Migratable Control Function Modeling Across Hardware Frequencies
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Solution Overview
Problem
Existing regulatory functions for technical systems, such as vehicles or manufacturing applications, are often designed for specific hardware environments and fail or become limited if the hardware fails or undergoes disruptions.
Innovation Solution
A procedure for creating a migrable model of a regulatory function that can be transferred to and executed on different technical systems or data processing devices, using stochastic distributions for input data and specified frequencies to model output variables, and employing uncertainty quantification techniques like Monte Carlo simulations or polynomial chaos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a control function is designed for a specific hardware environment with fixed frequency, then the control function can be executed efficiently on that hardware, but the control function becomes unavailable or limited when hardware fails or malfunctions
Solution Approach 1:
The control function is transformed into a migratable model that can execute on multiple different hardware environments. By defining stochastic distributions for input data and frequency parameters, and using uncertainty quantification methods, the model adapts to varying hardware characteristics while maintaining control functionality across different platforms.
Solution Approach 2:
The invention changes the approach from fixed frequency execution to stochastic frequency modeling. By defining frequency as a stochastic parameter with defined distributions rather than a fixed value, the control function can adapt to different hardware clock frequencies and computational capabilities, enabling migration across hardware boundaries.
2Adaptability or versatility
If a control function is designed for specific hardware with predetermined computing power, then the control function can be optimized for that hardware, but the control function cannot be transferred to other technical systems or data processing devices
Solution Approach 1:
Instead of directly porting the control function code between hardware platforms, the invention creates a mathematical model representation (copY) of the control function. This migratable model captures the essential behavior through stochastic distributions and uncertainty quantification, allowing execution on different hardware without direct code translation.
Solution Approach 2:
The migratable model serves as an intermediary between the original control function and the target hardware environment. By introducing stochastic distributions for input data and frequency parameters as intermediate representations, the system bridges the gap between different hardware platforms while maintaining control functionality.
3Reliability
If the control function is executed on the primary hardware system, then the control function operates with full computing resources, but the system lacks backup capability in case of hardware failure
Solution Approach 1:
The migratable model is prepared in advance through uncertainty quantification and stochastic distribution definition, enabling it to be quickly deployed to backup hardware when needed. This preliminary modeling work allows the control function to be migrated without extensive reconfiguration during actual hardware failure scenarios.
Data Source
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AI summary
The invention relates to a method (100) for providing a migrable model (8) of a control function (4) for a technical system (1), comprising the following steps: - providing (101) a control function (4), wherein the control function (4) determines at least one output variable (7) based on input data (5) and a frequency (6) specified by the technical system (1), wherein the specified frequency (6) is specific for a clock frequency of a hardware module (2) of the technical system (1) on which the control function (4) is executed, - defining (102) a stochastic distribution for the input data (5) and the specified frequency (6) based on a characteristic of at least one hardware on which the control function (4) can be executed,- Determining (103) the migrable model (8) of the control function (4) based on the determined stochastic distribution of the input data (5) and the specified frequency (6) using an uncertainty quantification method (9) to model a stochastic distribution of the at least one output variable (7), - Checking (104) whether the stochastic distribution of the at least one output variable (7) satisfies at least one defined control requirement, whereby the migrable model (8) of the control function (4) is provided based on a result of the check (104). The invention further relates to a computer program, a device and a storage medium for this purpose.