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Navigation and Control of Unconventional VTOL UAVs in Forward-Flight with Explicit Wind Velocity Estimation


Core Concepts
This paper presents a solution for the state estimation and control problems for unconventional VTOL UAVs in forward-flight conditions using an Invariant Extended Kalman Filter. The approach improves performance by incorporating wind velocity estimates into the attitude controller and control allocator.
Abstract
The paper addresses the navigation and control challenges of unconventional VTOL UAVs operating in forward-flight conditions. It introduces a tightly-coupled state estimation approach using an Invariant Extended Kalman Filter (IEKF) to estimate aircraft navigation states, sensor biases, and wind velocity. The wind velocity estimates are utilized in the attitude controller and control allocator to enhance performance. The study includes a numerical example with Monte-Carlo simulations demonstrating robustness to various conditions. Several key points include: Increasing use of VTOL UAVs for diverse applications. Consideration of tailsitter-type VTOL UAVs with unconventional configurations. Importance of estimating aircraft states and wind velocity for reliable performance. Application of SO(3)-based attitude controller without separate sideslip loop. Use of control allocator to determine actuator usage for desired moments. Integration of wind velocity estimates into control strategies for improved performance. Detailed modeling of aerodynamic forces on segments for accurate control allocation. The study provides insights into advanced navigation and control strategies for unconventional VTOL UAVs operating in forward-flight conditions, emphasizing the significance of incorporating explicit wind velocity estimation into the control framework.
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Changes were made to this version by the publisher prior to publication. DOI: 10.1109/LRA.2020.2966406 MITACS Accelerate and NSERC Discovery Grants Program supported this work.
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Deeper Inquiries

How can these advanced navigation and control strategies be adapted for other types of UAVs?

These advanced navigation and control strategies can be adapted for other types of UAVs by considering the specific characteristics and requirements of each type. For example, different UAV configurations may have varying numbers and types of sensors, actuators, or control surfaces. The key is to tailor the state estimation approach, guidance laws, attitude controllers, and control allocation methods to suit the particular dynamics and constraints of the UAV in question. By understanding how these components interact within the system architecture, adjustments can be made to optimize performance for different UAV designs.

What are potential limitations or drawbacks of relying on wind velocity estimates in the control system?

Relying on wind velocity estimates in a control system introduces several potential limitations or drawbacks. One limitation is the accuracy of wind estimation algorithms which may vary based on environmental conditions such as turbulence or gusts. Inaccurate wind estimates could lead to suboptimal performance or instability in flight operations. Additionally, delays in updating wind velocity information could impact real-time decision-making processes during flight maneuvers. Another drawback is that reliance on estimated wind velocities adds complexity to the overall system design. It requires additional computational resources for processing sensor data and implementing algorithms for estimating windspeed accurately. Moreover, uncertainties associated with changing weather patterns or unforeseen atmospheric conditions may introduce errors into the estimation process. Furthermore, there might be challenges related to sensor calibration drift over time affecting the reliability of wind velocity estimates used in controlling UAVs. These factors need careful consideration when integrating wind estimation into a UAV's control system.

How might advancements in sensor technology impact the effectiveness of these navigation and control techniques?

Advancements in sensor technology have significant implications for enhancing the effectiveness of navigation and control techniques utilized in unmanned aerial vehicles (UAVs). Improved sensors with higher precision measurements can provide more accurate data inputs essential for state estimation algorithms like Kalman filters used in this context. For instance: Higher resolution IMUs: Advanced inertial measurement units (IMUs) with reduced noise levels can enhance attitude determination accuracy. Lidar-based Wind Sensors: Lidar-based systems offer precise measurements aiding better estimations leading to improved stability during flight. Multi-Sensor Fusion: Integration with multiple sensors like GPS receivers along with vision-based systems enables redundancy ensuring robustness against individual sensor failures. Real-time Data Processing: Faster processors enable quicker data processing facilitating rapid updates improving responsiveness during dynamic flight scenarios. Overall, advancements in sensor technology contribute towards increased reliability, efficiency, and safety by providing more reliable information crucial for effective navigation planning and precise aircraft maneuvering capabilities required by modern autonomous drones operating under various conditions.
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