Toyokazu Mitsuoka

Skill based control by using fuzzy neural network for hierarchical intelligent control

A novel architecture of an intelligent control system for robotic manipulators is presented. The system is an integrated approach of neuromorphic and symbolic control of a robotic manipulator, including an applied neural network for the servo control, a knowledge-based approximation, and a fuzzy neural network (FNN) for skill-based control. The neural network in the servo control level is the numerical manipulation, while the knowledge-based part is the symbolic manipulation. In neuromorphic control, the neural network compensates for the nonlinearity of the system and the uncertainty in the environment. The knowledge-based part develops the control strategy symbolically for the servo level. The FNN is used between the servo control level and the knowledge-based part to link numerals to symbols and express human skills through learning. This system is analogous to the human cerebral control structure combined with reflex action.<<ETX>>

Neuromorphic control: adaptation and learning

A structure for a neural network-based robotic motion controller is presented. Simulations of both position and force servos are carried out, and the approach is shown to be useful for a nonlinear system in an uncertain environment. The neural network comprises a four-layer network, including input/output layers and two hidden layers. Time delay elements are included in the first hidden layer, so that the neural network can learn dynamics of the system. The authors also implement a new learning method based on fuzzy logic, which is useful to accelerate learning and improve convergence. >

Neural network application for robotic motion control-adaptation and learning

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

Adaptation and learning for robotic manipulator by neural network

Neural network applications for robotic motion control in which the controller is applicable to position and force control of robotic manipulators are addressed. The proposed neural servo controller is based on a neural network which consists of input/output layers and two hidden layers, and which has time delay elements in its first hidden layer. This neural network can learn the complex dynamics of the system in forward manner to cooperate with the feedback loop, depending on the unknown characteristics of objects to be handled. A variable learning method, fuzzy turbo, which is based on fuzzy set theory, is proposed. This method can avoid stagnation during the learning process and has insensitive characteristics at a stable extreme, so that the neural network can learn the dynamical system quickly. Simulations are carried out for the case of force control handling of unknown objects and trajectory control handling of unknown payloads of a two-dimensional robotic manipulator.<<ETX>>

New strategy for hierarchical intelligent control of robotic manipulator-hybrid neuromorphic and symbolic control

The authors present a novel scheme for intelligent control which is an integrated approach of the neuromorphic and symbolic control of a robotic manipulator, including an applied neural network (NN) for servo manipulation, while the knowledge-based part is symbolic manipulation. This control system is analogous to the human cerebral control structure. Experiments and simulations demonstrated that the NN can connect the numerical real world with the symbolic artificial intelligence world.<<ETX>>

Pipeline inspection and maintenance by applications of computer data processing and robotic technology

Abstract It is very important to detect leakage and flaws on pipelines without hampering pipeline operations by data processing and robotic technology. In this paper, two topics are described, a leak detection method by intensive computer data processing and a flaw detection method by a mobile inspection robot. (i) A new method for remote leak detection and localization in a pipeline system is presented by applying the prewhitening filter method formulated by autoregressive (AR) modelling to ultra acoustic signals, which are obtained from the acoustic emission (AE) sensors installed on the pipeline at certain intervals. (ii) A fully automatic robot for pipeline inspection is built to detect flaws and defects. The robot can move over the outside along pipelines with inspection sensors and has varied mobility and maneuverability, such as passing over flanges and so on. It can also detect small defects and flaws by scanning the surface of pipes.

Hybrid symbolic and neuromorphic control for hierarchical intelligent control

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.<<ETX>>

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>>

Neuromorphic sensing and control-applications to position, force, and impact control for robotic manipulators

The authors present a neural network (NN)-based approach for sensing and control of a robotic manipulator. They corroborate the effectiveness of the proposed approach for impact control of a robotic manipulator. Collisions are very quick phenomena and have strong nonlinearity. Therefore, it is difficult to sense collisions and to control the robotic manipulator undergoing collisions. The proposed approach has robustness against the impact force. It also has effectiveness in sensing and recognition of collisions by using a proximity sensor. In this case, the NN-based control can acquire desirable manipulation of its own accord, so as to avoid the impact.<<ETX>>

Neuromorphic sensing and control-impact control of robotic manipulator

A new approach for impact control of a robotic manipulator by a neural network (NN)-based controller is presented. Collisions are very quick phenomena and have strong nonlinearity. Therefore, it is difficult to sense collisions and to control a robotic manipulator undergoing collisions. The proposed approach has robustness against the impact force. It also has capabilities for sensing and perception of collisions by using a proximity sensor. In this case, the NN-based controller can use a nonlinear feedback in approaching the object to avoid impact. The NN-based control acquires these capabilities of its own accord.<<ETX>>