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SICE Annual Conference

作者： T. Nanayakkara K. Watanabe K. Kiguchi K. Izumi Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Japan Department of Mechanical Engineering Faculty of Science and Engineering Saga University Saga Japan Graduate School of Science and Engineering Faculty of Engineering Systems and Technology

A method for the identification of complex nonlinear dynamics of a multilink robot manipulator using Runge-Kutta-Gill neural networks (RKGNN) in the absence of input torque information is proposed. The RKGNN constructed using shape adaptive radial basis functions (RBF) are trained using an evolutionary algorithm. Due to the fact that the main function network is divided into subnetworks to represent detailed properties of the dynamics of a manipulator, the neural networks have greater information processing capacity and they can be tested for properties such as positive definiteness of the inertia matrix. Dynamics of an industrial seven-link manipulator are identified using only input-output position and their velocity data. Promising experimental control results are obtained to prove the ability of the proposed method in capturing highly nonlinear dynamics of a multilink manipulator in an effective manner.

关键词： Neural networks

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Flexible motion of pull-out-work by articulated robot arm based on forcefree control

Flexible motion of pull-out-work by articulated robot arm ba...

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Annual Conference of Industrial Electronics Society

作者： D. Kushida M. Nakamura S. Goto N. Kyura Department of Advanced Systems Control Engineering Saga University Honjomachi Saga Japan Department of Electrical Engineering Kinki University Iizuka Japan

Flexible motion of an industrial articulated robot arm is realized by using force-free control, in which the flexible motion means that the robot arm is actuated by external force passively. Force-free control enables the natural motion of the industrial articulated robot arm as if the robot arm exists in a circumstance of non-gravity and non-friction condition. The effectiveness of the force-free control was assured by the application of pull-out-work on a 2-degree-of-freedom articulated robot arm.

关键词： Force control Motion control Service robots Defense industry Manipulators Industrial control Servomechanisms Robotic assembly control systems Robot sensing systems

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Time series prediction by a neural network model based on the bi-directional computation style

Time series prediction by a neural network model based on th...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Wakuya J.M. Zurada Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Saga Japan Department of Electrical and Computer Engineering University of Louisville Louisville KY USA

A number of neural network models and training procedures for time series prediction have been proposed in the technical literature. These models typically used uni-directional computation flow or its modifications. In this study a novel concept of bi-directional computation style is proposed and applied to prediction tasks. Since the coupling effects between the future prediction system and the past prediction system help the proposed model improve its performance, it is found that the prediction score is better than with the traditional uni-directional method. The bi-directional predicting architecture has been found to perform better than the conventional one when tested with standard benchmark sunspots data.

关键词： Neural networks Predictive models Bidirectional control Computer networks Computer architecture Neurons Home computing Performance evaluation Benchmark testing Computer simulation

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Adaptive navigation control for a quadruped robot based on ART and fuzzy logic

Adaptive navigation control for a quadruped robot based on A...

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Annual Conference of Industrial Electronics Society

作者： Xuedong Chen K. Watanabe K. Kiguchi K. Izumi Graduate School of Science and Engineering Faculty of Engineering Systems and Technology Saga University Saga Japan Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Saga Japan Department of Mechanical Engineering Faculty of Science and Engineering Saga University Saga Japan

An adaptive navigation control problem is presented for a quadruped robot in cluttered environments by incorporating the capability of adaptive resonance theory (ART) in stable category recognition into the fuzzy logic control. An ART-based neural network is introduced as an environment identifier for the purpose of adaptive selection of the adequate rule base for the fuzzy controller. Therefore, the proposed adaptive control scheme for the robot navigation is implemented by the adaptive fuzzy rule base in response to changes of the robot's environment, which can be fine observed by the proposed environment identifier. Some simulation results are presented to illustrate the effectiveness of the proposed algorithm.

关键词： Programmable control Adaptive control Navigation Robots Subspace constraints Fuzzy control Resonance Fuzzy logic Neural networks Fuzzy neural networks

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Assistant force compensation for hand movement of patients by using exogenous signals and/or neurophysiological signals

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Artificial Life and Robotics 2000年第4期4卷 227-232页

作者： Bai, Ou Nakamura, Masatoshi Shibasaki, Hiroshi Department of Advanced Systems Control Engineering Saga University Saga Japan Department of Neurology and Human Brain Research Center Kyoto University Graduate School of Medicine Kyoto Japan

Because functional diseases of the brain can cause disabilities related to human movement control, a compensation method was developed for improving the performance of hand movements. The compensation for human hand movements can be carried out by adding an assistant force that is generated from artificial equipment attached to a human arm. From the experiment on visual target tracking, it was found that the tracking trajectory was adequately represented by a dynamic model of the motion of an articulated industrial robot arm, and the different abilities for movement control among healthy people and patients were classified by different model parameters as position loop gain, velocity loop gain, and response delay. Dynamic force compensation was approached by considering the different control features of the patients. The effectiveness of the proposed compensation method was verified in a simulation study on an actual industrial robot arm. A human-machine interface, e.g., a brain-computer interface (BCI), for realizing the control of artificial equipment to compensate for human hand movements is also presented and discussed.

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Design of an exoskeletal robot for human elbow motion support

Design of an exoskeletal robot for human elbow motion suppor...

