Systematic Identification of Decoupling in Dynamic System Models paper proposes a technique to quantitatively and systematically search for decou

pling among elements of a dynamic system model, and to partition models in which decoupling is found. The method can validate simplifying assumptions based on decoupling, determine when decoupling breaks down due to changes in system parameters or inputs, and indicate required model changes. A high-fidelity model is first generated using the bond graph formalism. The relative contributions of the terms of the generalized Kirchoff loop and node equations are computed by calculating and comparing a measure of their power flow. Negligible aggregate bond power at a constraint equation node indicates an unnecessary term, which is then removed from the model by replacing the associated bond by a modulated source of generalized effort or flow. If replacement of all low-power bonds creates separate bond graphs that are joined by modulating signals, then the model can be partitioned into driving and driven subsystems. The partitions are smaller than the original model, have lower-dimension design variable vectors, and can be simulated separately or in parallel. The partitioning algorithm can be employed alongside existing automated modeling techniques to facilitate efficient, accurate simulation-based design of dynamic systems. DOI: 10.1115/1.2745859

[1]  J. L. Stein,et al.  Fundamental concordances between balanced truncation and activity-based model reduction , 2005 .

[2]  Jeffrey L. Stein,et al.  An Energy-Based Approach to Parameterizing Parasitic Elements for Eliminating Derivative Causality , 2005 .

[3]  Tao Jiang,et al.  Target Cascading in Optimal System Design , 2003, DAC 2000.

[4]  Esref Eskinat,et al.  Model reduction in the physical domain , 2003 .

[5]  Gregory M. Hulbert,et al.  A Physical-Based Model Reduction Metric with an Application to Vehicle Dynamics , 1998 .

[6]  D. Karnopp,et al.  Analysis and Simulation of Planar Mechanism Systems Using Bond Graphs , 1979 .

[7]  Zoran Filipi,et al.  Target cascading in vehicle redesign: a class VI truck study , 2002 .

[8]  Huei Peng,et al.  A Model Accuracy and Validation Algorithm , 2002 .

[9]  Devadatta M. Kulkarni,et al.  Hierarchical overlapping coordination for large-scale optimization by decomposition , 1999 .

[10]  Jaroslaw Sobieszczanskisobieski,et al.  On the sensitivity of complex, internally coupled systems , 1988 .

[11]  Jeffrey L. Stein,et al.  System Partitioning and Improved Bond Graph Model Reduction Using Junction Structure Power Flow , 2005 .

[12]  Panos Y. Papalambros,et al.  Optimal model-based decomposition of powertrain system design , 1995 .

[13]  R. C. Rosenberg,et al.  Power-based model insight , 1988 .

[14]  O. S. Turkay,et al.  Model reduction in the physical domain , 2003, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[15]  Bruce H. Wilson,et al.  An Algorithm for Obtaining Proper Models of Distributed and Discrete Systems , 1995 .

[16]  Jeffrey L. Stein,et al.  Generating Proper Integrated Dynamic Models for Vehicle Mobility Using a Bond Graph Formulation , 2001 .

[17]  Gregory M. Hulbert,et al.  Generating proper dynamic models for truck mobility and handling , 2004 .

[18]  A. Galip Ulsoy,et al.  An Input-Output Criterion for Linear Model Deduction , 1996 .

[19]  John B. Ferris,et al.  Development of proper models of hybrid systems , 1994 .

[20]  Jeffrey L. Stein,et al.  Systematic Model Decoupling Through Assessment of Power-Conserving Constraints: An Engine Dynamics Case Study , 2004 .

[21]  R. Rosenberg,et al.  System Dynamics: Modeling and Simulation of Mechatronic Systems , 2006 .