Optimization of Automotive Control Parameters with FRONTIER GT-SUITE User’s Conference 2001 1 Agenda n Introduction of FRONTIER n Optimization of Control Parameters Ø GT-Power+Simulink Co-simulation Model l Optimize PID control gain: Engine Speed Feedback Control GT-SUITE User’s Conference 2001 2 Introduction History of FRONTIER n EU Project: 1996 Ø Project Name: FRONTIER l British Aerospace Systems, Daimler Chrisler Aerospace, Zanussi-Electrolux Ø For the development of Airbus, design optimization software for multi-objective was developed Ø In 1998 FRONTIER v.1.0 as an academic software n ES.TEC.O Ø Founded in 1999 with the participation of: l ENGIN SOFT: an engineering company active in numerical simulation since 1984 l Three researchers strong back ground in Information Technology, Optimisation Techniques and Numerical Analysis Ø In 2000 FRONTIER v.2.0 released as a commercial product Ø In 2001 FRONTIER v.2.4 released worldwide GT-SUITE User’s Conference 2001 3 Introduction Features of FRONTIER(1) n Allows you to optimize real Multi Objective Problems Ø Only FRONTIER in the market Ø MOGA (Multi Objective Genetic Algorithm) l Extended conventional GA for Multi Objective l 10 times faster than conventional GA n Supports you to make your decision Ø MCDM Multi Criteria Decision Making l Define your sense of value based on Pareto Frontier l Create a utility function as an objective to be maximized for searching for your preference solution Ø Allows you to review and standardize the optimization criteria n Conjunction: MOGA and Gradient based approach Ø SIMPLEX, BFGS: search for your preference solution GT-SUITE User’s Conference 2001 4 Introduction Features of FRONTIER(2) n Response Surface Modeling Ø Linear/Quadratic Polynomials, Exponential Functions Ø Gaussian Process, Neural Network, K-Nearest, Kriging n Platform Free Ø Java language n Application Free Ø Input and Output: ASCII text file Ø Executable with batch mode n Distributed Computation Ø Networking Ø Optimization of Multi-disciplinary Problem l Heterogeneous applications executed in parallel on Network GT-SUITE User’s Conference 2001 5 Optimization Example n Optimization of Control Parameters Ø GT-Power+Simulink Co-simulation Model l Objectives ² Engine Speed Deviation: Minimize ² Settling Time: Minimize ² Throttle Angle Fluctuation: Minimize l Design Parameters ² PID control gain of Engine Speed Feedback Control GT-SUITE User’s Conference 2001 6 Automotive Control System Subject of Software Development n Control Algorithm Ø Design Tools in the Market: Already in use for mass-production including automatic code generation l MATLAB/Simulink, Stateflow l MATRIXx l ASCET-SD etc. n Control Parameters Ø Tuned using Calibration Tools l Tuning for each target system l Automatic Tuning: Auto-Bench, Auto-CDM l Based on Experiences l Wisdom of Calibration Engineers Subject Design of Control Parameters GT-SUITE User’s Conference 2001 7 Automotive Control System Development Flow Co-simulation provides efficient Functional Development ITE m /si AB TL MA TE UI -S GT Idea uli nk System Design The Subject Parameter Tuning with Vehicles Control Functions Development & Design H In ardw the ar Lo e op U -S GT Hardware Performance System Evaluation Functional Evaluation Software Design Software Debug Implementation to ECU GT-SUITE User’s Conference 2001 8 Design of Control Parameters Multiple Co-simulation GT-SUITE GT-SUITE Hardware Hardware Performance Performance Co-Sim. Efficient Functional Develop. Simulink Simulink Control Control Algorithm Algorithm Co-Sim. FRONTIER FRONTIER Optimization Optimization Design of Parameters (From “Tuning” to “Design”) Parameters Parameters Optimization: Optimization: Nonlinear, Nonlinear, Discrete, Discrete, Multi Multi Objective Objective ONLY ONLY by by FRONTIER FRONTIER GT-SUITE User’s Conference 2001 9 Automotive Control System Development Flow with FRONTIER Idea n tio k iza R lin t i m TIE mu O p ON /si FR AB TL MA TE UI -S GT Hardware Performance Design Algorithms and Parameters