Variable Fidelity Modeling as Applied to Trajectory Optimization for a Hydraulic Backhoe

Modeling, simulation, and optimization play vital roles throughout the engineering design process; however, in many design disciplines the cost of simulation is high, and designers are faced with a tradeoff between the number of alternatives that can be evaluated and the accuracy with which they are evaluated. In this paper, a methodology is presented for using models of various levels of fidelity during the optimization process. The intent is to use inexpensive, low-fidelity models with limited accuracy to recognize poor design alternatives and reserve the high-fidelity, accurate, but also expensive models only to characterize the best alternatives. Specifically, by setting a user-defined performance threshold, the optimizer can explore the design space using a low-fidelity model by default, and switch to a higher fidelity model only if the performance threshold is attained. In this manner, the high fidelity model is used only to discern the best solution from the set of good solutions, so computational resources are conserved until the optimizer is close to the solution. This makes the optimization process more efficient without sacrificing the quality of the solution. The method is illustrated by optimizing the trajectory of a hydraulic backhoe. To characterize the robustness and efficiency of the method, a design space exploration is performed using both the low and high fidelity models, and the optimization problem is solved multiple times using the variable fidelity framework.Copyright © 2009 by ASME

[1]  Christiaan J. J. Paredis,et al.  Modeling Design Concepts under Risk and Uncertainty using Parameterized Efficient Sets , 2008 .

[2]  Holger Giese,et al.  Summary of the workshop on multi-paradigm modeling: concepts and tools , 2006, MoDELS'06.

[3]  G. Hazelrigg.  Systems Engineering: An Approach to Information-Based Design , 1996 .

[4]  Stuart Pugh,et al.  Total Design: Integrated Methods for Successful Product Engineering , 1991 .

[5]  Wei Chen,et al.  Decision Making in Engineering Design , 2006 .

[6]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[7]  Thomas A. Zang,et al.  AIAA 99-3798 Multidisciplinary Design Optimization Techniques: Implications and Opportunities for Fluid Dynamics Research , 1999 .

[8]  John W. Bandler,et al.  Review of the Space Mapping Approach to Engineering Optimization and Modeling , 2000 .

[9]  Christiaan J. J. Paredis,et al.  An agent-based approach to the design of rapidly deployable fault-tolerant manipulators , 1996 .

[10]  John E. Renaud,et al.  CONSTRUCTING VARIABLE FIDELITY RESPONSE SURFACE APPROXIMATIONS IN THE USABLE FEASIBLE REGION , 2000 .

[11]  Christiaan J. J. Paredis.  An Open-Source Modelica Library of Fluid Power Models , 2008 .

[12]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[13]  R. A. Miller,et al.  Sequential kriging optimization using multiple-fidelity evaluations , 2006 .

[14]  Hilding Elmqvist,et al.  Physical system modeling with Modelica , 1998 .

[15]  Panos Y. Papalambros,et al.  Combined Optimal Design and Control With Application to an , 2002 .

[16]  George A. Hazelrigg,et al.  A Framework for Decision-Based Engineering Design , 1998 .

[17]  Ashwin P. Gurnani,et al.  A constraint-based approach to feasibility assessment in preliminary design , 2006, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[18]  J. F. Rodríguez,et al.  Sequential approximate optimization using variable fidelity response surface approximations , 2000 .

[19]  Jacobs Sverdrup,et al.  SYSTEM LEVEL OPTIMIZATION IN THE INTEGRATED HYPERSONIC AEROMECHANICS TOOL (IHAT) , 2003 .

[20]  P. A. Newman,et al.  Approximation and Model Management in Aerodynamic Optimization with Variable-Fidelity Models , 2001 .

[21]  Christiaan J. J. Paredis,et al.  Model-Based Optimization of a Hydraulic Backhoe using Multi-Attribute Utility Theory , 2009 .

[22]  L. Schmit,et al.  Some Approximation Concepts for Structural Synthesis , 1974 .

[23]  P. A. Newman,et al.  Optimization with variable-fidelity models applied to wing design , 1999 .

[24]  Andy J. Keane,et al.  Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[25]  R. Lewis,et al.  An Overview of First-Order Model Management for Engineering Optimization , 2001 .

[26]  Ning Qin,et al.  Variable-fidelity aerodynamic optimization for turbulent flows using a discrete adjoint formulation , 2004 .

[27]  Yoji Akao,et al.  Quality Function Deployment : Integrating Customer Requirements into Product Design , 1990 .

[28]  B. Grossman,et al.  Variable-complexity response surface approximations for wing structural weight in HSCT design , 1996 .

[29]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox , 2002 .

[30]  Wolfgang Beitz,et al.  Engineering Design: A Systematic Approach , 1984 .

[31]  A. Belegundu,et al.  Optimization Concepts and Applications in Engineering , 2011 .

[32]  Robert T. Hays,et al.  Simulation Fidelity in Training System Design: Bridging the Gap Between Reality and Training , 1988 .

[33]  Kyriakos C. Giannakoglou,et al.  Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence , 2002 .

[34]  Andrew P. Sage,et al.  Introduction to systems engineering , 2000 .

[35]  H. Raiffa,et al.  Decisions with Multiple Objectives , 1993 .

[36]  Kok Wai Wong,et al.  Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems , 2005 .

[37]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[38]  Deborah L Thurston,et al.  A formal method for subjective design evaluation with multiple attributes , 1991 .

[39]  Michael Tiller,et al.  Introduction to Physical Modeling with Modelica , 2001 .

[40]  高正红,et al.  APPLICATION OF VARIABLE-FIDELITY MODELS TO AERODYNAMIC OPTIMIZATION , 2006 .

[41]  John E. Renaud,et al.  Multilevel Variable Fidelity Optimization of a Morphing Unmanned Aerial Vehicle , 2004 .

[42]  Kemper Lewis,et al.  Feasibility Assessment in Preliminary Design Using Pareto Sets , 2005, DAC 2005.

[43]  François E. Cellier,et al.  Continuous system modeling , 1991 .

[44]  Ronald A. Howard,et al.  Decision analysis: practice and promise , 1988 .