This study presents a model-following positioning control system based on modified sliding mode control. In the proposed method, a robust gain matrix prevents the dependence on the plant parameters by applying the switching function to an error dynamics equation. Simulation studies are applied to a nominal plant with stable and unstable terms, and a plant with a modeling error and an input-side disturbance. Simulation and experimental results indicate the effectiveness of this method in determining a robust gain matrix. The proposed method further demonstrates positioning control with superior performance.
StefaniRTSavantCJJrShahianB. Design of feedback control systems. 3rd ed.Boston: Saunders College Publishing, 1994.
2.
YangZ-JQiX-H. Model-following sliding mode controller design for flight control systems with wind disturbances. In: Proceedings of 4th IEEE conference on industrial electronics and applications, Xi’an, China, 2009, pp.287–291.
3.
FurutaKKomiyamaK.Design of model-following servo controller. IEEE T Automat Contr1982; 27(3): 725–727.
4.
XinminDZhiguoXQinL. Gain scheduled model following control of flight control system based on neural network. In: Proceedings of the 2003 IEEE international conference on neural networks and signal processing, Nanjing, China, 2003, pp.301–305.
5.
WangDWuSWangZ. Model following control system with robot system. In: Proceedings of the 2nd international Asia conference on informatics in control, automation and robotics, Wuhan, China, 2010, pp.445–448.
6.
PhanphairojeSWitheephanichKPiyaratW. A robust control structure enhancing nominal model following scheme for induction servomotor drive. In: Proceedings of the SICE annual conference, Sapporo, Japan, 2004, pp.388–397.
7.
PaiM-C.Observer-based adaptive sliding mode control for robust tracking and model following. Int J Control Autom2010; 11(2): 225–232.
8.
GezerRBKuatayAT. Robust model following control design for missile roll autopilot. In: Proceedings of the 2014 UKACC international conference on control, Loughborough, UK, 2014, pp.7–12.
9.
MaddiAGuessomABarkaniD. Improvement tracking performances of adaptive model following control systems with unknown disturbances. In: Proceedings of the 2015 6th international conference on modeling, simulation, and applied optimization, Istanbul, Turkey, 2015, pp.1–6.
10.
EmelyanovSV.Variable structure control systems. Int J Control1989; 23(4): 123–145.
11.
ItkisU.Control systems of variable structure. New York: Wiley, 1976.
12.
UtkinVI.Sliding mode and their applications. Moscow: Mir, 1978.
13.
UtkinVI.Variable structure systems with the sliding mode: A survey. IEEE T Automat Contr1977; 22(2): 212–222.
14.
BurtonJAZinoberASI. Continuous self-adaptive control using a smoothed variable structure controller. Int J Syst Sci1988; 19(8): 1515–1528.
15.
YoungKKD. Variable structure control for robotics and aerospace applications. New York: Elsevier Science Inc., 1993.
16.
AmbrosinoGCelentanoGGarofaloF.Variable structure model reference adaptive control system. Int J Control1984; 39(6): 1339–1349.
17.
SlotineJJELiW. Applied nonlinear control. New Jersey: Prentice Hall, 1991.
18.
YangYWangWZhouT. Sliding mode control based on three-sliding-surface for an inverted pendulum system. In: Proceedings of the 2014 33rd Chinese control conference, Nanjing, China, 2014, pp.65–70.
19.
ShinghKNemaSPadhyPK. Modified PSO based PID sliding mode control for inverted pendulum. In: Proceedings of the 2014 international conference on control, instrumentation, communication and computational technologies, Kanyakumari, India, 2014, pp.722–727.
20.
PhakamachPTiacharoenSAkkaraphongC.Position control of a brushless DC servomotor using a sliding mode model following (SMFC) system. In: Proceedings of the 2002 IEEE international conference on industrial technology, Bangkok, China, 2002, pp.566–571.
21.
SeglamCOBaranEANeglzAO. Model following control with discrete time SMC for time-delayed bilateral control systems. In: Proceedings of the 2011 IEEE international conference on mechatronics, Istanbul, Turkey, 2011, pp.997–1002.
