Neural Network Controller for Mobile Robot Motion Control

In this paper the neural network-based controller is designed for motion control of a mobile robot. This paper treats the problems of trajectory following and posture stabilization of the mobile robot with nonholonomic constraints. For this purpose the recurrent neural network with one hidden layer is used. It learns relationship between linear velocities and error positions of the mobile robot. This neural network is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control inputs. The performance of the proposed system is investigated using a kinematic model of the mobile robot.

Authors:

• Jasmin Velagic
• Nedim Osmic
• Bakir Lacevic

Keywords:

• Mobile robot
• kinematic model
• neural network
• motion control
• adaptive learning rate.

References:

[1] A. Lacaze, "A neural network planner that prunes its own tree," Term
paper, Knowledge Engineering Department, University of Meryland,
1997.
[2] A. G. Barto, "Neuronlike adaptive elements that can solve difficult
learning control problems," IEEE Transactions on System, Man and
Cybernetics, vol. 13, pp. 834-846, 1983.
[3] Z. Hendzel, "Adaptive Critic Neural Networks for Motion Control of
Wheeled Mobile Robot," Nonlinear Dynamics, vol. 50, no. 4, pp. 849-
855, 2007.
[4] K.S. Narenda, and K. Pathasarathy, "Identification and control of
dynamic systems using neural network," IEEE Transaction on Neural
Networks, vol. 1, no. 1, pp. 4-27, 1990.
[5] D. Nguyen, and B. Widrow, "Neural Networks for Self- Learning
Control Systems," IEEE Control System Magazine, vol. 10, no. 1, pp.
18-23, Feb. 1990.
[6] K. Hornik, M. Stinchombe, and H. White, "Universal Approximation of
an Unknown mapping and its Derivatives Using Multilayer Feedforward
Networks," Neural Networks, vol. 3, 1990.
[7] M. Corradini, G. Ippoliti, and S. Longhi, "Neural Networks Based
Control of Mobile Robots: Development and Experimental Validation",
Journal of Robotic Systems, vol. 20, no. 10, pp. 587-600, 2003.
[8] Z. P. Jiang, E. Lefeber, and H. Nijmeijer, "Saturated stabilization and
tracking of a nonholonomic mobile robot," Systems & Control Letters,
vol. 42, pp. 327-332, 2001.
[9] G. Ramírez,. and S. Zeghloul, "A New Local Path Planner for
Nonholonmic Mobile Robot Navigation in Cluttered Environments, " in
Proc. IEEE Int. Conf. on Robotics and Automation, 2000,p. 2058-2063..
[10] H.G. Tanner, and K.J. Kyriakopoulos, "Backstepping for nonsmooth
systems, " Automatica, vol. 39, 2003, pp. 1259-1265.
[11] J. Velagic, B. Lacevic, and B. Perunicic, "A 3-Level Autonomous
Mobile Robot Navigation System Designed by Using Reasoning/Search
Approaches," Robotics and Autonomous Systems, vol. 54, no. 12, pp.
989-1004, Dec. 2006.
[12] R. Fierro, and F. L. Lewis, "Control of a nonholonomic mobile robot
using neural networks", IEEE Transactions on Neural Networks, vol. 9,
no. 4, pp. 389-400, 1998.
[13] S. X. Yang, and M. Meng, "Real-time fine motion control of robot
manipulators with unknown dynamics," in: Dynamics in Continuous,
Discrete and Impulse Systems, Series B, 2001.
[14] J. Velagic, and M. Hebibovic, "Neuro-Fuzzy Architecture for
Identification and Tracking Control of a Robot," in Proc. The World
Automation Congress - 5th International Symposium on Soft Computing
for Industry ISSCI2004, June 28 - July 1, Sevilla, Spain, paper no.
ISSCI-032 (1:9), 2004.
[15] J. Velagic, B. Lacevic, and M. Hebibovic, "On-Line Identification of a
Robot Manipulator Using Neural Network with an Adaptive Learning
Rate," in Proc. 16th IFAC World Congress, 03-08 June, Prague, Czech
Republic, 2005, paper no. 2684(1-6), 2005.
[16] M. Egerstedt, X. Hu, A. Stotsky, "Control of mobile platforms using a
virtual vehicle approach," IEEE Transactions on Automatic Control ,
vol. 46, no. 11, pp. 1777-1882, 2001.