DHP Adaptive Critic Motion Control of Autonomous Wheeled Mobile Robot

Autonomous drive of wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis of the WMR system. This paper presents the dual heuristic programming (DHP) adaptive critic design of the motion control system that enables WMR to achieve the control purpose simply by learning through trial. The design consists of an adaptive critic velocity neuro-control loop and a posture neuro-control loop. The neural weights in the velocity neuro-controller (VNC) are corrected with the DHP adaptive critic method. The designer simply expresses the control objective with a utility function. The VNC learns by sequential optimization to satisfy the control objective. The posture neuro-controller (PNC) approximates the inverse velocity model of WMR so as to map planned positions to desired velocities. Supervised drive of WMR in variant velocities supplies training samples for the PNC and VNC to setup the neural weights. In autonomous drive, the learning mechanism keeps improving the PNC and VNC. The design is evaluated on an experimental WMR. The excellent results make it certain that the DHP adaptive critic motion control design enables WMR to develop the control ability autonomously.

[1]  Peter Kwong-Shun Tam,et al.  Position control for wheeled mobile robots using a fuzzy logic controller , 1999, IECON'99. Conference Proceedings. 25th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.99CH37029).

[2]  Y. W. Lee,et al.  Optimal tracking control of a nonholonomic mobile robot , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[3]  D. T. Greenwood.  Principles of dynamics , 1965 .

[4]  Roberto A. Santiago,et al.  Adaptive critic designs: A case study for neurocontrol , 1995, Neural Networks.

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

[6]  Xiaoping Yun,et al.  Internal dynamics of a wheeled mobile robot , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[7]  A. Bloch,et al.  Control and stabilization of nonholonomic dynamic systems , 1992 .

[8]  K. Kozlowski,et al.  Fuzzy logic implementation in mobile robot control , 2001, Proceedings of the Second International Workshop on Robot Motion and Control. RoMoCo'01 (IEEE Cat. No.01EX535).

[9]  Bogdan M. Wilamowski.  Neural network architectures and learning , 2003, IEEE International Conference on Industrial Technology, 2003.

[10]  Wei-Song Lin,et al.  Autonomous mobile robot navigation using stereovision , 2005, IEEE International Conference on Mechatronics, 2005. ICM '05..

[11]  W.-S. Lin,et al.  Hierarchical fuzzy control for autonomous navigation of wheeled robots , 2005 .

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

[13]  Ernest Barany,et al.  Adaptive control of nonholonomic robotic systems , 1998, J. Field Robotics.

[14]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[15]  Paul J. Werbos,et al.  Approximate dynamic programming for real-time control and neural modeling , 1992 .

[16]  Teresa M Adams,et al.  A fuzzy navigation system for mobile construction robots , 1997 .

[17]  T. T. Shannon,et al.  Application considerations for the DHP methodology , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[18]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[19]  Frank L. Lewis,et al.  Control of a nonholonomic mobile robot using neural networks , 1998, IEEE Trans. Neural Networks.