Autonomous Device Control System Real-Time Mass Inertia Measurement
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Solution Overview
Problem
Existing control systems for autonomous or semi-autonomous devices, such as drones, face challenges in accurately and rapidly tuning proportional integral derivative (PID) controllers and signal filters, which are essential for stabilizing these devices against external forces and internal errors, often requiring human input and being unsuitable for real-time autonomous control.
Innovation Solution
A method that involves determining an initial control response based on actual and desired acceleration, incorporating PID and filters, applying mathematical transforms, and fitting parameters to a mathematical model using advanced algebras to derive transfer functions and improve control loop performance, allowing for real-time adjustments and noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed PID parameters are used in control systems, then the control loop is simple to implement, but the system cannot adapt to changing conditions and requires human tuning input
Solution Approach 1:
The control system automatically identifies its own parameters (mass, moment of inertia, drag coefficients) by analyzing sensor data and applying mathematical transforms, eliminating the need for external human tuning. The system serves itself by continuously adapting its control parameters based on real-time measurements of its dynamic characteristics.
Solution Approach 2:
The system dynamically changes control parameters by deriving transfer functions from identified physical parameters. The PID gains and filter parameters are automatically adjusted based on the calculated mass, moment of inertia, and drag coefficients, allowing the system to adapt to changing operating conditions without increasing operational complexity.
2Extent of automation
If human input is required for tuning PIDs and filters, then initial setup is possible, but real-time autonomous adjustment cannot be performed
Solution Approach 1:
The manual tuning process is replaced with an automated computational system that uses mathematical transforms (Fourier, Laplace, wavelet) to identify system parameters and generate optimal control parameters. This substitution of mechanical/human tuning with automated mathematical processing enables real-time autonomous adjustment without time loss.
Solution Approach 2:
The system performs preliminary identification of its dynamic characteristics continuously in the background, maintaining up-to-date transfer functions and parameters ready for immediate control adjustments. This preliminary action ensures that when control tuning is needed, the system can act instantly without requiring human intervention or time for parameter determination.
3Adaptability or versatility
If control parameters are buried in the control loop, then the system is compact, but the parameters are not separable and cannot be improved independently
Solution Approach 1:
The control parameters are segmented into distinct physical components: mass, moment of inertia, drag coefficients, and transfer function parameters. Each parameter is identified and calculated separately through mathematical transforms of sensor data, allowing independent optimization of each parameter while maintaining the compact control loop structure.
Data Source
AI summary
A control system and method for controlling an autonomous or semi-autonomous device. The method includes receiving a command signal representative of a desired acceleration, instructing the device to accelerate according to the desired acceleration, receiving a measurement signal representative of an actual acceleration, determining an initial control response based on the actual acceleration via a prediction model, applying a mathematical transform to the command signal and measurement signal, determining a mathematical model of the device based on the transformed command signal and transformed measurement signal, smoothing parameters of the mathematical model, inverting a transfer function of the mathematical model, updating control responses based on the mathematical model and inverted transfer function, and controlling the device according to the updated control responses. Improved performance of the control system itself and hence improved control of the autonomous or semi-autonomous device is thereby achieved.