Variable learning rate neuromorphic guidance controller for automated transit vehicles

This paper presents the development and the performance of a guidance controller for automated transit vehicles operating at high speeds. The controller is based on a feedforward neural network with the back propagation algorithm for learning. Traditional back-propagation neural controllers make use of a fixed learning factor. Herein, a controller with variable learning rate, whose value depends on the operating parameters of the vehicle is described. The operating parameters considered are the linear speed of the vehicle, the instantaneous position and the orientation offsets of the longitudinal axis of the vehicle with respect to the track. Empirical relationships are derived to compute the suitable learning rates in real-time. Simulation studies illustrate that the vehicle recovers from initial offsets and follows the track within few seconds for vehicle speeds less than 4.0 m/s (14 km/hr).

[1]  Tarun Khanna,et al.  Foundations of neural networks , 1990 .

[2]  F.-C. Chen,et al.  Back-propagation neural networks for nonlinear self-tuning adaptive control , 1990, IEEE Control Systems Magazine.

[3]  P. K. Simpson.  Fuzzy Min-Max Neural Networks-Part 1 : Classification , 1992 .

[4]  Kumpati S. Narendra,et al.  Adaptive control using neural networks , 1990 .

[5]  R. M. H. Cheng,et al.  A new control strategy for tracking in mobile robots and AGVs , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[6]  Ingemar J. Cox,et al.  Autonomous Robot Vehicles , 1990, Springer New York.

[7]  R. M. H. Cheng,et al.  Synthesis of an optimal control law for path tracking in mobile robots , 1992, Autom..

[8]  K. Asakawa,et al.  Mobile robot control by a structured hierarchical neural network , 1990, IEEE Control Systems Magazine.

[9]  B. Widrow,et al.  The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.

[10]  Panos J. Antsaklis,et al.  Neural networks for control systems , 1990, IEEE Trans. Neural Networks.

[11]  Y.-F. Huang,et al.  Learning algorithms for perceptions using back-propagation with selective updates , 1990, IEEE Control Systems Magazine.

[12]  B. Bavarian,et al.  Introduction to neural networks for intelligent control , 1988, IEEE Control Systems Magazine.

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

[14]  Kuldip S. Rattan,et al.  A Multi-layered Motion Controller for a Mobile Robot Implemented With Fuzzy Logic , 1993, 1993 American Control Conference.

[15]  H. Saxen,et al.  A feed-forward artificial neural network as a simulator for a chemical process , 1991, Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences.

[16]  Kumpati S. Narendra,et al.  Neural networks in control systems , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[17]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[18]  Ignacio Bellido,et al.  Backpropagation Growing Networks: Towards Local Minima Elimination , 1991, IWANN.

[19]  John J. Helferty,et al.  Neuromorphic control of robotic manipulators , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[20]  R. M. H. Cheng,et al.  Neuromorphic controller for AGV steering , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[21]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[22]  H. Hatwal,et al.  An Optimal Control Approach to the Path Tracking Problem for an Automobile , 1986 .

[23]  Ramesh Rajagopalan,et al.  Guidance control for automatic guided vehicles employing binary camera vision , 1991 .

[24]  Karsten Berns,et al.  An application of a backpropagation network for the control of a tracking behavior , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.