12-month multi-seat subscription. The platform is compatible with the physical Aero 2 curriculum which covers concepts including modelling, system identification, attitude and speed control, PID control, gain scheduling, state-feedback control, coupled dynamics, and Kalman filtering.
| Specifications |
|
| App Download & Access to Subscription | Quanser Academic Portal |
| App OS Compatibility | Microsoft Windows 10 or Later |
| Required Software | Curriculum designed for MATLAB and Simulink R2022a or later, with Python 3 compatibility |
| Minimum Specifications | • Intel Core Ultra 5, Intel Core i5, Ryzen 5 • 8GB RAM • Intel UHD, or Iris Xe integrated GPU |
| Recommended Specifications | • Intel Core Ultra 7, Intel Core i7, Ryzen 7 • 16GB RAM • Intel Iris Xe or Arc integrated GPU |
• High-fidelity, credible lab experiences equivalent to use of physical lab equipment
• 12-month multi-seat subscription
• Full access to system parameters through MATLAB®/Simulink®
• Comprehensive curriculum for 1 DOF attitude, 2 DOF helicopter, and half-quadrotor configurations
ABET-aligned Instructor and Student Workbooks with complete lab exercises, covering topics:
• Hardware integration
• Single propeller speed control
• Pole-placement state-feedback balance control
• 1 DOF attitude control configuration
– PID control
– Iintroduction to IMU
– Modeling and model validation using transfer function
– System identification
– Gain scheduling
Laboratory Guides with modeling and control design examples:
• 2 DOF helicopter configuration
– Modeling
– Linear state-space representation
– State-feedback control
– Coupled dynamics
• Half-quadrotor configuration
– Modeling
– Simple yaw control
– Kalman filter
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