HIERARCHICAL HYBRID NEUROMORPHIC CONTROL-SYSTEM FOR ROBOTIC MANIPULATORS

In this paper, we present a hierarchical intelligent control system. W e propose this system for generalization of the neural network-based c ontroller using the higher-level control based on AI technology to acq uire knowledge heuristically. Therefore, this system comprises two lev els: a ''learning'' level and an ''adaptation'' level. The neural netw orks are employed for both the long-term learning of the control proce ss and the short-term adaptation of the dynamic process. The learning level has a hierarchical structure for recognition and is used for the strategic planning of robotic manipulation in conjunction with the kn owledge base in order to expand the adaptable range to the environment . New information from the adaptation level updates the learning level through the long-term learning process. On the other hand, the adapta tion is used for the adjustment of the control law to the current stat us of the dynamic process. The motion controller at the adaptation lev el is particularly useful in non-linear dynamical systems having uncer tainty in the environment.