ROBUST FUZZY CONTROL DESIGN USING GENETIC ALGORITHM OPTIMIZATION APPROACH: CASE STUDY OF SPARK IGNITION ENGINE TORQUE CONTROL

Document Type: Research Paper

Authors

1 Department of Electrical Engineering, Diponegoro University, Semarang, Indonesia

2 Department of Electrical Engineering, Diponegoro University, Se- marang, Indonesia

Abstract

In the case of widely-uncertain non-linear system control design, it was very difficult to design a single controller to overcome control design specifications in all of its dynamical characteristics uncertainties. To resolve these problems, a new design method of robust fuzzy control proposed. The solution offered was by creating multiple soft-switching with Takagi-Sugeno fuzzy model for optimal solution control at all operating points that generate uncertainties. Optimal solution control at each operating point was calculated using genetic algorithm. A case study of engine torque control of spark ignition engine model was used to prove this new method of robust fuzzy control design. From the simulation results, it can be concluded that the controller operates very well for a wide uncertainty.

Keywords


[1] M. Athans, A tutorial on the LQG/LTR method, in 1986 American Control Conference,
0582(606) (1986), 1289{1296.
[2] S. G. Cao, N. W. Rees and G. Feng, Analysis and design of fuzzy control systems using
dynamic fuzzy-state space models, IEEE Trans. Fuzzy Syst., 7(2) (1999), 192-200.
[3] M. A. Denai and S. A. Attia, Robust state feedback control of eld oriented induction motor,
in Advanced Motion Control, 2000, (2000), 281{286.
[4] P. Eklund and J. Zhou, Comparison of learning strategies for adaptation of fuzzy controller
parameters, Fuzzy Sets Syst., 106(3) (1999), 321{333.
[5] D. E. Goldberg, Genetic algorithm in search, optimization, and machine learning. Addison-
Wesley Publ. Co, (1989), 60{85.
[6] N. Heintz, M. Mews, G. Stier, A. Beaumont and A. Noble, An approach to torque-based
engine management systems, Soc. Automot. Eng., (2001-01-0269) (2001), 95-102.
[7] D. M. Lamberson, Torque management of gasoline engines, Thesis, University of California
at Berkeley, (2003), 27-40.
[8] Q. Li and T. Chai, Fuzzy adaptive control for a class of nonlinear systems, Fuzzy Sets Syst.,
101(1) (1999), 31{39.
[9] T.- Li, S.- Tong and G. Feng, A novel robust adaptive-fuzzy-tracking control for a class of
nonlinear multi-input/multi-output systems, Fuzzy Syst. IEEE Trans., 18(1) (2010), 150{
160.
[10] F. Mei, M. Zhihong, X. Yu and T. Nguyen, A robust tracking control scheme for a class of
nonlinear systems with fuzzy nominal models, Appl. Math. Comput. Sci., 8(1:19) (1998),
145-158.
[11] Y. Shi and M. Mizumoto, A new approach of neuro-fuzzy learning algorithm for tuning fuzzy
rules, Fuzzy Sets Syst., 112(1) (2000), 99{116.
[12] A. Stefanopoulou, Modeling and control of advanced technology engines, Thesis, The Univer-
sity of Michigan, (1996), 11-28.
[13] T. Takagi and M. Sugeno, Fuzzy identi cation of systems and its applications to modeling
and control, IEEE Trans. Syst. Man Cybern., (1) (1985), 116-132.
[14] A. Triwiyatno, M. Nuh, A. Santoso and I. N. Sutantra, Engine torque control of spark ignition
engine using robust fuzzy logic control, IACSIT Int. J. Eng. Technol., 3(4) (2011), 352{358.
[15] A. Triwiyatno, M. Nuh, A. Santoso and I. N. Sutantra, Engine torque control of si engine
using linear quadratic integral tracking ( LQIT ) Optimal Control, IPTEK, 22(4) (2011),
190{197.

[16] A. Triwiyatno, M. Nuh, A. Santoso and I. N. Sutantra, T-S fuzzy model design for engine
torque control system of spark ignition engine, Transmisi, 11(4) (2009), 177-182.
[17] A. Triwiyatno, M. Nuh, A. Santoso and I. N. Sutantra, A new method of robust fuzzy control?:
case study of engine torque control of spark ignition engine, Int. J. Acad. Res., 3(5) (2011),
178-185.