Design of neural-fuzzy-based controller for two autonomously driven wheeled robot

An intelligent control architecture for two autonomously driven wheeled robot is developed in this paper. Consider the parametric variation, external load disturbance, nonlinear friction, unpredicted and unstructured uncertainties for the practical applications, the transient and unmodelled uncertainty will be occurred. In the proposed control scheme, the fuzzy inference is designated as a main controller and the neural network is an auxiliary part. In the fuzzy controller, the translation width and total sliding surface are adopted to reduce the chattering phenomena. The neural uncertainty observer is added in the balance, speed and synchronous controllers to reduce the accumulated error and ascend the stability. The hardware includes a microcontroller, gyroscope, accelerometer, and two autonomous motors, etc. The effectiveness is verified by simulation and experimental results, and the result is compared with conventional PD control scheme for the same robot.

[1]  Marilena Vendittelli,et al.  WMR control via dynamic feedback linearization: design, implementation, and experimental validation , 2002, IEEE Trans. Control. Syst. Technol..

[2]  Peter Kwong-Shun Tam,et al.  A fuzzy sliding controller for nonlinear systems , 2001, IEEE Trans. Ind. Electron..

[3]  M. Kositsky,et al.  Dynamical dimension of a hybrid neurorobotic system , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[5]  H. Nakamura,et al.  Grobal position and posture control of a two-wheeled mobile robot using a discontinuous homogeneous control , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[6]  Dong Sun,et al.  Orientation control of a differential mobile robot through wheel synchronization , 2005 .

[7]  Sheng-Luen Chung,et al.  Robust static output-feedback stabilization for nonlinear discrete-time systems with time delay via fuzzy control approach , 2005, IEEE Trans. Fuzzy Syst..

[8]  Rong-Jong Wai,et al.  Adaptive enhanced fuzzy sliding-mode control for electrical servo drive , 2006, IEEE Transactions on Industrial Electronics.

[9]  Xiaogang Ruan,et al.  On-line adaptive control for inverted pendulum balancing based on feedback-error-learning , 2007, Neurocomputing.

[10]  Bimal K. Bose,et al.  Power Electronics and Ac Drives , 1986 .

[11]  Jorge Angeles,et al.  A New Family of Two-Wheeled Mobile Robots: Modeling and Controllability , 2007, IEEE Transactions on Robotics.

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

[13]  Indra Narayan Kar,et al.  Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics , 2006, Neurocomputing.

[14]  S.X. Yang,et al.  Tracking control of a nonholonomic mobile robot by integrating feedback and neural dynamics techniques , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[15]  Toru Namerikawa,et al.  Robust Stabilization of Running Self-Sustaining Two-wheeled Vehicle , 2007, 2007 IEEE International Conference on Control Applications.

[16]  Peter Kwong-Shun Tam,et al.  Lyapunov-function-based design of fuzzy logic controllers and its application on combining controllers , 1998, IEEE Trans. Ind. Electron..

[17]  Faa-Jeng Lin,et al.  Adaptive fuzzy sliding-mode control for PM synchronous servo motor drives , 1998 .

[18]  Dong Sun,et al.  Orientation control of a differential mobile robot through wheel synchronization , 2005, IEEE/ASME Transactions on Mechatronics.

[19]  Wei Wu,et al.  The application of disturbance observer in two-wheeled mobile robot , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..