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IEEE International Workshop on Intelligent Robots and systems (IROS)

作者： K. Kiguchi S. Kariga T. Niwa K. Watanabe K. Izumi T. Fukuda Department of Advanced Control Systems Engineering Saga University Saga Japan Department of Mechanical Engineering Saga University Saga Japan Center for Cooperative Research in Advanced Science and Tech University of Nagoya Nagoya Japan

We are stepping into an aging society rapidly. In that society, it is important that physically weak people are able to take care of themselves. In this paper, we introduce a 1DOF exoskeletal robot to support the elbow motion of physically weak people. Fuzzy control has been applied to control the exoskeletal robot system based on vague biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated force by the human subject's wrist during the human elbow motion have been used as input information of the fuzzy controller.

关键词： Humans Elbow Fuzzy control Aging Robot control Biological control systems control systems Skin Electromyography Signal generators

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Generation of adjustment strategy of fuzzy-neural force controllers using genetic algorithms with fuzzy evaluation

Generation of adjustment strategy of fuzzy-neural force cont...

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Annual Conference of Industrial Electronics Society

作者： K. Kiguchi K. Watanabe K. Izumi T. Fukuda Department of Advanced Control Systems Engineering Saga University Saga Japan Center of Cooperative Research in Advanced Science and Technology University of Nagoya Nagoya Japan

This paper presents an effective force control method in which a fuzzy-neuro force controller is automatically adjusted in accordance with the unknown dynamics of an environment using a neural network. The adjustment strategy of the fuzzy-neural force controller, according to the environment dynamics, is automatically generated by the neural network in off-line manner using genetic algorithms with fuzzy evaluation. The effectiveness of the proposed force controller is evaluated by computer simulation with a 3-DOF planar robot manipulator model.

关键词： Force control Automatic generation control Automatic control Neural networks Genetic algorithms Fuzzy control Fuzzy neural networks Computer simulation Robotics and automation Manipulators

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controlling multi-link manipulators by fuzzy selection of dynamic models

Controlling multi-link manipulators by fuzzy selection of dy...

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Annual Conference of Industrial Electronics Society

作者： T. Nanayakkara K. Watanabe K. Kiguchi K. Izumi Faculty of Engineering Systems and Technology Graduate School of Science and Engineering Saga University Honjomachi Saga Japan Department of Advanced Systems Control Engineering Graduage School of Science and Engineering Saga University Honjomachi Saga Japan Department of Mechanical Engineering Faculty of Science and Engineering Saga University Honjomachi Saga Japan

A method for the identification of complex nonlinear dynamics of a multi-link robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs) in the absence of input torque information is proposed. The RKGNNs constructed using shape adaptive radial basis functions are trained by an evolutionary algorithm. Due to the fact that the main function network is divided into sub-networks to represent the dynamic properties of the manipulator, the neural networks have greater information, processing capacity and can be tested for properties such as positive definiteness of the inertia matrix. Dynamics of a three-link manipulator are identified using only their input-output position and velocity data, and promising control results are obtained to prove the effectiveness of the proposed method in capturing highly nonlinear dynamics of a multi-link manipulator.

关键词： Manipulator dynamics Fuzzy control Torque control Velocity control Neural networks Robots Sampling methods PD control Proportional control control systems

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Teaching system for a polishing robot using a game joystick

Teaching system for a polishing robot using a game joystick

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SICE Annual Conference

作者： F. Nagata K. Watanabe Interior Design Research Institute Fukuoka Industrial Technology Center Fukuoka Japan Department of Advanced Systems Control Engineering Graduate School of Scinence and Engineering Saga University Saga Japan

In this paper, a user-friendly teaching system for a polishing robot using a game joystick is proposed. The teaching process is as follows. First, a zigzag path, which depends on both sizes of each object and polishing tool, is prepared. Next, the polishing robot whose control system consists of an impedance model following force controller profiles the object's surface using the zigzag path. In this case, the operator has only to control the orientation of the polishing tool using a usual game joystick. Further, since the impedance model following force controller keeps the contact force to be a desired value, the operator has to give no attention to a sudden overload or noncontact state. Through the above process, the desired trajectory is automatically obtained as the data including both continuous information of the position and its normal vector of the object's surface along the zigzag path. After that, the robot can achieve the polishing task without any assists of the operator by referring to the obtained trajectory. The effectiveness of the proposed method is proved through some experiments on polishing task of a woody material.

关键词： Education Educational robots

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Combining First Principles with Black-Box Techniques for Reaction systems

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IFAC Proceedings Volumes 2000年第15期33卷 427-432页

作者： Libei Chen Yves Hontoir Dexian Huang Jie Zhang A. Julian Morris Centre for Systems Engineering and Applied Mechanics Catholic University of Louvain Belgium SOLVAY Advanced Process Control Chemicals Corporate Research and Technology Brussels Belgium Department of Chemical and Process Engineering The University of Newcastle-upone-Tyne UK Department of Automation Tsinghua University Beijing China

A control oriented hybrid model structure combining first principles models with standard black-box techniques for modelling nonlinear dynamics of reaction systems is presented in this paper. The approach is formulated in a general framework for continuous stirred tank reactors and analyzed in details through a case study of a reactive distillation column. The approach is based on easily established mass balance equations, the stoichiometry of the system as well as model reduction techniques. The choice of combined inputs and the model structure is motivated by some general control objectives for this class of systems. A progressive identification of this model structure can be performed when a dominant part exists. The application to real process data is presented. This model structure has been successfully used in an IMC scheme for an industrial reactive distillation column.

关键词： Hybrid modelling neural networks process optimization reaction systems reactive distillation column

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