Flatness-based adaptive fuzzy control of spark-ignited engines

• Autorzy: Rigatos G.G. , Siano P.

Identyfikatory

DOI

10.1515/jaiscr-2015-0011

• Języki publikacji: EN

Abstrakty

EN

An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system’s model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system’s parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.

Słowa kluczowe

EN

adaptive fuzzy controller; spark-ignited engines; SI engines; performance criterion; neuro-fuzzy network; neuro-fuzzy approximator; Lyapunov stability analysis; simulation experiment

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 4
• Strony: 231--242
• Opis fizyczny: Bibliogr. 45 poz., rys.

Twórcy

autor

Rigatos G.G.

• Unit of Industrial Automation, Industrial Systems Institute, 26504, Rion Patras, Greece

autor

Siano P.

• Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy

Bibliografia

• [1] S. Sugihira and H. Ohmori, Model-based starting control of SI engines via adaptive feedback linearization, SICE Annual Conference 2008, The University of Electro-Communications, Japan, Aug. 2008.
• [2] J. Zhang, T. Shen and R. Marino, Model-based cold-start speed control scheme for spark ignition engines, Control Engineering Practice, Elsevier, vol. 18, pp. 1285-1294, 2010.
• [3] Abid Ali and Jan P. Blath, Nonlinear Torque Control of a Spark-Ignited Engine, Proc. of the 2006 American Control Conference Minneapolis, Minnesota, USA, June 2006
• [4] Z. Zhang and Z. Sun, Rotational Angle Based Pressure Control of a Common Rail Fuel Injection System for Internal Combustion Engines, 2009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA, June 2009.
• [5] T. Leroy, J. Chauvin and N. Petit, Motion planning for experimental air path control of avariablevalve-timing spark ignition engine, Control Engineering Practice, Elsevier, vol. 17, pp. 14321439,2009.
• [6] P. Moulin and J. Chauvin, Modeling and control of the air system of a turbocharged gasoline engine, Control Engineering Practice, vol. 19, pp. 287297, 2011.
• [7] O. Fl¨ardh, G. Ericsson, E. Klingborg and J. Martensson, Optimal Air Path Control During Load Transients on a Spark Ignited Engine With Variable Geometry Turbine and Variable Valve Timing, IEEE Transactions on Control Systems Technology, 2014.
• [8] A. Kurylowicz, I. Jaworska and S.G. Tzafestas, Robust Stabilizing Control : An Overview, Applied Control : Current Trends and Modern Methodologies (S.G. Tzafestas ed.), Marcel Dekker, pp. 289-324, 1993.
• [9] L. Lublin and M. Athans, An experimental comparison of and designs for interferometer testbed, Lectures Notes in Control and Information Sciences: Feedback Control , Nonlinear Systems and Complexity, (Francis B. and Tannenbaum A., eds.),Springer, pp. 150-172, 1995.
• [10] J.C. Doyle, K. Glover, P.P. Khargonekar and B.A. Francis, State-Space Solutions to Standard H2 and H# Control Problems, IEEE Transactions on Automatic Control, vol. 34, pp. 831-847, 1989.
• [11] L.X.Wang, A course in fuzzy systems and control, Prentice-Hall, 1998.
• [12] R.J. Wai and J.M. Chang, Implementation of Robust Wavelet-Neural-Network Sliding-Mode Control for Induction Servo Motor Drive, IEEE Transactions on Industrial Electronics, vol. 50, no.6, pp. 1317-1334, 2003.
• [13] H.N. Nounou and H. Rehman, Application of adaptive fuzzy control to AC machines, Applied Soft Computing, Elsevier, vol. 7, no.3, pp. 899-907, 2007.
• [14] Y.J. Lin and W. Wang, Adaptive fuzzy control for a class of uncertain non-affine nonlinear systems, Information Sciences, Elsevier, vol. 177, pp. 3901-3917, 2007.
• [15] R. Qi, G. Tao, C. Tan and X. Yao, Adaptive control of discrete-time state-space TS fuzzy systems with general relative degree, Fuzzy Sets and Systems, Elsevier, vol. 217, pp. 2240, 2013.
• [16] Y. Yang, C. Zhou, and X. Jia, Robust adaptive fuzzy control and its application to ship roll stabilization, Information Sciences, Elsevier, vol. 142, pp. 177194, 2002.
• [17] S. Tong, H-X. Li and G. Chen, Adaptive Fuzzy Decentralized Control for a Class of Large-Scale Nonlinear Systems, IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, vol. 34, no.1, pp. 770-775, 2004.
• [18] G. Rigatos and Q. Zhang, Fuzzy model validation using the local statistical approach, Fuzzy Sets and Systems, Elsevier, vol. 60, no.7, pp. 882-904, 2009.
• [19] M. Bassevile and I. Nikiforov, Detection of abrupt changes: Theory and Applications, Prentice-Hall, 1993.
• [20] R. Sepulveda, O. Montiel, O. Castello and P. Melin, Embedding a high-speed interval type-2 fuzzy controller for a real plant into a FPGA, Applied Soft Computing, Elsevier, vol. 12, no. 3, pp. 988-998, 2012.
• [21] A. Ballachi, L. Benvenuti, M.D. di Benedetto and A. Sangiovanni-Vincentelli, The design of dynamical observers for hybrid systems: theory and application of an automotive control problem, vol. 49, no. 4, pp. 915-925, 2013.
