The Experts below are selected from a list of 14490 Experts worldwide ranked by ideXlab platform
T. Yamada - One of the best experts on this subject based on the ideXlab platform.
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Remarks on hybrid neural Network Controller using different convergence speeds
Proceedings of 1995 IEEE International Conference on Robotics and Automation, 1995Co-Authors: T. YamadaAbstract:A neural Network requires the partial derivative of a plant output with regard to its input. However, it is unknown for an unknown nonlinear plant. This paper proposes a hybrid neural Network Controller which overcomes this problem and which compensates online neural Networks for plant fluctuation by using an identifier and a Controller with different convergence speeds.
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Remarks on neural Network Controller for a inverse dynamics of many-to-one plant
Proceedings IEEE Conference on Industrial Automation and Control Emerging Technology Applications, 1995Co-Authors: T. YamadaAbstract:Neural Networks have excellent characteristics such as a learning capability and a flexible structure. Several types of a neural Network Controller have been studied in order to incorporate these characteristics in servo Controllers. These neural Network Controllers are expected to apply to any nonlinear plant. However, the input value of some nonlinear object plants is not one which corresponds to one output value. Most neural Network Controllers have to express the inverse dynamics of many-to-one plants. However the neural Network output can only express one value. In this case the neural Network appears to express only a part of the inverse dynamics. Therefore, one should investigate the characteristics of the neural Network Controller for a many-to-one plant. The author selects two types of neural Network Controller for investigation. One was proposed by K.S. Narendra and it has the identification stage and the control stage. The other Controller is proposed by the author and it learns the inverse dynamics of the object plant in cooperation with control. Simulation results confirm the characteristics of these Controllers when they are applied to a many-to-one plant.
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Application of learning type feedforward feedback neural Network Controller to dynamic systems
Proceedings of 1993 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS '93), 1993Co-Authors: T. Yamada, T. YabutaAbstract:Feedforward feedback neural Network Controllers have been proposed. These Controllers use the sum of neural Network output and conventional Controller output as the object plant input. The authors proposed an adaptive type feedforward feedback Controller using a neural Network and confirmed its characteristics. However, th learning type neural Network Controller is also attractive for servo control applications. Therefore, this paper proposes a learning type feedforward feedback neural Network Controller for discrete time dynamic systems. Simulated results using a second order plant confirm the characteristics of the proposed Controller.
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IROS - Application of learning type feedforward feedback neural Network Controller to dynamic systems
Proceedings of 1993 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS '93), 1993Co-Authors: T. Yamada, T. YabutaAbstract:Feedforward feedback neural Network Controllers have been proposed. These Controllers use the sum of neural Network output and conventional Controller output as the object plant input. The authors proposed an adaptive type feedforward feedback Controller using a neural Network and confirmed its characteristics. However, th learning type neural Network Controller is also attractive for servo control applications. Therefore, this paper proposes a learning type feedforward feedback neural Network Controller for discrete time dynamic systems. Simulated results using a second order plant confirm the characteristics of the proposed Controller.
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Nonlinear neural Network Controller for dynamic system
[Proceedings] IECON '90: 16th Annual Conference of IEEE Industrial Electronics Society, 1990Co-Authors: T. Yamada, T. YabutaAbstract:A learning type of Controller using a neural Network is proposed and compared with a conventional Controller. The learning neural Network Controller can use not only quadratic error but also a more general cost function. A practical design method is proposed, and the advantages of the learning type of neural Network Controller in comparison with the adaptive type are discussed. Simulated and experimental results confirm the realization of nonlinear optimal control using the proposed Controller.
Masakazu Fujii - One of the best experts on this subject based on the ideXlab platform.
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image based visual servoing using takagi sugeno fuzzy neural Network Controller
International Symposium on Intelligent Control, 2007Co-Authors: Miao Hao, Zengqi Sun, Masakazu FujiiAbstract:In this paper, a Takagi-Sugeno fuzzy neural Network Controller (TS-FNNC) based image based visual servoing (IBVS) method is proposed. Firstly, the eigenspace based image compression method is explored which is chosen as the global feature transformation method. After that, the inner structure, performance and training method of T-S neural Network Controller are discussed respectively. Besides, the whole architecture of the TS-FNNC is investigated. No artificial mark is needed in the visual servoing process. No priori knowledge of the robot kinetics and dynamics or camera calibration is needed. The method is implemented and validated on a Motoman UP6 based eye-in-hand platform and the experimental results are also reported in the end.
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ISIC - Image Based Visual Servoing Using Takagi-Sugeno Fuzzy Neural Network Controller
2007 IEEE 22nd International Symposium on Intelligent Control, 2007Co-Authors: Miao Hao, Zengqi Sun, Masakazu FujiiAbstract:In this paper, a Takagi-Sugeno fuzzy neural Network Controller (TS-FNNC) based image based visual servoing (IBVS) method is proposed. Firstly, the eigenspace based image compression method is explored which is chosen as the global feature transformation method. After that, the inner structure, performance and training method of T-S neural Network Controller are discussed respectively. Besides, the whole architecture of the TS-FNNC is investigated. No artificial mark is needed in the visual servoing process. No priori knowledge of the robot kinetics and dynamics or camera calibration is needed. The method is implemented and validated on a Motoman UP6 based eye-in-hand platform and the experimental results are also reported in the end.
