Flight Control Evaluation Using Barrier Functions After Rotor Failure
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Solution Overview
Problem
Current multirotor air vehicles, such as quadrotors and octorotors, become unstable and uncontrollable if multiple rotors fail, and existing solutions for flight safety and control are costly, require manual oversight, and lack flexibility in adapting to new environments and unplanned situations.
Innovation Solution
A computerized method and system that evaluates flight controls by defining a vehicle dynamics model, constructing barrier functions, identifying candidate invariant sets, and analyzing flight controls using these sets to confirm command tracking and identify commands outside safe regions, ensuring the aerial vehicle's stability and adaptability through automated reasoning tools like SMT solvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based or expert knowledge systems are used for flight control, then flight safety can be addressed, but the system cost increases and manual oversight is required
Solution Approach 1:
The patent replaces rules-based control systems with a model predictive control (MPC) system that uses a vehicle dynamics model and barrier functions to automatically ensure flight safety. The MPC controller computes control inputs that inherently satisfy safety constraints without requiring manual oversight or complex rules-based logic.
Solution Approach 2:
The patent transforms the control approach by changing from discrete rules to continuous mathematical functions (barrier functions) that define safe regions in the state space. This allows the system to automatically adapt to different flight conditions while maintaining safety guarantees through the dynamics model.
2Extent of automation
If autonomous subsystems are used for flight control, then some automation is achieved, but manual oversight is still required and flexibility in new environments is limited
Solution Approach 1:
The patent implements a dynamic control system where the MPC controller continuously recalculates optimal control inputs based on the current vehicle state and desired trajectory. The barrier functions dynamically adjust to maintain the vehicle within safe regions while adapting to changing flight conditions, eliminating the need for manual oversight.
Solution Approach 2:
The vehicle dynamics model serves multiple functions: it predicts future vehicle states for trajectory planning, defines safe operating regions through barrier functions, and generates control commands that inherently satisfy safety constraints. This unified approach provides both full automation and adaptability to new environments.
3Reliability
If more rotors are added to increase robustness to rotor failures, then fault tolerance improves, but device complexity and cost increase
Solution Approach 1:
The patent implements a feedback mechanism where the MPC controller continuously monitors the vehicle state and compares it against safe regions defined by barrier functions. When rotor failures occur, the controller automatically adjusts control inputs to maintain stability and keep the vehicle within safe operating regions, providing robustness without additional hardware.
Data Source
AI summary
Systems and methods provide for the evaluation of flight controls for an aerial vehicle and define a vehicle dynamics model corresponding to an aerial vehicle. One or more barrier functions are constructed based on the defined vehicle dynamics model, and candidate invariant sets are identified for the defined vehicle dynamics model using the one or more barrier functions. A plurality of flight controls of the aerial vehicle are analyzed using the identified candidate invariant sets and within the analyzed candidate invariant sets, command tracking of the aerial vehicle is confirmed with control commands, using the analyzed plurality of flight controls. Control commands falling outside of the analyzed candidate invariant sets are identified.