Neural ODE Controller Design for Dynamical System Analysis
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Solution Overview
Problem
Existing techniques for modeling dynamical systems using deep neural networks face challenges in identifying transfer functions due to reliance on brute force approximation, making it difficult to analyze and design controllers effectively.
Innovation Solution
The use of neural ordinary differential equations (NODEs) as a controller for dynamical systems, where a NODE model is trained using measurement and control data to provide analytically tractable building blocks, allowing for easier parameter extraction and systematic analysis of system properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are used to model dynamical systems, then the system can capture complex non-linear behaviors, but it becomes difficult to identify transfer functions due to reliance on brute force approximation
Solution Approach 1:
The patent segments the neural network into structured components with explicit transfer functions. Instead of treating the network as a monolithic black box, it divides the computation into discrete layers and operations where each component has a known, analyzable transfer function, enabling systematic identification while maintaining modeling flexibility.
Solution Approach 2:
The patent transforms the approach from learning arbitrary complex functions to learning parameters within a structured framework with predefined transfer functions. By changing the parameters of known functional forms rather than approximating unknown functions, the system maintains adaptability while enabling analytical tractability.
2Extent of automation
If traditional neural networks are used for controller design, then they can learn from data, but systematic analysis of system properties becomes difficult
Solution Approach 1:
The patent introduces an intermediary layer between data-driven learning and systematic analysis. The structured NODE architecture with explicit transfer functions serves as a mediator that allows both automated learning from data and formal systematic analysis of system properties, bridging the gap between these two requirements.
Solution Approach 2:
The patent performs preliminary structuring of the neural network before the learning process. By pre-defining the transfer functions and network architecture structure, the system enables subsequent systematic analysis while still allowing data-driven parameter optimization, rather than attempting analysis after arbitrary network structures are learned.
3Adaptability or versatility
If brute force approximation is used to identify transfer functions, then the system can be trained flexibly, but scalability is limited
Solution Approach 1:
The patent employs dynamic structured NODEs where the network architecture and transfer functions can adapt to different dynamical systems while maintaining a consistent structured framework. This dynamic adaptability within structure enables both training flexibility across different applications and scalability through systematic parameter optimization rather than brute force approaches.
Data Source
AI summary
In general, the disclosure describes techniques for characterizing a dynamical system and a neural ordinary differential equation (NODE)-based controller for the dynamical system. An example analysis system is configured to: obtain a set of parameters of a NODE model used to implement the NODE-based controller, the NODE model trained to control the dynamical system; determine, based on the set of parameters, a system property of a combined system comprising the dynamical system and the NODE-based controller, the system property comprising one or more of an accuracy, safety, reliability, reachability, or controllability of the combined system; and output the system property to modify one or more of the dynamical system or the NODE-based controller to meet a required specification for the combined system.