Foldable Quadrotor Attitude Control During Configuration Switching
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Solution Overview
Problem
Existing foldable quadrotor designs face challenges in maintaining stable attitude tracking during configuration switching due to parameter-varying dynamics, modeling uncertainties, and external disturbances, leading to potential instability and crashes when navigating narrow spaces.
Innovation Solution
A novel adaptive control framework for foldable quadrotors is developed, incorporating a parameter estimation method and robustness term, coupled with a control-aware minimum-jerk trajectory planner to ensure stable attitude tracking and safe configuration transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If foldable quadrotors switch between different geometric configurations to navigate narrow spaces, then adaptability and versatility improve, but stability and reliability deteriorate due to parameter-varying dynamics during switching
Solution Approach 1:
The control system dynamically adapts to configuration changes by detecting the current geometric configuration and switching between different control parameters. The system transitions from static control to dynamic control that automatically adjusts to folding states, resolving the stability issue during configuration switching while maintaining the ability to navigate narrow spaces
Solution Approach 2:
The invention changes control parameters based on the geometric configuration state. By detecting whether the quadrotor is in a folded or unfolded configuration, the system adjusts control parameters accordingly, allowing stable attitude tracking across different configurations while maintaining adaptability for narrow space navigation
2Device complexity
If conventional geometric controllers are used for foldable quadrotors, then device complexity is reduced, but measurement precision and control accuracy worsen due to unmodeled aerodynamic disturbances and inertia variations
Solution Approach 1:
The system implements feedback mechanisms that detect configuration state and use this information to adjust control parameters. This feedback loop compensates for aerodynamic disturbances and inertia variations without requiring complex modeling, maintaining control accuracy while keeping the device relatively simple
Solution Approach 2:
The control system prepares by pre-defining control parameters for different geometric configurations. When a configuration change is detected, the system switches to the pre-prepared parameters for that configuration, enabling accurate attitude tracking without real-time complex calculations
Data Source
AI summary
A vehicle control framework enables improved attitude tracking and mode switching of a vehicle by modeling the vehicle as a switched system, where the vehicle is operable for changing a geometric configuration during flight. The vehicle control framework implements a control law that accommodates modeling uncertainties and unknown external disturbances. The vehicle also enforces a switching time constrained by a minimum dwell time which can be adaptively updated based on attitude errors.