Adaptive Autopilot Gain Scheduling Across Velocity, Altitude, and Overload
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Solution Overview
Problem
Existing autopilot control systems for unmanned aerial vehicles (UAVs) and high-tech weapons (HTWs) face challenges in maintaining control quality under varying velocities, altitudes, and overloads, leading to increased miss distance errors and reduced target hit probability due to inadequate gain scheduling and manual controller selection, which is time-consuming and lacks a scientific basis.
Innovation Solution
A novel method for designing adaptive autopilot controllers using a three-loop structure with gain scheduling, involving two inner loops for angular velocity and one outer loop for error control, and employing adaptive control gain sets based on velocity, altitude, and overload, optimized through a two-part process to determine initial and adaptive controllers, using aerodynamic coefficient tables and physical parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the number of operating points is increased to improve control quality across varying flight conditions, then control precision is improved, but design time and computational burden increase exponentially
Solution Approach 1:
The patent implements dynamic gain scheduling where controller gains are automatically adjusted based on real-time flight conditions (velocity, altitude, overload). Instead of manually designing controllers for numerous fixed operating points, the system dynamically adapts gains using scheduling variables, reducing design time while maintaining control quality across varying flight conditions.
Solution Approach 2:
The patent changes the approach from discrete operating point design to continuous parameter-based gain scheduling. By using scheduling variables (velocity, altitude, overload) to continuously adjust controller gains, the system achieves adaptability without requiring exponential increase in design points, thus reducing design time while maintaining control precision.
2Device complexity
If linear interpolation by velocity is used to simplify controller design, then design complexity is reduced, but control quality deteriorates at points far from selected operating points
Solution Approach 1:
The patent extends the gain scheduling from single-dimensional velocity-based interpolation to multi-dimensional scheduling incorporating velocity, altitude, and overload. By adding dimensional parameters, the system maintains control quality across diverse flight conditions while keeping the design methodology systematic and manageable through structured gain tables.
Solution Approach 2:
The patent creates a universal gain scheduling framework that works across multiple flight dimensions (velocity, altitude, overload) rather than being limited to velocity-only interpolation. This multi-functional approach ensures control quality is maintained throughout the entire flight envelope while using a systematic design process that doesn't excessively increase complexity.
3Stability of the object's composition
If manual selection of controllers at operating points is performed to ensure smooth coefficient curves, then control smoothness is improved, but designer workload and selection time increase significantly
Solution Approach 1:
The patent implements self-service through automated gain scheduling algorithms that automatically select and interpolate controller gains based on flight conditions. The systematic approach using scheduling variables and structured gain tables enables the system to self-regulate coefficient smoothness without requiring manual designer intervention, thus maintaining control smoothness while dramatically reducing designer workload.
4Adaptability or versatility
If gain scheduling is implemented to adapt to varying flight conditions, then adaptability is improved, but computational burden increases
Solution Approach 1:
The patent segments the gain scheduling computation by pre-calculating and storing controller gains in structured tables for different flight conditions. During operation, the system only needs to perform table lookups and simple interpolations based on current flight parameters, rather than computing full controller designs in real-time. This segmentation reduces computational burden while maintaining adaptability to varying flight conditions.
Data Source
AI summary
The method for designing an autopilot controller for flying objectives (FOs) that adapts to velocity, altitude, and overload consists of two parts: part I focuses on determining the initial controller, and part II focuses on determining the adaptive controller. This method eliminates transfer function zeros, prevents continuous integration, and limits the control signal to ensure the operational conditions of the control loops. The use of a unified control structure allows for a consistent design and tuning process across all three control channels, thereby generalizing the control problem. The solution ensures strong adaptability across a wide range of flight scenarios for FOs, particularly those operating at high velocities, with high overload, and continuously changing altitudes.