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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055240Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1460
 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.
 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.
 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.
 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.
 F. Grasser, A. D-Arrigo, S. Colombi, A.C. Rufer, JOE: a mobile, inverted pendulum, IEEE Trans. Ind. Electron. 39(1) (2002) 107-114.
 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.
 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.
 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.
 M. Krstic, I. Kanellakopoulos, P.V. Kokotovic, Nonlinear and Adaptive control Design. New York: Wiley, 1995.
 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.
 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.
 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.