Adaptive Vehicle Rotation Control for Variable Inertia Loading
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Solution Overview
Problem
Current vehicle rotation control systems face challenges in accurately managing inertia variations, leading to deviations from ideal rotation characteristics and increased structural loading, which results in heavier and less efficient vehicles due to the need for stronger materials and larger structures to handle unpredictable loads.
Innovation Solution
A process and machine that estimate and incorporate inertia values about the vehicle's axis into rotation control laws using gain scheduling, allowing for more precise control of vehicle rotation by accounting for variations in material weight and distribution, thereby reducing structural requirements and improving maneuverability and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If structural components are strengthened to accommodate predicted loads and prevent structural deformity or fracture, then reliability is improved, but weight increases
Solution Approach 1:
The patent applies dynamics by making the inertia value used in rotation control variable rather than fixed. The control system continuously updates the inertia value based on actual vehicle configuration and load conditions, allowing the control parameters to adapt dynamically. This resolves the contradiction by enabling reliable control without requiring over-engineered static structures, as the system adapts to actual conditions in real-time
Solution Approach 2:
The patent changes the parameter of inertia value from a fixed design constant to a variable parameter that is continuously updated based on sensor data about vehicle configuration and material distribution. This allows the rotation control to accurately reflect actual vehicle conditions, preventing excessive structural loading while maintaining control reliability without increasing weight
2Strength
If dimensions of structural components are increased or denser materials are used to withstand greater loads, then strength is improved, but weight increases
Solution Approach 1:
The system dynamically adjusts rotation control based on real-time inertia values calculated from actual material distribution and vehicle configuration. This prevents unexpected peak loads that would require heavier structures, while maintaining sufficient control authority through adaptive parameter adjustment rather than increased structural mass
Solution Approach 2:
The patent implements feedback by using sensor data about vehicle configuration and material distribution to continuously update the inertia value used in rotation control. This closed-loop approach ensures control actions are appropriately scaled to actual vehicle conditions, preventing excessive structural loading and eliminating the need for overweight structural design margins
3Device complexity
If rotation control does not account for inertia variations, then device complexity is reduced, but manufacturing precision deteriorates due to deviations from ideal rotation characteristics
Solution Approach 1:
The patent changes the inertia parameter from a fixed value to a dynamically updated value based on sensor measurements of vehicle configuration and material distribution. This allows precise rotation control that adapts to actual vehicle conditions without requiring complex mechanical structures, achieving manufacturing precision through software-based parameter adaptation
Solution Approach 2:
The patent replaces complex mechanical precision mechanisms with a computational approach that calculates and applies inertia compensation through control algorithms. This substitutes mechanical complexity with computational processing, achieving precise rotation control through software-based inertia management rather than mechanical precision components
Data Source
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AI summary
A machine and process for control of rotation of a vehicle about an axis of the vehicle is shown. A flight control system includes control laws that control the rotation of the vehicle (100) around the axis (X,Y,Z) of the vehicle. An estimate is derived for an inertia about the axis. The estimated inertia is derived from sensed quantities of material in a component (200,202,204) of the vehicle (100). An inertia gain schedule and filter are added to enhance, using the estimated inertia, the accuracy of the control laws that control the rotation of the vehicle (1200) around the axis (X,Y,Z) of the vehicle.