Open Access Open Access  Restricted Access Subscription or Fee Access

Multi-Objective Pareto Robust Design of PID Controllers for Variable Compression Ratio Engines Using Genetic Algorithms


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irea.v6i6.16999

Abstract


Modern automobile engines must satisfy the challenging and often conflicting goals of minimizing exhaust emissions, providing increased fuel economy and satisfying driver performance requirements over a wide range of operating conditions. An innovative mechanical design approach to achieve these goals has been the development of variable compression ratio (VCR) engines. A challenge with this new engine design is that the compression ratio has a direct influence on the output torque. This is a problem because the engine driver can feel it as a jerk in the movement. Therefore, in order to fully take the advantage of benefits of this engine design it is necessary to have some sort of torque control that can keep a constant torque regardless of changes in compression ratio. In this work a control-oriented engine model is developed to represent a variable compression ratio engine over a range of operating conditions. The model is validated with some available experimental data. Based on the mode, a robust PID controller is optimally designed to control the output torque for the step changes of compression ratio in the presence of parametric uncertainties.
Copyright © 2018 Praise Worthy Prize - All rights reserved.

Keywords


VCR Engine; Torque Modeling; Robust Control; Multi-Objective Optimization

Full Text:

PDF


References


Y. Nilsson, L. Eriksson, M. Gunnarsson, A model for fuel optimal control of a spark-Ignited variable compression engine, Number 2006-01-0399, SAE Congress, Detroit, USA, 2006.
https://doi.org/10.4271/2006-01-0399

E. Sher, T. Bar-Kohany, Optimization of Variable Valve Timing for Maximizing Performance of an Unthrottled Sl Engine-A Theoretical Study, Energy, Vol. 27, n. 8, pp. 757-775, 2002.
https://doi.org/10.1016/s0360-5442(02)00022-1

O. Kultar, H. Arslan, A. Calik, Methods to Improve Efficiency of Four Stroke Spark Ignition Engines at Part load, Energy Conversion & Management, Vol. 46, pp.3202-3220, 2005.
https://doi.org/10.1016/j.enconman.2005.03.008

E. Moses, A. Yarin, Y. Pinhas Bar, On Knocking Prediction in Spark Ignition Engines, Combustion and Flame, Vol. 101, n.3 , pp. 239-261, 1995.
https://doi.org/10.1016/0010-2180(94)00202-4

H.Bayraktar, Theoretical Investigation of Flame Propagation Process in an SI Engine Running on Gasoline-Ethanol Blends, Renewable Energy, Vol. 33, n. 5, pp. 758-771, 2007.
https://doi.org/10.1016/j.renene.2006.03.017

B. Porter, A.H. Jones, Genetic tuning of digital PID controllers, Electronic Letters, Vol. 28, n. 9, pp. 843-844, 1992.
https://doi.org/10.1049/el:19920533

D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, 1989).

W. A. Wolovich, Automatic Control Systems Saunders (College Publishing, Harcourt Brace College Pub. 1994, Orlando, USA).

L. G. Crespo, Optimal performance, robustness and reliability base designs of systems with structured uncertainty, Proc. of American Control Conf, Colorado, USA, 2003, pp. 4219-4224.
https://doi.org/10.1109/acc.2003.1240498

A. Herreros, E .Baeyens, J.R. Persan, A genetic algorithm for multi objective robust control design, Engineering Application of Artificial Intelligence, Vol. 15, pp. 285-301, 2002.
https://doi.org/10.1016/s0952-1976(02)00036-2

Hajiloo, A., Nariman-zadeh, N., Jamali, A. Bagheri, A. Alasti, A., Gamification Procedure Based on Real-Time Multibody Simulation, (2008) International Review of Mechanical Engineering (IREME), 1 (6), pp. 259-266.

A. Bergström, Torque Modeling and Control of a Variable Compression Ratio Engine, Master thesis, Linköpings University, Linköping, 2003.

J. B. Heywood, Internal Combustion Engines Fundamentals (McGraw-Hill Company, 1988).

E. Hendricks, A. Chevalier, M. Jensen, S. Sorenson, Modeling of the Intake Manifold Filling Dynamics, SAE Paper 960037, 1996.
https://doi.org/10.4271/960037

D. Lim, Y.S. Ong, B.S. Lee, Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty, GECCO’ 05, Washington.USA, 2005, pp. 55-62.
https://doi.org/10.1145/1102256.1102266

M. Papadrakakis, N.D. Lagaros, V. Plevris, Structural optimization considering the probabilistic system response, Theoretical Appl. Mech., Vol. 31, pp. 361-393, 2004.
https://doi.org/10.2298/tam0404361p

B.A. Smith, S.P. Kenny, L.G. Crespo, Probabilistic Parameter Uncertainty Analysis of Single input Single Output Control Systems, NASA report, TM-2005-213280, 2005.

L. Ray, R. F. Stengel, A Monte Carlo Approach to the Analysis of Control System Robustness, Automatica, , Vol. 29, n.1, pp. 229-236, 1993.
https://doi.org/10.1016/0005-1098(93)90187-x

Z. Kang, Robust design of structures under uncertainties, PhD. Dissertation, University of Stuttgart, 2005.

Q. Wang, R.F. Stengel, Searching for Robust Minimal-order Compensators, Journal of Dynamic Systems, Measurement, and Control, Vol.123, pp. 223-236, 2001.
https://doi.org/10.1115/1.1367270

M. H. Kalos , P.A. Whitlock, Monte Carlo Methods (Wiley, New York, 1986).

N. Nariman-Zadeh, K. Atashkari, A. Jamali, A. Pilechi, X. Yao, Inverse modeling of multi-objective thermodynamically optimized turbojet engine using GMDH-type neural networks and evolutionary algorithms, Engineering Optimization, Vol. 37, n. 26, pp. 437-462, 2005.
https://doi.org/10.1080/03052150500035591

K. Atashkari, N. Nariman-zadeh, A. Jamali, A. Pilechi, X. Yao, Thermodynamic Pareto optimization of turbojet using multi-objective genetic algorithm, International Journal of Thermal Science, Vol. 44, n. 11, pp. 1061-1071, 2005.
https://doi.org/10.1016/j.ijthermalsci.2005.03.016

C.A. Coello Coello, A.D. Christiansen, Multiobjective optimization of trusses using genetic algorithms, Computers & Structures, Vol. 75, pp. 647-660, 2000.
https://doi.org/10.1016/s0045-7949(99)00110-8

A. Toffolo, E. Benini, Genetic Diversity as an Objective in Multi-objective evolutionary Algorithms, Evolutionary Computation .MIT Press, 2003.
https://doi.org/10.1162/106365603766646816

K. Deb , S. Agrawal, A. Pratap, T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. On Evolutionary Computation, Vol. 6, n. 2, pp. 182-197, 2002.
https://doi.org/10.1109/4235.996017

N. Nariman-zadeh, A. Darvizeh, A. Jamali, A. Moeini, Evolutionary Design of Generalized Polynomial Neural Networks for Modelling and Prediction of Explosive Forming Process, Journal of Material Processing and Technology, Vol. 164, pp. 1561-1571, 2005
https://doi.org/10.1016/j.jmatprotec.2005.02.020


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize