Adaptive fuzzy logic control of dynamic balance and motion for wheeled inverted pendulums

In this paper, adaptive fuzzy logic control of dynamic balance and motion is investigated for wheeled inverted pendulums with parametric and functional uncertainties. The proposed adaptive fuzzy logic control based on physical properties of wheeled inverted pendulums makes use of a fuzzy logic engine and a systematic online adaptation mechanism to approximate the unknown dynamics. Based on Lyapunov synthesis, the fuzzy control ensures that the system outputs track the given bounded reference signals to within a small neighborhood of zero, and guarantees semi-global uniform boundedness of all closed-loop signals. The effectiveness of the proposed control is verified through extensive simulations.

[1]  Hiroshi Ishiguro,et al.  Human-like natural behavior generation based on involuntary motions for humanoid robots , 2004, Robotics Auton. Syst..

[2]  Masahiro Oya,et al.  Adaptive motion tracking control of uncertain nonholonomic mechanical systems including actuator dynamics , 2005 .

[3]  Shuzhi Sam Ge,et al.  Adaptive neural control of uncertain MIMO nonlinear systems , 2004, IEEE Transactions on Neural Networks.

[4]  Yoon Keun Kwak,et al.  Dynamic Analysis of a Nonholonomic Two-Wheeled Inverted Pendulum Robot , 2005, J. Intell. Robotic Syst..

[5]  Jorge Angeles,et al.  On the nonlinear controllability of a quasiholonomic mobile robot , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

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

[7]  Rodney A. Brooks,et al.  Sensing and Manipulating Built-for-Human Environments , 2004, Int. J. Humanoid Robotics.

[8]  Jorge Angeles,et al.  Controllability and Posture Control of a Wheeled Pendulum Moving on an Inclined Plane , 2007, IEEE Transactions on Robotics.

[9]  Kaustubh Pathak,et al.  Velocity and position control of a wheeled inverted pendulum by partial feedback linearization , 2005, IEEE Transactions on Robotics.

[10]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[11]  Jorge Angeles,et al.  The control of semi-autonomous two-wheeled robots undergoing large payload-variations , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[12]  Nicholas R. Gans,et al.  Visual Servo Velocity and Pose Control of a Wheeled Inverted Pendulum through Partial-Feedback Linearization , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[14]  Alberto Isidori,et al.  Robust Autonomous Guidance: An Internal Model Approach , 2003 .

[15]  Shuzhi Sam Ge,et al.  Fuzzy unidirectional force control of constrained robotic manipulators , 2003, Fuzzy Sets Syst..

[16]  Chun-Yi Su,et al.  Robust motion/force control of mechanical systems with classical nonholonomic constraints , 1994, IEEE Trans. Autom. Control..

[17]  Lorenzo Marconi,et al.  Robust Autonomous Guidance , 2003 .

[18]  Tao Zhang,et al.  A direct adaptive controller for dynamic systems with a class of nonlinear parameterizations , 1999, Autom..

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

[20]  A. Blankespoor,et al.  Experimental verification of the dynamic model for a quarter size self-balancing wheelchair , 2004, Proceedings of the 2004 American Control Conference.

[21]  Hugang Han,et al.  Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators , 2001, IEEE Trans. Fuzzy Syst..

[22]  Aiguo Ming,et al.  Intelligent compliant force/motion control of nonholonomic mobile manipulator working on the nonrigid surface , 2005, Neural Computing & Applications.

[23]  Jin Zhang,et al.  Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback , 2003, IEEE Trans. Neural Networks.

[24]  Wolfgang Hahn,et al.  Stability of Motion , 1967 .