Hoist Flight Director Control for Helicopter Load Stabilization
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Solution Overview
Problem
Current flight directors for helicopters are not effective in stabilizing loads suspended from a hoist, particularly during operations like search and rescue missions, where swinging due to winds, ocean waves, and vehicle acceleration occurs, necessitating manual pilot intervention.
Innovation Solution
A system utilizing an imaging sensor, controller, and flight control system to capture load movement, determine swing data, and adjust aircraft flight controls based on machine learning algorithms to stabilize suspended loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current flight directors are used to control helicopter flight, then the helicopter can maintain absolute position or hover at low speed, but the system cannot effectively stabilize suspended loads during hoisting operations
Solution Approach 1:
The flight director mode dynamically adapts by transitioning from static position-holding control to dynamic load-stabilization control. The system continuously monitors load swing data and adjusts flight control commands in real-time to counteract swinging, making the control system flexible and adaptive to hoisting operations rather than relying on fixed absolute position coordinates.
Solution Approach 2:
The system implements a feedback mechanism by detecting load swing data through imaging sensors and using this information to generate corrective flight control commands. The load stabilization mode continuously receives feedback on load position and swing velocity, then adjusts helicopter flight accordingly to minimize swinging, creating a closed-loop control system that improves reliability during hoisting.
2Reliability
If pilots manually stabilize suspended loads during hoisting, then load stability can be improved, but pilot workload increases significantly
Solution Approach 1:
The system enables self-service by implementing automatic load stabilization through the flight control system. The helicopter automatically detects load swing through imaging sensors and generates corrective flight commands without pilot intervention. The system serves itself by using its own sensors and control mechanisms to stabilize the load, eliminating the need for manual pilot effort while maintaining load stability.
Solution Approach 2:
The system replaces manual mechanical control with automated sensor-based control. Instead of relying on pilot mechanical input through flight controls, the system uses imaging sensors to detect load swing and automatically generates flight control commands, substituting the mechanical pilot-in-the-loop system with an automated sensing and control system that reduces pilot workload.
3Extent of automation
If imaging sensors and machine learning modules are added to detect and analyze load swing, then automation and precision are improved, but device complexity increases
Solution Approach 1:
The imaging sensor serves multiple functions: it captures images for load position detection, provides data for swing velocity calculation, and supports both visualization and control functions. By making the imaging sensor multi-functional, the system achieves high automation without proportionally increasing complexity, as one component performs multiple critical roles in the load stabilization process.
Solution Approach 2:
The swing data calculation module acts as an intermediary that processes raw imaging sensor data and transforms it into meaningful load position and velocity information. This intermediary layer simplifies the overall system architecture by creating a clear data flow from sensing to control, making the complex automation process more manageable and easier to implement through modular processing steps.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Automatically stabilizes swinging loads by predicting and counteracting movement, reducing pilot workload and enhancing safety and efficiency in hoisting operations.
Implementation Method 1
an imaging sensor configured to capture images of a swinging of a load suspended from the hoist
Implementation Method 2
the controller may be configured to utilize a machine learning module to determine the positions
Implementation Method 3
the swing data may include a pendulum period of the load based on the plurality of time stamps
Data Source
AI summary
A system and method are disclosed and may be used for controlling (e.g., stabilizing) a suspended load. The system may include a hoist, an imaging sensor configured to capture images of a swinging of a load suspended from the hoist, and a controller. The controller may include one or more processors configured to execute a set of program instructions stored in a memory. The program instructions may be configured to cause the one or more processors to receive the images, determine positions of the load over time based on the images, calculate swing data of the swinging of the load based on the positions, determine flight control commands based on the swing data, and direct a flight control system configured to adjust a flight of an aircraft based on the flight control commands.