Stepper Motor Canard Control for Rolling Projectile Stability
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Solution Overview
Problem
Conventional rolling airframe projectiles become unstable when large canards are used for guidance, and existing stabilization systems are expensive due to the need for servo motors and complex data processing.
Innovation Solution
The use of stepper motors to move canards in discrete steps, combined with a neural network that processes projectile state data and guidance commands to generate control signals for stabilization and guidance, providing a cost-effective alternative to conventional systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large canards are used for guidance, then control capability is improved, but projectile stability deteriorates
Solution Approach 1:
The system dynamically adjusts canard positions in real-time during projectile flight. The neural network continuously processes projectile state data and generates updated canard position commands, allowing the system to adapt to changing flight conditions and maintain stability despite using large canards for enhanced control capability.
Solution Approach 2:
The control system implements feedback by continuously monitoring projectile state (position, velocity, orientation) and using this information to adjust canard positions. The neural network receives projectile state data as input and generates appropriate canard control commands, creating a closed-loop system that maintains stability while utilizing large canards for improved control.
2Measurement precision
If conventional control systems with servo motors are used, then stabilization accuracy is improved, but system cost deteriorates
Solution Approach 1:
The system replaces expensive servo motors with cheaper stepper motors. While stepper motors are generally less precise than servo motors, the neural network compensation and optimized control algorithm achieve sufficient stabilization accuracy. This substitution significantly reduces system cost while maintaining adequate performance for the application.
Solution Approach 2:
The system substitutes the conventional servo motor mechanical control system with a neural network-based control approach using stepper motors. The neural network processes projectile state data and generates optimized stepper motor commands, replacing the need for complex servo mechanisms and their associated expensive components while achieving effective stabilization.
3Device complexity
If stepper motors are used to move canards, then system cost is reduced, but control precision deteriorates
Solution Approach 1:
The neural network is trained in advance with extensive simulation data to learn optimal canard positioning strategies. This preliminary training allows the network to compensate for the lower inherent precision of stepper motors by predicting and correcting for positioning errors, achieving sufficient control precision without requiring expensive high-precision motors.
Solution Approach 2:
The system changes the control parameters by using discrete stepper motor steps rather than continuous servo motor control. The neural network adapts to this discrete control paradigm by learning to generate optimal step sequences and timing, transforming the control approach to match the capabilities of stepper motors while maintaining effective stabilization and guidance performance.
Data Source
AI summary
Rolling airframe projectile guidance and stability systems are disclosed. Flight control surfaces, such as canards and/or tail fins are attached to a projectile airframe that is designed to roll during flight. Stepper motors are attached to the flight control surfaces and move the flight control surfaces in discrete increments. A control system generates signals that control the flight control surfaces. The control system may include a neural network that is trained to generate control signals in response to received inputs.