Adaptive control of dynamic systems by back propagation networks

✍ Scribed by Wolfram H. Schiffmann; H. Willi Geffers

• Publisher: Elsevier Science
• Year: 1993
• Tongue: English
• Weight: 624 KB
• Volume: 6
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/s0893-6080(05)80055-3

✦ Synopsis

Artificial neural networks--especially those using the error bach" propagation algorithm--are capable of learning to control an unknown plant by atttonomousl), extracting the necessary information from the plant. Following the approach of Psaltis, Sideris, and Yamamura, and Saerens and Soqttel, a control architecture based on error back propagation has been developed and trained to control a third order linear and time invariant plant with deadtime. 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 otltpttts of the plant decreases while learning. It is accelerated by local adaptation of the learning rate.