22.
GinoyaDLPatelTRShendgePD. Design and hardware implementation of model following sliding mode control with inertial delay observer for uncertain systems. In: Proceedings of the 2011 3rd international conference on electronics computer technology, Vol. 3, Kanyakumari, India, 2011, pp.192–196.
23.
NonamiKDenK.Sliding mode control. Tokyo: Corona Publishing, 1994 (in Japanese).
24.
NonamiKNishimuraH.Basis of the designing control system by MATLAB. Tokyo: Tokyo Denki University Publishing, 1998 (in Japanese).
25.
HashimotoSFujiiYKigureM. High-precision control of linear actuators based on internal model control. In: Proceedings of the second IEEE conference on industrial electronics and applications, Harbin, China, 2007, pp.2148–2153.
26.
IshikawaYMasukakeYIshidaY.Discrete modified internal model control for plants with an integrator and time delay. IEICE T Fund Electr2007; 90(8): 663–665 (in Japanese).
27.
Al-FaizMZSalehMSOglahAA.Type-2 fuzzy logic controllers based genetic algorithm for the position control of DC motor. Lect Notes Contr Inf2013; 4(1): 108–113.
28.
El-SousyFFM. Intelligent optimal recurrent wavelet Elman neural network control system for permanent-magnet synchronous motor servo drive. IEEE T Ind Inform20139(4): 1986–2003.
29.
MunadiJDSAkbarMA. Simulation of fuzzy logic control for DC servo motor using Arduino based on Matlab/Simulink. In: Proceedings of the 2014 international conference on intelligent autonomous agents, networks and systems, Bandung, Indonesia, 2014, pp.42–46.
30.
HiranoMSasakiKFujitaT. Control system design of a pneumatic servo system considering the dynamic characteristics of the servo valve. In: Proceedings of the 7th JFPS international symposium on fluid power, Toyama, Japan, 2008, pp.457–460.
31.
YuanRSunCBaS. Analysis of position servo system of pneumatic manipulator based on RBF neural network PID control. In: Proceedings of the 2010 international conference on web information systems and mining, Sanya, China, 2010, pp.221–226.
32.
LiJKawashimaKKagawaT. Trajectory control of pneumatic servo table with air bearing. In: Proceedings of the 2011 international conference on fluid power and mechatronics, Beijing, China, 2011, pp.683–690.
33.
YuH-YShiSLP. The study of tracking control of electro-hydraulic servo system with feed forward and fuzzy-integrate control. In: Proceedings of the 2011 international conference on electronic and mechanical engineering and information technology, Harbin, China, 2011, pp.4409–4412.
34.
ZhangJFanYZhongW. PID neural network control of hydraulic roll gap control system. In: Proceedings of the 2012 international conference on measurement, information and control, Harbin, China, 2012, pp.791–795.
35.
YokotaSMitsuhashiMTorataniT. Control of electro-hydraulic manipulators for electric distributed power line tasks using a disturbance observer, 2nd report, accurate trajectory control of a two-link manipulator arm. Hydraulic Pneuma1994; 25(6): 746–752.
36.
ChernTLWuY-C.Design of integral variable structure controller and application to electro hydraulic velocity servo systems. IEE Proc-D1991; 138(5): 439–444.
37.
ChernTLWuY-C.Integral variable structure control approach for robot manipulators. IEE Proc-D1992; 139(2): 460–463.
38.
ChernTLChangJ.DSP-based integral variable structure model following control for brushless DC motor drivers. IEEE T Power Electr1997; 12(1): 53–63.
39.
WangWNonamiKOhiraY.Model reference sliding mode control of small helicopter XRB based on vision. Int J Adv Robot Syst2008; 5(3): 235–242.
40.
TanakaRShibasakiHOgawaH. Controller design approach based on linear programming. ISA T2013; 52(6): 744–751.
41.
Aguillar-IbanezCGarrido-MoctezumaRDavilaJ.Output feedback trajectory stabilization of the uncertainty DC servomechanism system. ISA T2012; 51(6): 801–807.
