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Research Papers

Fuzzy Sliding Mode Control for Turbocharged Diesel Engine

[+] Author and Article Information
Samia Larguech

MIS Laboratory,
University of Picardie Jules Verne,
33 rue Saint Leu,
Amiens 80039, France;
STA Laboratory,
National School of Engineering of Sfax,
BP 1173,
Sfax 3038, Tunisia
e-mail: samia.larguech@u-picardie.fr

Sinda Aloui

STA Laboratory,
National School of Engineering of Sfax,
BP 1173,
Sfax 3038, Tunisia
e-mail: aloui_sinda@yahoo.fr

Olivier Pagès

MIS Laboratory,
University of Picardie Jules Verne,
33 rue Saint Leu,
Amiens 80039, France
e-mail: opages@u-picardie.fr

Ahmed El Hajjaji

MIS Laboratory,
University of Picardie Jules Verne,
33 rue Saint Leu,
Amiens 80039, France
e-mail: ahmed.hajjaji@u-picardie.fr

Abdessattar Chaari

STA Laboratory,
National School of Engineering of Sfax,
BP 1173,
Sfax 3038, Tunisia
e-mail: abdessattar2004@yahoo.fr

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 18, 2015; final manuscript received October 20, 2015; published online November 18, 2015. Assoc. Editor: Junmin Wang.

J. Dyn. Sys., Meas., Control 138(1), 011009 (Nov 18, 2015) (15 pages) Paper No: DS-15-1030; doi: 10.1115/1.4031913 History: Received January 18, 2015; Revised October 20, 2015

In this work, fuzzy second-order sliding mode control (2-SMC) and adaptive sliding mode control (ASMC) are developed for a turbocharged diesel engine (TDE). In control design, the TDE is represented by multi-output multi-input (MIMO) nonlinear model with partially unknown dynamics. To regulate the intake manifold pressure, the exhaust manifold pressure, the compressor flow, and to estimate the unknown functions, a sliding mode control (SMC) combined with fuzzy logic is first developed. Second to reduce the chattering phenomenon without deteriorating the tracking performance, two approaches are investigated. A special case of the 2-SMC: the super-twisting SMC is developed. The results obtained using the ASMC are also presented to compare the performances of both methods. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis, so that convergence to zero of tracking errors and boundedness of all signals in the closed-loop system are guaranteed. Simulation results are given to show the efficiency of the proposed approaches.

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References

Figures

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Fig. 1

Schematic diagram of a TDE

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Fig. 2

Block diagram of a fuzzy logic system [32]

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Fig. 3

Intake manifold pressure response p1 (Pa) (1-SMC)

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Fig. 4

Exhaust manifold pressure response p3 (Pa) (1-SMC)

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Fig. 5

Fresh airflow rate response Wc (kg/s) (1-SMC)

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Fig. 6

Evolution of tracking errors: (a) e1, (b) e2, and (c) e3 (1-SMC)

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Fig. 7

Evolution of control law u1 (1-SMC)

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Fig. 8

Evolution of control law u2 (1-SMC)

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Fig. 9

Evolution of control law u3 (1-SMC)

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Fig. 10

Intake manifold pressure response p1 (Pa) (2-SMC)

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Fig. 11

Exhaust manifold pressure response p3 (Pa) (2-SMC)

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Fig. 12

Fresh airflow rate response Wc (kg/s) (2-SMC)

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Fig. 13

Evolution of tracking errors: (a) e1, (b) e2, and (c) e3 (2-SMC)

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Fig. 14

Evolution of control law u1 (2-SMC)

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Fig. 15

Evolution of control law u2 (2-SMC)

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Fig. 16

Evolution of control law u3 (2-SMC)

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Fig. 17

Intake manifold pressure response p1 (Pa) (ASMC)

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Fig. 18

Exhaust manifold pressure response p3 (Pa) (ASMC)

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Fig. 19

Fresh airflow rate response Wc (kg/s) (ASMC)

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Fig. 20

Evolution of tracking errors: (a) e1, (b) e2, and (c) e3 (ASMC)

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Fig. 21

Evolution of control law u1 (ASMC)

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Fig. 22

Evolution of control law u2 (ASMC)

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Fig. 23

Evolution of control law u3 (ASMC)

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