Neural dynamics based full-state tracking control of a mobile robot

In this paper, a novel biologically inspired approach to real-time tracking control of a nonholonomic mobile robot is proposed. The proposed algorithm incorporates a neural dynamics model derived from a biological membrane equation with the conventional full-state tracking control technique. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow desired trajectories. The proposed approach resolves the speed jump problem existing in some previous tracking controllers. In addition, it can track both continuous and discrete paths. The practicality and effectiveness of the proposed tracking controller were demonstrated by simulation and comparison results.

[1]  Li-Xin Wang.  Modeling and control of hierarchical systems with fuzzy systems , 1997, Autom..

[2]  Frank L. Lewis,et al.  Control of a nonholonomic mobile robot: backstepping kinematics into dynamics , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[3]  W. L. Nelson,et al.  Continuous steering-function control of robot carts , 1989 .

[4]  Satoshi Murata,et al.  Steering Control Algorithm for Autonomous Vehicle , 1989 .

[5]  Nick Pears,et al.  Mobile robot tracking of pre-planned paths , 2001, Adv. Robotics.

[6]  Bor-Sen Chen,et al.  Adaptive tracking control design of nonholonomic mechanical systems , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[7]  George J. Pappas,et al.  Dynamic Modelling and Tracking Control of Nonholonomic Wheeled Vehicles , 1993 .

[8]  Edward Tunstel,et al.  Soft computing-based design and control for mobile robot path tracking , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[9]  Ingemar J. Cox,et al.  Blanche-an experiment in guidance and navigation of an autonomous robot vehicle , 1991, IEEE Trans. Robotics Autom..

[10]  Vijay Kumar,et al.  Control of Mechanical Systems With Rolling Constraints , 1994, Int. J. Robotics Res..

[11]  Steven A. Velinsky,et al.  On the Tracking Control of Differentially Steered Wheeled Mobile Robots , 1997 .

[12]  Gyu-Hyeong Cho,et al.  Improved PWM method of forced commutated cycloconvertors , 1989 .

[13]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[14]  S. S. Lee,et al.  A fast tracking error control method for an autonomous mobile robot , 1993, Robotica.

[15]  Kostas J. Kyriakopoulos,et al.  Navigation of nonholonomic vehicles in complex environments with potential fields and tracking , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[16]  Henk Nijmeijer,et al.  Tracking Control of Mobile Robots: A Case Study in Backstepping , 1997, Autom..

[17]  Manuel Mazo,et al.  Neural Control of the Movements of a Wheelchair , 1999, J. Intell. Robotic Syst..

[18]  Frank L. Lewis,et al.  Control of a nonholomic mobile robot: Backstepping kinematics into dynamics , 1997 .

[19]  Fumio Miyazaki,et al.  A stable tracking control method for an autonomous mobile robot , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[20]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[21]  Yutaka Kanayama,et al.  Trajectory generation for mobile robots , 1984 .

[22]  Max Q.-H. Meng,et al.  Neural network approaches to dynamic collision-free trajectory generation , 2001, IEEE Trans. Syst. Man Cybern. Part B.