Adaptive control of dynamic systems by back propagation networks

Artificial neural networks-especially those using the error back propagation algorithm-are capable of learning to control an unknown plant by autonomously extracting the necessary information from the plant. Following the approach of Psaltis, Sideris, and Yamamura, and Saerens and Soquet, a control architecture based on error back propagation has been developed and trained to control a third order linear and time invariant plant with dead-time Simulation results show that the network is able to invert the plant's behaviour and characteristics, thus learning to control the plant accurately. The time to reach the desired outputs of the plant decreases while learning. It is accelerated by local adaptation of the learning rate.

[1]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[2]  Marco Saerens,et al.  Neural controller based on back-propagation algorithm , 1991 .

[3]  X. Xu,et al.  Effective neural algorithms for the traveling salesman problem , 1991, Neural Networks.

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  Enis Ersü,et al.  A New Concept for Learning Control Inspired by Brain Theory , 1984 .

[6]  Andrew G. Barto,et al.  An approach to learning control surfaces by connectionist systems , 1990 .

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

[8]  Enis Ersü,et al.  Learning Control Structures with Neuron-Like Associative Memory Systems , 1988 .

[9]  Frank Fallside,et al.  An adaptive training algorithm for back propagation networks , 1987 .

[10]  Esther Levin,et al.  Neural network architecture for adaptive system modeling and control , 1991, International 1989 Joint Conference on Neural Networks.

[11]  William B. Levy,et al.  Synaptic modification, neuron selectivity, and nervous system organization , 1985 .

[12]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Enis Ersü,et al.  Software Implementation of a Neuron-Like Associative Memory System for Control Applications , 1982 .

[14]  Enis Ersü,et al.  Control of PH by Use of a Self-Organizing Concept With Associative Memories , 1983 .

[15]  Luís B. Almeida,et al.  Speeding up Backpropagation , 1990 .

[16]  Wolfram Schiffmann,et al.  Performance Evaluation of Evolutionarily Created Neural Network Topologies , 1990, PPSN.

[17]  R. Eckmiller.  Advanced neural computers , 1990 .