A WIP Control Based On an Intelligent Controller

In this study, a robust intelligent backstepping tracking control (RIBTC) system combined with adaptive output recurrent cerebellar model articulation control (AORCMAC) and H∞ control technique is proposed for wheeled inverted pendulums (WIPs) real-time control with exact system dynamics unknown. Moreover, a robust H∞ controller is designed to attenuate the effect of the residual approximation errors and external disturbances with desired attenuation level. The experimental results indicate that the WIPs can stand upright stably when using the proposed RIBTC.

• Chih-Hui Chiu
• Chun-Hsien Lin

• Wheeled inverted pendulum
• backsteppingtracking control
• H∞ control
• adaptive output recurrentcerebellar model articulation control.

[1] T. Ohta, T. Murakami, A stabilization control of bilateral system
with time delay by vibration indexÔÇöapplication to inverted
pendulum control, IEEE Trans. Ind. Electron. 56(5) (2009)
1595-1603.
[2] R.J. Wai, L.J. Chang, Adaptive stabilizing and tracking control for a
nonlinear inverted-pendulum system via sliding-mode technique,
IEEE Trans. Ind. Electron. 53(2) (2006) 674-692.
[3] N. Motoi, T. Suzuki, K. Ohnishi, A bipedal locomotion planning
based on virtual linear inverted pendulum mode, IEEE Trans. Ind.
Electron. 56(1) (2009) 54-61.
[4] C.H. Chiu, The design and implementation of a wheeled inverted
pendulum using an adaptive output recurrent cerebellar model
articulation controller, IEEE Trans. Ind. Electron. 57(5) (2010)
1814-1822.
[5] F. Grasser, A. D-Arrigo, S. Colombi, A.C. Rufer, JOE: a mobile,
inverted pendulum, IEEE Trans. Ind. Electron. 39(1) (2002)
107-114.
[6] T.J. Ren, T.C. Chen, C.J. Chen, Motion control for a two-wheeled
vehicle using a self-tuning PID controller, Control Engineering
Practice 16(3) (2008) 365-375.
[7] S.C. Lin, C.C. Tsai, Development of a self-balancing human
transportation vehicle for the teaching of feedback control, IEEE
Trans Educ. 52(1) (2009) 157-168.
[8] C.H. Lee, C.C. Teng, Identification and control of dynamic systems
using recurrent fuzzy neural networks, IEEE Trans. Fuzzy Systems
8(4) (2000) 349-366.
[9] M. Krstic, I. Kanellakopoulos, P.V. Kokotovic, Nonlinear and
Adaptive control Design. New York: Wiley, 1995.
[10] F.J. Lin, P.H. Shen, R.F. Fung, RFNN control for PMLSM drive via
backstepping technique, IEEE Trans. Aerosp. Electron. Syst. 41
(2005) 620-644.
[11] Y.G. Leu, W.Y. Wang, T.T. Lee, Robust adaptive fuzzy-neural
controllers for uncertain nonlinear systems, IEEE Trans. Robot
Automat. 15 (1999) 805-817.
[12] B.S. Chen, C.H. Lee, H∞ tracking design of uncertain nonlinear
SISO system: adaptive fuzzy approach, IEEE Trans. Fuzzy Syst. 4
(1996) 32-43.