HIERARCHICAL HYBRID NEUROMORPHIC CONTROL-SYSTEM FOR ROBOTIC MANIPULATORS

In this paper, we present a hierarchical intelligent control system. We propose this system for generalization of the neural network-based controller using the higher-level control based on AI technology to acquire knowledge heuristically. Therefore, this system comprises two levels: a ''learning'' level and an ''adaptation'' level. The neural networks are employed for both the long-term learning of the control process 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 knowledge 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 adaptation is used for the adjustment of the control law to the current status of the dynamic process. The motion controller at the adaptation level is particularly useful in non-linear dynamical systems having uncertainty in the environment.