by Optimization Parameter Evaluation System Design Control Functions Development & Design “Tuning” to “Evaluation” System Evaluation Functional Evaluation Software Design Software Debug Implementation to ECU GT-SUITE User’s Conference 2001 10 Construction of Multiple Co-simulation n Optimize GT-SUITE+Simulink Co-simulation Model Ø Activate GT-SUITE Model via Simulink l Optimize parameters by FRONTIER l Open/Closed Loop, Dynamic Control Parameters l GT-SUITE Attributes Simulink Simulink GT-SUITE GT-SUITE S-Function (( .mdl) .mdl) INPUT OUTPUT INPUT OUTPUT M-File ( .m) Logfile Dat-File ( .dat) Out-File ( .out) TRN-File ( .trn) FRONTIER FRONTIER (MOGA) (MOGA) MOGA: MOGA: Multi Multi Objective Objective Genetic Genetic Algorithm Algorithm GT-SUITE User’s Conference 2001 11 Problem Definition n Throttle Angle Control Ø Engine Speed Feedback Control Ø Optimize PID Control Gains Ø GT-Power Example Model l genset.mdl/gtm ( Refer to %GTIHOME%\GTpower\v5.1.0\examples\simulink ) Ø Evaluation Function l Amplitude T IntDev = ∫ Ntrg − Ne dt 2 0 T l Settling Time IntTime = Tover dt ∫ 0 Ntrg : Target Engine Speed Ne : Engine Speed 2 Tover : Period while Ntrg − Ne ≥ Prescribed Value GT-SUITE User’s Conference 2001 12 GT-Power+Simulink Co-simulation Model Throttle Angle Engine Speed Feedback Controller Target Speed GT-Power PID Control Load Generation Simple PID Controller Torque=f(Ne) Evaluation Function GT-SUITE User’s Conference 2001 13 GT-Power Model Throttle Valve Torque Generator Engine Speed Throttle Angle GT-SUITE User’s Conference 2001 14 FRONTIER Optimization Flow PID Control Parameters Activate Simulink GT-SUITE User’s Conference 2001 Objectives 15 MOGA Optimization History(1) PID Gains Integral Gain: Ki Proportional Gain: Kp Differential Gain Kdn: Numerator, Kdd: Denominator GT-SUITE User’s Conference 2001 16 MOGA Optimization History(2) Evaluation Functions IntTime IntDev GT-SUITE User’s Conference 2001 17 MOGA Optimization History(3) Se arc hin gD ire cti on IntTime Distribution of Evaluation Function Values Pareto Pareto IntDev Distributed in complex form ² Very difficult to manually search for an optimum combination of PID Control Gains GT-SUITE User’s Conference 2001 18 Optimization Result (Pursued only Response Speed) Original Optimized Engine Speed Throttle Angle (Thv) Load Fluctuation of Throttle Angle Due to pursuit of Response Speed using simple PID Logic = Countermeasures = >> Improve PID Logic >> Optimize parameters applying an additional objective to minimize the fluctuation GT-SUITE User’s Conference 2001 19 Re-Optimization Minimize Fluctuation of Throttle Angle n Add an Objective without Logic Modification l Evaluation Function IntDThv = T ∫0 dThv / dt dt Add Constraints Add an Evaluation Function Add an Objective GT-SUITE User’s Conference 2001 20 IntDThv IntDThv Searching for Pareto by MOGA Pareto Pareto IntDev IntTime IntTime Pareto IntDev GT-SUITE User’s Conference 2001 21 Integrate Objectives by MCDM MCDM: Multi Criteria Decision Making n Create an Objective integrating original Objectives based on the results of MOGA Ø Fine Optimization using SIMPLEX GT-SUITE User’s Conference 2001 22 Optimization History SIMPLEX IntDev IntTime Optimum IntDThv GT-SUITE User’s Conference 2001 23 Optimization Result Original Optimized(1) Optimized(2) Engine Speed 3.3s 2.3s Throttle Angle GT-SUITE User’s Conference 2001 24 Conclusion n Multi Objective Problems can be solved Ø Easy to use ² No need: Particular knowledge Ø MOGA: Robust Optimizer Extraordinary Skill ² Easily apply to daily work Ø Reliable default settings n Easy to make decision Ø Operate MCDM naturally Ø Visualize designer’s sense of value ² Review the sense of value ² Utilize MCDM to put “taste” on n Optimize complex problems Ø Multi-disciplinary Ø Multiple Co-simulation ² Applying to Control Fields greatly parameter setting improves development efficiency ² GT-SUITE+Simulink+FRONTIER is the best choice GT-SUITE User’s Conference 2001 25
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