Artificial neural network (NN) can be applied to complex dynamical control system. The multilayer neural network with sigmoid function is often used in this field. But this type of NN cannot learn the patterns additionally. It must learn both unlearned patterns and patterns given before. The neural network based on the distance between patterns (NDP) can memorize patterns additionally and recognize unlearned patterns. Adaptive force control using NDP is proposed in this paper. Hierarchical neuromorphic controller is used, in which the higher level NDP detects changes in environments and activates a corresponding lower level controller. Multilayer neural networks are used at the lower level for the control of unknown plant. Hierarchical structure can enlarge the range of the adaptation autonomously, and learn additionally.

The authors present a scheme for intelligent control of robotic manipulators. This is a hybrid system of neuromorphic control and symbolic control of a robotic manipulator, including a neural network for the servo control and a knowledge-based approximation. The neural network in the servo control level is for numerical manipulation, while the knowledge-based approximation is for symbolic manipulation. In neuromorphic control, the neural network compensates for the nonlinearity of the system and the uncertainty in the environment. The knowledge base develops the control strategy symbolically for the servo level. This is an analogous control system to the human cerebral control structure.>

The authors consider neural network applications to robotic motion control in which the controller is used for the position and force control of robotic manipulators. The proposed neural servo controller is based on a neural network consisting of two hidden layers and input/output layers. The controller can adjust the neural network output to the robot in the forward manner to cooperate with the feedback loop, depending on unknown characteristics of handling objects. In particular, the proposed neural network has delay elements in itself, so that it can learn the dynamics of the system. Simulations are carried out for the case of one- and two-dimensional robotic manipulators. The performance of the proposed neural servo controller is shown in terms of its frequency response, and the robustness against impulsive noises is also shown. The authors propose a fuzzy turbo to avoid stagnation, so that the neural network can learn the dynamical system quickly