Intelligent Learning Control System via Unfalsified Control

In this paper, we present an intelligent learning algorithm via unfalsified control. Using this approach, the algorithm can distinguish quality of candidate controllers by experimental plant input and output data. The key is at how to compute the new fictitious error signal. Any artificial intelligence technique can be applied using the proposed concept. Finally, we discuss an open problem whether or not the algorithm can achieve human intelligence.