Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Design Model-free Fuzzy Sliding Mode Control of Internal Combustion Engine
Farzin Piltan, N. Sulaiman, Payman Ferdosali, Iraj Assadi Talooki
Pages - 302 - 312     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Internal Combustion Engine, Sliding Mode Controller, Chattering Phenomenon, Fuzzy Sliding Mode Controller, Chattering Control
Modeling and control of engine systems are vital due to wide range of their applications. As it is obvious stability is the minimum requirement in any control system, however the proof of stability is not trivial especially in the case of nonlinear systems. One of the most active research areas in field of internal combustion engine (IC engine) is control of the fuel ratio. The strategies for control of engines are classified into two main groups: classical and non-classical methods, where the classical methods used the conventional control theory and non-classical methods used the artificial intelligence theory such as fuzzy logic, neural networks and/or neurofuzzy. One of the best nonlinear robust controllers which can be used in uncertainty nonlinear systems is sliding mode controller (SMC). Chattering phenomenon is the most important challenge in this controller. Fuzzy logic and neuro control have been applied successfully in many applications. Therefore stable control of an internal combustion engine is challenging because it has uncertain dynamic parameters. This research presents a design fuzzy sliding mode controller with improved in sliding mode controller which offers a model-free sliding mode controller. The fuzzy sliding mode controller is designed as a 49 rules Mamdani’s error-based fuzzy sliding-like equivalent part instead of nonlinear dynamic equation of equivalent part. Various performance indices like the minimum error, trajectory, disturbance rejection, and chattering control are used for comparison.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 Heywood, J., “Internal Combustion Engine Fundamentals”, McGraw-Hill, New York, 1988.
2 Ferguson, C., “Internal Combustion Engines: Applied Thermosciences”, John Wiley & Sons, Inc., New York, 2001.
3 Guzzella, L., “Introduction to Modeling and Control of Internal Combustion Engine Systems” Springer, New York, 2004.
4 Ramos, J., “Internal Combustion Engine Modeling”, Hemisphere Publishing corporation, New York, 1989.
5 Blair, G., “Design and Simulation of Four Stroke Engines”, Society of Automotive Engineers, Warrendale, Pa, 1999.
6 G. Zhu, et al, "Closed-Loop Ignition Timing Control for SI Engines Using Ionization Current Feedback," IEEE Trans on Control Systems, pp. 416-427, May 2007.
7 I. Haskara, et al, "On Combustion Invariants For MBT Timing Estimation and Control," in ASME Internal Combustion Engine Division, 2004.
8 Frank L.Lewis. Nonlinear dynamics and control, Handbook, pages 51-70. CRC press, 1999.
9 Thomas R.Kurfess.,” Dynamic plant and Automation Handbook”, CRC press, 2005.
10 Lotfi A. Zadeh” Toward a theory of fuzzy information granulation and its centrality in human easoning and fuzzy logic” Fuzzy Sets and Systems 90 (1997) 111-127
11 Lotfi.A.Zadeh”Fuzzy logic,Nural network, and Soft computing” communications of the ACM, March 1994, Vol.37.No.3
12 Dawson, J., “An experimental and Computational Study of Internal Combustion Engine Modeling for Controls Oriented Research” Ph.D. Dissertation, The Ohio State University, 2005.
13 Lee, B., “Methodology for the Static and Dynamic Model Based Engine Calibration and Optimization” Ph.D. Dissertation, The Ohio State University, 2005.
14 Okyak Kaynak, “Guest Editorial Special Section on Computationally Intelligent Methodologies and Sliding-Mode Control”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 48, NO. 1, 2001
15 Norsinnira Zainul Azlan and Johari Halim Shah Osman,” Modeling and Proportional Integral Sliding Mode Control of Hydraulic Manipulators”, SCOReD 2006, 2006.
16 Soteris A. Kalogirou,” Artificial intelligence for the modeling and control of combustion processes: a review”, Progress in Energy and Combustion Science, science direct, 2003
17 J. G. Rivard, "Closed-loop Electronic Fuel Injection Control of the IC Engine," in Society of Automotive Engineers, 1973.
18 F. Piltan, et al., "Artificial Control of Nonlinear Second Order Systems Based on AFGSMC," Australian Journal of Basic and Applied Sciences, 5(6), pp. 509-522, 2011.
19 Piltan, F., et al., 2011. Design sliding mode controller for robot manipulator with artificial tunable gain. Canaidan Journal of pure and applied science, 5 (2): 1573-1579.
20 Piltan, F., et al., 2011. Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC with Tunable Gain, International Journal of Robotic and Automation, 2 (3): 205-220.
21 Piltan, F., et al., 2011. Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller with Minimum Rule Base, International Journal of Robotic and Automation, 2 (3): 146-156.
22 Piltan, F., et al., 2011. Design of FPGA based sliding mode controller for robot manipulator, International Journal of Robotic and Automation, 2 (3): 183-204.
23 Piltan, F., et al., 2011. A Model Free Robust Sliding Surface Slope Adjustment in Sliding Mode Control for Robot Manipulator, World Applied Science Journal, 12 (12): 2330-2336.
24 Piltan, F., et al., 2011. Design Adaptive Fuzzy Robust Controllers for Robot Manipulator, World Applied Science Journal, 12 (12): 2317-2329.
Mr. Farzin Piltan
- Malaysia
Mr. N. Sulaiman
- Malaysia
Mr. Payman Ferdosali
- Iran
Dr. Iraj Assadi Talooki
- Iran