Adaptive Wind Estimation for sUAS Trajectory and Flight Control
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Solution Overview
Problem
Conventional aerial system control technologies face challenges in providing accurate navigation and control for small unmanned aerial systems (sUASs) due to wind disturbances, especially in complex urban environments, as they assume known and uniform wind fields, and require expensive hardware and computational power for wind estimation and compensation.
Innovation Solution
The development of an adaptive wind estimation and trajectory generation system using onboard sensors like IMUs and rate gyros, which estimates aerodynamic drag coefficients and wind components in real-time, generating feasible trajectories and motor/thrust commands to compensate for wind disturbances without relying on wind sensors or specific hardware maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional adaptive and robust control strategies are used to compensate for wind effects, then tracking accuracy is improved, but the approach cannot handle thrust limits and requires expensive wind sensors or specific hardware
Solution Approach 1:
The system uses the aircraft's own motion data from onboard sensors (IMU, rate gyros) to estimate wind components and aerodynamic drag coefficients. The aircraft serves its own wind sensing needs through adaptive estimation algorithms that process its flight dynamics data, eliminating the need for external wind sensors or specialized hardware maneuvers.
Solution Approach 2:
The patent replaces mechanical wind sensing hardware with computational estimation algorithms. Instead of using physical wind sensors or dedicated hardware maneuvers to measure wind, the system uses adaptive algorithms that process motion data from standard onboard sensors to estimate wind components and drag coefficients computationally.
2Reliability
If accurate wind field measurements are assumed to be available, then trajectory generation accuracy is improved, but this assumption requires expensive measurement units and is not practical for sUAS
Solution Approach 1:
The system introduces an intermediate estimation process that uses readily available motion data from onboard sensors as a mediator to infer wind components. Instead of directly measuring wind with expensive sensors, the algorithm processes intermediate motion measurements (accelerations, angular rates) to derive wind estimates, making accurate trajectory generation accessible to sUAS with standard sensor suites.
3Manufacturing precision
If conventional controllers directly compensate for wind effects, then tracking performance is improved, but the controllers cannot handle thrust limits
Solution Approach 1:
The system performs preliminary wind estimation and trajectory generation that explicitly accounts for thrust limits before execution. By pre-computing feasible trajectories that respect thrust constraints and incorporating wind estimates in advance, the system ensures that tracking commands are both accurate and physically realizable within the aircraft's thrust capabilities.
4Measurement precision
If wind is considered as external disturbance in adaptive control, then control accuracy is improved, but this approach assumes known and uniform wind fields which is not valid in complex urban environments
Solution Approach 1:
The system transitions from static wind field assumptions to dynamic wind estimation. The adaptive algorithms continuously update wind component estimates based on real-time motion data, allowing the system to track and compensate for time-varying and spatially complex wind fields typical of urban environments, rather than assuming uniform steady wind conditions.
Solution Approach 2:
The system implements feedback through adaptive estimation algorithms that continuously process motion data to update wind component and drag coefficient estimates. This closed-loop approach allows the controller to adapt to changing wind conditions in real-time, maintaining accuracy in complex urban environments where wind fields are non-uniform and time-varying.
Data Source
AI summary
Adaptive wind estimation, trajectory generation, and flight control for aerial systems using motion data is provided. The adaptive wind estimation approach may be implemented using onboard computing power, may rapidly converge to true values, may be computationally inexpensive, and may not require any specific hardware or specific vehicle maneuvers for the convergence. There may be no prior knowledge of the wind field, using the motion of the aircraft itself rather than wind sensors. The algorithm may include three blocks. An identification/estimation block may identify aerodynamic drag coefficients in still-air flight and estimate the wind components in moving and variable air flight. A navigation block may generate feasible trajectories, taking into account the estimated wind field. A control block may generate motor/engine thrust commands necessary to track the generated trajectories while compensating for the wind disturbance.