Adaptation and learning for hierarchical intelligent control

The authors discuss a novel strategy for hierarchical intelligent control. They propose this strategy for a neural-network-based controller to be generalized with the higher level control based on artificial intelligence and to acquire knowledge heuristically. This system comprises two levels: a learning level and an adaptation level. The neural networks are used for both levels. The learning level has a hierarchical structure for recognition and planning, and is used for the strategy of robotic manipulation in conjunction with the knowledge base in order to expand the adaptive range. The recent information from the adaptation level updates the learning level through a long-term learning process. On the other hand, the adaptation is used for the adjustment of the control law to the current status of the dynamic process.<<ETX>>