• [22] J. Blake Vance, B.C. Kaul, S. Jagannathan and J.A. Drallmeier, Output feedback controller for operation of spark ingition engines at lean conditions using neural networks, IEEE Transactions on Control Systems Technology, vol. 16, no.2, pp. 214-227,2008
• [23] C. Alipi, C. de Russion and V. Piuri, A neural network-based control solution to air-fuel ratio control for automotive fuel injection systems, IEEE Transactions on Systems, Man and Cybernetics -Part C: Applications and Reviews, vol. 33, no.2, pp. 259-268, 2003.
• [24] G. Colin, Y. Chamuillard, G. Bloch and G. Corde, Neural control of fast nonlinear systems: Application to a turbocharged SI engine with VCT, IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 1101-1114, 2007.
• [25] A. Nguyen, J. Lauber and M. Dambrine, Multiobjective control design for turbo-charged spark ignited air system: a switching Takagi-Sugeno model approach, 2013 American Control Conference, Washington DC, USA, 2013.
• [26] G.G. Rigatos, Adaptive fuzzy control with output feedback for H# tracking of SISO nonlinear systems, International Journal of Neural Systems, World Scientific, vol. 18, no.4, pp. 1-16, 2008
• [27] G.G. Rigatos, A differential flatness theory approach to observer-based adaptive fuzzy control of MIMO nonlinear dynamical systems, Nonlinear Dynamics, Springer. 2014.
• [28] G.G. Rigatos and S.G. Tzafestas, Adaptive Fuzzy Control for the Ship Steering Problem, Journal of Mechatronics, Elsevier, vol. 16, no. 6, pp. 479-489, 2006.
• [29] G.G. Rigatos, Adaptive fuzzy control for nonlinear dynamical systems based on differential flatness theory, IET Control Theory and Applications, vol. 6, no. 17, pp. 2644 2656, 2012.
• [30] G.G. Rigatos, Adaptive fuzzy control of DC motors using state and output feedback, Electric Power Systems Research, Elsevier, vol. 79, no.11, pp. 1579-1592 2009.
• [31] H. Yue and J. Li, Output-feedback adaptive fuzzy control for a class of nonlinear time-varying delay systems with unknown control directions, IET Control Theory and Applications, vol. 6, pp. 1266-1280, 2012.
• [32] H.A.Yousef, M. Hamdy and M. Shafiq, Flatnessbased adaptive fuzzy output tracking excitation control for power system generators, Journal of the Franklin Institute, vol. 350, pp. 2334353, 2013.
• [33] Y.W. Cho, C.W. Park, J.H. kim and M. Park, Indirect model reference adaptive fuzzy control of dynamic fuzzy-state space model, IET Proc. on Control Theory and Applications, vol. 148, no.4, pp. 273-282, 2005.
• [34] G. Rigatos and A. Al-Khazraji, Flatness-Based Adaptive Fuzzy Control for MIMO Nonlinear Dynamical Systems, in: ”Nonlinear Estimation and Applications to Industrial Systems Control”, Nova Publications, 2011.
• [35] Y.S. Huang, D.Q. Zhou, S.P. Xiao and D. Ling, Coordinated decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems with strong interconnections, IET Control Theory and applications, vol. 3, no.9, pp. 1261-1274, 2009.
• [36] J. Rudolph, Flatness Based Control of Distributed Parameter Systems, Steuerungs- und Regelungstechnik, Shaker Verlag, Aachen, 2003.
• [37] H. Sira-Ramirez and S. Agrawal, Differentially Flat Systems, Marcel Dekker, New York, 2004.
• [38] J. L´evine, On necessary and sufficient conditions for differential flatness, Applicable Algebra in Engineering, Communications and Computing, Springer, vol. 22, no. 1, pp. 47-90, 2011.
• [39] M. Fliess and H. Mounier, Tracking control and #-freeness of infinite dimensional linear systems, In: G. Picci and D.S. Gilliam Eds.,Dynamical Systems, Control, Coding and Computer Vision, vol. 258, pp. 41-68, Birkha¨user, 1999.
• [40] L.Menhour, B. d’Andr´ea-Novel, M. Fliess and H. Mounier, Coupled nonlinear vehicle control: Flatness-based setting with algebraic estimation techniques, Control Engineering Practice, vol. 2, pp, 13546, 2014.
• [41] P. Rouchon, Flatness-based control of oscillators, ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik, vol. 85, no.6, pp. 411-421, 2005.
• [42] Ph. Martin and P. Rouchon, Syst`emes plats: planification et suivi des trajectoires, Journ´ees XUPS, ´ Ecole des Mines de Paris, Centre Automatique et Syst`emes, Mai,1999.
• [43] S. Bououden, D. Boutat, G. Zheng, J.P. Barbot and F. Kratz, A triangular canonical form for a class of 0-flat nonlinear systems, International Journal of Control, Taylor and Francis, vol. 84, no. 2, pp. 261-269, 2011.
• [44] B. Laroche, P. Martin, and N. Petit, Commande par platitude: Equations diff´erentielles ordinaires et aux deriv´ees partielles, Ecole Nationale Sup´erieure des Techniques Avanc´ees, Paris, 2007.
• [45] G. Rigatos, Modelling and control for intelligent industrial systems: Adaptive algorithms in robotics and industrial engineering, Springer, 2011