A Radial-Basis-Function-Network-Based Controller Derived from a Neural-Network-Based Controller and its Application to Controlling Mechanical Systems

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概要

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• In this paper we describe a method of designing a radial-basis-function-network-based(RBFN)controller. The RBFN controller is derived from a neural-network-based(NN)controller using differential operator focusing on the sigmoid function derivative of the NN controller. To overcome the Jacobian problem, the RBFN controller uses a learning algorithm and a neural identifier which uses an adaptive algorithm to estimate the plant Jacobian. A conventional feedback controller is incorporated into the RBFN controller to ensure both robustness and stability at the beginning of the learning process. Simulation results for mathematical plants demonstrate the applicability of the RBFN controller for controlling nonlinear systems and experimental results for 1-degree-of-freedom robots demonstrate its usefulness for controlling practical systems. Application to controlling an ODD positioner and a tunneling machine demonstrates the effectiveness of the RBFN controller for controlling mechanical systems.

• 一般社団法人日本機械学会の論文
• 1997-03-15

著者

• Takahashi Kazuhiko

  Integrated Information & Energy Systems Laboratories, Nippon Telegraph and Telephone Corporation

• Yamada Takayuki

  Access Network Systems Laboratories Nippon Telegraph And Telephone Corporation

関連論文

• Neural-Network-Based Controller Used for Directional Control of Tunneling Machine
• Neural-Network-Based Controller with Application to a Flexible Micro-Actuator : Direct Neural Controller and its Extension to an Open-Loop Neural Controller (Special Issue on Micromachine Technology)
• Application of an Immune Feedback Mechanism to Control Systems
• A Radial-Basis-Function-Network-Based Controller Derived from a Neural-Network-Based Controller and its Application to Controlling Mechanical Systems

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