Reduced-Order Time Modeling for Stochastic Delay in Distributed Control
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Solution Overview
Problem
Existing methods fail to adequately account for stochastically distributed time effects, such as jitter, delay, and degradation, in the design of controllers for highly automated or autonomous systems, leading to conservative or inefficient control systems.
Innovation Solution
A method for generating a complexity-reduced time model by decomposing stochastic processes into modes, performing sensitivity analysis, and selecting a subset of modes to create a simplified model suitable for controller design, allowing for real-time implementation and reduced resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full stochastic process modeling is used to account for time effects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The stochastic process is decomposed into multiple modes (e.g., through Karhunen-Loève expansion or similar techniques), where each mode represents a specific temporal pattern of the time effects. This segmentation allows the complex stochastic process to be broken down into manageable components that can be selectively used based on their contribution to the overall system behavior.
Solution Approach 2:
The sensitivity analysis is used to extract and identify the most influential modes from the decomposed stochastic process. By removing less significant modes, the model complexity is reduced while retaining the essential time effect characteristics that most impact controller performance.
2Measurement precision
If extensive simulations are performed to characterize uncertain time effects, then measurement precision is improved, but productivity decreases
Solution Approach 1:
Instead of performing exhaustive simulations with all possible stochastic variations, the method performs simulations using only the selected subset of influential modes. This partial action approach achieves sufficient characterization accuracy for controller design while dramatically reducing the computational burden and simulation time required.
3Reliability
If conservative tuning is applied to account for non-deterministic delays, then reliability is improved, but productivity decreases
Solution Approach 1:
The method transforms the control design approach by changing from conservative fixed-parameter tuning to adaptive parameter selection based on the actual influential modes of the stochastic process. This allows controller parameters to be optimized according to the real impact of time effects rather than assuming worst-case scenarios, improving both reliability and performance.
Data Source
AI summary
A method for generating a complexity-reduced time model of a distributed system. The method includes receiving time data, receiving a control path model and modeling the received time data as a stochastic process in order to obtain an original stochastic process. The method furthermore includes decomposing the original stochastic process into a plurality of modes of the original stochastic process, in order to obtain an approximation of the original stochastic process. The method includes performing a sensitivity analysis using the control path model and the approximation. The method furthermore includes selecting a first subset of modes of the approximation on the basis of the comparison and/or a default value, and outputting a complexity-reduced time model on the basis of the first subset of the modes.