Hierarchical hybrid neuromorphic control system for robotic manipulator

Summary form only given, as follows. The authors discuss a hierarchical hybrid neuromorphic control system for intelligent control of robotic manipulators and for acquiring knowledge and learning. This robotic motion control system comprises two levels: a learning level and an adaptation level. Neural networks are employed for both levels. The learning level has a hierarchical structure and is used for the strategic planning of the robotic manipulator in conjunction with the neural knowledge database (NKDB) in order to enlarge the adaptation range. The proposed NKDB can infer an unknown fact from prior knowledge for strategic planning and is updated by the recent information from the adaptation level, the neural servo controller, 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. The initial states of the adaptation level are given by the NKDB. Experiments and simulations show that, if the NKDB recognizes objects correctly, the NSC can adapt easily, but if not the NSC tries to adapt objects and gain the force sensing information so as to acquire new knowledge for the NKDB