T. Yabuta - One of the best experts on this subject based on the ideXlab platform.
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Application of learning type feedforward feedback neural Network Controller to dynamic systems
Proceedings of 1993 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS '93), 1993Co-Authors: T. Yamada, T. YabutaAbstract:Feedforward feedback neural Network Controllers have been proposed. These Controllers use the sum of neural Network output and conventional Controller output as the object plant input. The authors proposed an adaptive type feedforward feedback Controller using a neural Network and confirmed its characteristics. However, th learning type neural Network Controller is also attractive for servo control applications. Therefore, this paper proposes a learning type feedforward feedback neural Network Controller for discrete time dynamic systems. Simulated results using a second order plant confirm the characteristics of the proposed Controller.
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IROS - Application of learning type feedforward feedback neural Network Controller to dynamic systems
Proceedings of 1993 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS '93), 1993Co-Authors: T. Yamada, T. YabutaAbstract:Feedforward feedback neural Network Controllers have been proposed. These Controllers use the sum of neural Network output and conventional Controller output as the object plant input. The authors proposed an adaptive type feedforward feedback Controller using a neural Network and confirmed its characteristics. However, th learning type neural Network Controller is also attractive for servo control applications. Therefore, this paper proposes a learning type feedforward feedback neural Network Controller for discrete time dynamic systems. Simulated results using a second order plant confirm the characteristics of the proposed Controller.
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Nonlinear neural Network Controller for dynamic system
[Proceedings] IECON '90: 16th Annual Conference of IEEE Industrial Electronics Society, 1990Co-Authors: T. Yamada, T. YabutaAbstract:A learning type of Controller using a neural Network is proposed and compared with a conventional Controller. The learning neural Network Controller can use not only quadratic error but also a more general cost function. A practical design method is proposed, and the advantages of the learning type of neural Network Controller in comparison with the adaptive type are discussed. Simulated and experimental results confirm the realization of nonlinear optimal control using the proposed Controller.
Miao Hao - One of the best experts on this subject based on the ideXlab platform.
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image based visual servoing using takagi sugeno fuzzy neural Network Controller
International Symposium on Intelligent Control, 2007Co-Authors: Miao Hao, Zengqi Sun, Masakazu FujiiAbstract:In this paper, a Takagi-Sugeno fuzzy neural Network Controller (TS-FNNC) based image based visual servoing (IBVS) method is proposed. Firstly, the eigenspace based image compression method is explored which is chosen as the global feature transformation method. After that, the inner structure, performance and training method of T-S neural Network Controller are discussed respectively. Besides, the whole architecture of the TS-FNNC is investigated. No artificial mark is needed in the visual servoing process. No priori knowledge of the robot kinetics and dynamics or camera calibration is needed. The method is implemented and validated on a Motoman UP6 based eye-in-hand platform and the experimental results are also reported in the end.
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ISIC - Image Based Visual Servoing Using Takagi-Sugeno Fuzzy Neural Network Controller
2007 IEEE 22nd International Symposium on Intelligent Control, 2007Co-Authors: Miao Hao, Zengqi Sun, Masakazu FujiiAbstract:In this paper, a Takagi-Sugeno fuzzy neural Network Controller (TS-FNNC) based image based visual servoing (IBVS) method is proposed. Firstly, the eigenspace based image compression method is explored which is chosen as the global feature transformation method. After that, the inner structure, performance and training method of T-S neural Network Controller are discussed respectively. Besides, the whole architecture of the TS-FNNC is investigated. No artificial mark is needed in the visual servoing process. No priori knowledge of the robot kinetics and dynamics or camera calibration is needed. The method is implemented and validated on a Motoman UP6 based eye-in-hand platform and the experimental results are also reported in the end.
K. Jezernik - One of the best experts on this subject based on the ideXlab platform.
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IROS - Trajectory tracking neural Network Controller for a robot mechanism and Lyapunov theory of stability
Proceedings of IEEE RSJ International Conference on Intelligent Robots and Systems (IROS'94), 1994Co-Authors: R. Safaric, K. JezernikAbstract:In this paper a neural Network Controller for trajectory tracking for a two DOF SCARA robot mechanism is presented. Two types of neural Network Controllers have been built: a joint space neural Network Controller and a task space neural Network Controller. The two Controllers have been compared with the computed torque method Controller, also in the joint and task space. The four Controllers were tested on a real robot mechanism. Lyapunov theory, for deriving the adaptation law, or the learning algorithm of neural Networks, was used to prove the robot system stability with a neural Network Controller. >
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Transputer based trajectory tracking neural Network Controller for a robot mechanism
Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics, 1994Co-Authors: R. Safaric, A. Hace, K. JezernikAbstract:The paper presents a neural Network Controller for trajectory tracking for a two D.O.F. SCARA robot mechanism. Two types of neural Network Controllers were built: a joint space neural Network Controller and a task space neural Network Controller. The two Controllers were compared with a computed torque method Controller in a joint as well as task space. The four Controllers were tested on a real robot mechanism. The Lyapunov theory for deriving the adaptation law, or the learning algorithm of neural Networks, was used to prove the robot system stability with a neural Network Controller.
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Trajectory tracking neural Network Controller for a robot mechanism and Lyapunov theory of stability
Proceedings of IEEE RSJ International Conference on Intelligent Robots and Systems (IROS'94), 1994Co-Authors: R. Safaric, K. JezernikAbstract:In this paper a neural Network Controller for trajectory tracking for a two DOF SCARA robot mechanism is presented. Two types of neural Network Controllers have been built: a joint space neural Network Controller and a task space neural Network Controller. The two Controllers have been compared with the computed torque method Controller, also in the joint and task space. The four Controllers were tested on a real robot mechanism. Lyapunov theory, for deriving the adaptation law, or the learning algorithm of neural Networks, was used to prove the robot system stability with a neural Network Controller.
