State-Space Model Assembly for Software-Independent Mechatronic Control
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Solution Overview
Problem
Conventional methods for simulating and controlling mechatronic systems require laborious and costly re-engineering when upgrading or extending hardware or software, and are dependent on commercial software licenses, limiting tool and platform independence.
Innovation Solution
A method for assembling a state space model system that specifies input and output parameters, maps them to initial state space models, optimizes by identifying approximately linear regions, and assembles linearized models for tool- and platform-independent simulation and control of mechatronic system components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If commercial software is used for high-fidelity modeling and simulation, then modeling accuracy and simulation fidelity are improved, but software dependency and licensing costs increase
Solution Approach 1:
The patent creates a copy of the commercial software model by extracting its mathematical structure and converting it into a standardized state-space representation. This copy can be executed independently without the original commercial software, eliminating licensing dependencies while preserving the modeling accuracy and simulation fidelity of the original model.
Solution Approach 2:
The patent replaces the mechanical dependency on commercial software execution environments with a mathematical substitution approach. By transforming the commercial model into pure mathematical state-space equations, the system can be simulated using any standard computational platform, substituting the proprietary software mechanism with a universal mathematical framework.
2Measurement precision
If high-fidelity models are upgraded or extended, then system capability and accuracy are improved, but re-engineering effort and costs increase
Solution Approach 1:
The patent segments the model updating process into independent, modular steps: parameter extraction, state-space transformation, and model assembly. Each segment can be performed independently, allowing incremental updates to the model without requiring complete re-engineering. This modular approach reduces complexity when upgrading or extending model capabilities.
Solution Approach 2:
The patent enables model upgrades through parameter changes rather than structural re-engineering. By maintaining a standardized state-space framework, improvements in model accuracy can be achieved by updating parameters, matrices, and coefficients without changing the fundamental model structure, significantly reducing re-engineering effort.
3Productivity
If parallel simulations are run on control units, then system performance and processing speed are improved, but software licensing requirements and dependencies increase
Solution Approach 1:
The patent creates a universal state-space model representation that can execute on any standard control unit or computing platform. The standardized mathematical formulation ensures platform independence, allowing parallel simulations to be distributed across multiple control units without requiring proprietary software licenses, thereby enabling flexible deployment architectures.
Solution Approach 2:
The patent creates portable copies of the simulation model in state-space form that can be deployed independently on multiple control units. These copies eliminate the need for centralized commercial software licenses, enabling parallel execution across distributed systems while maintaining full simulation capability and platform independence.
Data Source
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AI summary
A computer implemented method (100) of assembling a state space model system for controlling a movement of a component of a mechatronic system comprises specifying (S102) a set of input parameters and a set of output parameters for an initial set of state space models, which is determined (S104) by mapping multiple sets of values of the specified (S102) set of input parameters to multiple associated sets of values of the specified (S102) set of output parameters. The determined (S104) initial set of state space models is optimized (S106) by identifying (S106-1) approximately linear regions, which comprise regions in a parameter space of the initial set of state space models, and selecting (S106-2) one state space model per approximately linear region. The parameter space comprises the input parameters and the output parameters. The selected state space models are assembled (S108) for obtaining the state space model system.