Adaptive Flight Control Using Prediction Models and Sensor Feedback
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Solution Overview
Problem
Existing aerial vehicles face instability in flight due to inexperienced operator control, defects, or external environmental factors, leading to deviations from predetermined flight paths.
Innovation Solution
A flight control apparatus that collects flight parameters using a prediction model and sensors to adjust flight control, updates the model based on actual flight data, and detects defects to ensure stable flight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the aerial vehicle is controlled by a manipulation device, then the operator can directly control the flight, but the aerial vehicle cannot fly according to a predetermined flight path due to inexperienced operation
Solution Approach 1:
The flight control apparatus continuously compares the actual flight state (obtained from sensors) with the predicted flight state (from the prediction model) and uses this feedback to generate corrected control signals. This closed-loop feedback mechanism compensates for operator inexperience and ensures adherence to the predetermined flight path.
Solution Approach 2:
The prediction model acts as an intermediary between the manual control input and the actual flight control. It predicts the expected flight state based on operation information and sensor data, then compares this prediction with actual sensor measurements to generate corrected control signals, thereby mediating the control process to improve flight path adherence.
2Reliability
If the aerial vehicle is controlled by an autonomous control system, then the flight can be more stable, but stable flight cannot be guaranteed due to defects or external environmental factors
Solution Approach 1:
The prediction model is dynamically updated during flight operations by comparing predicted flight states with actual sensor measurements. This dynamic adaptation allows the control system to adjust to changing environmental conditions and potential defects, maintaining flight stability while improving responsiveness to unexpected situations.
Solution Approach 2:
The flight control apparatus performs self-diagnosis and self-correction by continuously comparing predicted versus actual flight states. When discrepancies are detected, the system automatically updates the prediction model and adjusts control signals without external intervention, enabling the system to maintain stability and adapt to defects autonomously.
3Reliability
If a prediction model is used to control flight, then flight control performance can be improved, but the model requires continuous updating based on actual flight data
Solution Approach 1:
The prediction model is continuously updated throughout flight operations by comparing predicted flight states with actual sensor measurements. This continuous learning process ensures the model remains accurate and adaptive to current flight conditions, maintaining high control performance without requiring separate calibration phases.
Solution Approach 2:
The system implements a feedback loop where actual sensor measurements are continuously compared with prediction model outputs. This feedback drives automatic model updates, ensuring the prediction model adapts to actual flight characteristics while maintaining improved control performance. The feedback mechanism integrates seamlessly with the control process.
Data Source
AI summary
Disclosed are a flight control apparatus for an aerial vehicle and a method of operating the same. The flight control apparatus includes a communication circuit that establishes communication with a control system and receive first information related to operation information for controlling flight of an aerial vehicle from the control system, a sensor that obtains second information related to flight data of the aerial vehicle, a memory that stores a prediction model for predicting a flight state of the aerial vehicle based on the first information and the second information, and a flight parameter estimation value including at least one of a landing gear spring coefficient, an aerodynamic coefficient, a friction coefficient, an control surface effect, a thrust coefficient, or inertia moment or any combination thereof or any combination thereof, and a processor that is electrically connected to the communication circuit, the sensor, and the memory.