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Abstract

A new robust nonlinear controller is proposed to improve the performance of the atmospheric pressure simulator that has some special characteristics such as asymmetry and nonlinearity. The three major components in such systems, the chamber, the servo valve and the vacuum pump, are studied to develop a full nonlinear model which encompasses all the major nonlinearities. Based on the model expressed in the controllability canonical form, a feedback linearization controller is developed to handle the strong asymmetry and nonlinearity of the atmospheric pressure simulator. Considering the parametric uncertainties and un-modeled dynamics existing in the atmospheric pressure simulator, a self-tuning fuzzy proportional integral derivative controller integrating with feedback linearization is introduced to improve the performance of the atmospheric pressure simulator at high-altitude simulations. Simulations and experiments indicate that the proposed controller can effectively raise the dynamic response performance, and in the meantime stability can be ensured.

Keywords

Vacuum pressure simulator nonlinear control feedback linearization self-tuning fuzzy proportional integral derivative asymmetry

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References

Gans

Dixon

Lind

. A hardware in the loop simulation platform for vision-based control of unmanned air vehicles. Mechatronics 2009; 19: 1043–1056.

Karpenko

Sepehri

. Hardware-in-the-loop simulator for research on fault tolerant control of electrohydraulic actuators in a flight control application. Mechatronics 2009; 19: 1067–1077.

Bradley

Moffitt

Mavris

. Hardware-in-the-loop testing of a fuel cell aircraft powerplant. J Propul Power 2009; 25: 1336–1344.

Nasiri

Montazeri-Gh

. Time-delay compensation for actuator-based hardware-in-the-loop testing of a jet engine fuel control unit. Proc IMechE, Part I: J Systems and Control Engineering 2012; 226: 1371–1380.

MelLellan

Schlepper

McLintock

. GPS/barometry height-aided positioning system. In: Proceedings of position location and navigation symposium, Las Vegas, NV, 11–15 April 1994. IEEE.

Slotine

J-JE

. Applied nonlinear control. Englewood Cliffs, NJ: Prentice Hall, 1991.

Rehman

Petersen

Fidan

. Feedback linearization-based robust nonlinear control design for hypersonic flight vehicles. Proc IMechE, Part I: J Systems and Control Engineering 2013; 227: 3–11.

Khayati

Bigras

Dessaint

. A robust feedback linearization force control of a pneumatic actuator. In: IEEE international conference on systems, man and cybernetics, New York, USA, 10–13 October 2004, vols. 1–7, pp.6113–6119. IEEE.

Xiang

Wikander

. Block-oriented approximate feedback linearization for control of pneumatic actuator system. Control Eng Pract 2004; 12: 387–399.

10.

Szabo

Buchholz

Dietmayer

. A feedback linearization based observer for an electropneumatic clutch actuated by on/off solenoid valves. In: 2010 IEEE international conference on control applications, Yokohama, Japan, 8–10 September 2010, pp.1445–1450. IEEE.

11.

Zhao

Ben-Tzvi

Lin

. Two-layer sliding mode control of pneumatic position synchro system with feedback linearization based on friction compensation. In: International workshop on robotic and sensors environments, 2008, Ottawa, ON, Canada, 17–18 October 2008, pp.41–45. IEEE.

12.

Zheng

Zhao

Wei

. Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press. Control Eng Pract 2009; 17: 1398–1404.

13.

Beater

. Pneumatic drives: system design, modeling and control. Verlag Berlin Heidelberg: Springer, 2006.

14.

Roth

. Vacuum technology. Amsterdam/New York/Oxford: North-Holland Publishing Company, 1982.

15.

Khalil

. Nonlinear systems. Upper Saddle River, NJ: Prentice Hall, 2002.

16.

Guzzella

. Stability of the feedback linearization of bilinear plants with bounded inputs. Kybernetika 1994; 30: 141–152.

17.

Astrom

Hagglund

. PID controllers: theory, design, and tuning. Research Triangle Park, NC: ISA Press, 1995.

18.

Ahn

Truong

Thanh

. Online self-tuning fuzzy proportional-integral-derivative control for hydraulic load simulator. Proc IMechE, Part I: J Systems and Control Engineering 2008; 222: 81–95.

19.

Visioli

. Tuning of PID controllers with fuzzy logic. IEE P: Contr Theor Ap 2001; 148: 1–8.

20.

Wang

. A course in fuzzy systems and control. Upper Saddle River, NJ: Prentice Hall, 1997.

21.

Zhao

Z-Y

Tomizuka

Isaka

. Fuzzy gain scheduling of PID controllers. IEEE T Syst Man Cyb 1993; 23: 1392–1398.

Feedback linearization-based self-tuning fuzzy proportional integral derivative control for atmospheric pressure simulator

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