WIPs real-time control using RIBTC

In this study, a robust intelligent backstepping tracking control (RIBTC) system combined with AORCMAC and H∞ control technique is proposed for wheeled inverted pendulums (WIPs) real-time control with exact system dynamics unknown and external disturbances. In the proposed control system, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is used to copy an ideal backstepping control (IBC), and a robust H∞ controller is designed to attenuate the effect of the residual approximation errors and external disturbances with desired attenuation level. Moreover, the all adaptation laws of the RIBTC system are derived based on the Lyapunov stability analysis, the Taylor linearization technique and H∞ control theory, so that the stability of the closed-loop system and H∞ tracking performance can be guaranteed.

[1]  Syuan-Yi Chen,et al.  FPGA-Based Computed Force Control System Using Elman Neural Network for Linear Ultrasonic Motor , 2009, IEEE Transactions on Industrial Electronics.

[2]  Alfred C. Rufer,et al.  JOE: a mobile, inverted pendulum , 2002, IEEE Trans. Ind. Electron..

[3]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[4]  Seul Jung,et al.  Control Experiment of a Wheel-Driven Mobile Inverted Pendulum Using Neural Network , 2008, IEEE Transactions on Control Systems Technology.

[5]  Faa-Jeng Lin,et al.  RFNN control for PMLSM drive via backstepping technique , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Rong-Jong Wai,et al.  Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor , 2004, IEEE Transactions on Industrial Electronics.

[7]  Ronald G. Harley,et al.  Recurrent Neural Networks Trained With Backpropagation Through Time Algorithm to Estimate Nonlinear Load Harmonic Currents , 2008, IEEE Transactions on Industrial Electronics.

[8]  Faa-Jeng Lin,et al.  Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network , 2009, IEEE Transactions on Industrial Electronics.

[9]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[10]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[11]  Ming-Feng Yeh,et al.  Design of a hybrid adaptive CMAC with supervisory controller for a class of nonlinear system , 2009, Neurocomputing.

[12]  Ya-Fu Peng Robust intelligent backstepping tracking control for uncertain non-linear chaotic systems using H∞ control technique , 2009 .

[13]  Rong-Jong Wai,et al.  Adaptive stabilizing and tracking control for a nonlinear inverted-pendulum system via sliding-mode technique , 2006, IEEE Trans. Ind. Electron..

[14]  Yih-Guang Leu,et al.  Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems , 1999, IEEE Trans. Robotics Autom..

[15]  Chih-Hui Chiu,et al.  The Design and Implementation of a Wheeled Inverted Pendulum Using an Adaptive Output Recurrent Cerebellar Model Articulation Controller , 2010, IEEE Transactions on Industrial Electronics.

[16]  T. Murakami,et al.  A Stabilization Control of Bilateral System With Time Delay by Vibration Index—Application to Inverted Pendulum Control , 2009, IEEE Transactions on Industrial Electronics.

[17]  M. Azizur Rahman,et al.  Development and Implementation of a Novel Fault Diagnostic and Protection Technique for IPM Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[18]  Shin'ichi Yuta,et al.  Trajectory tracking control for navigation of the inverse pendulum type self-contained mobile robot , 1996, Robotics Auton. Syst..

[19]  Chao-Chung Peng,et al.  Robust chaotic control of Lorenz system by backstepping design , 2008 .

[20]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[21]  Chun-Jung Chen,et al.  Motion control for a two-wheeled vehicle using a self-tuning PID controller , 2008 .