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Neuro-based adaptive internal model control for robot manipulators

By Q. Li, A. N. Poi, C. M. Lim and M. Ang

Abstract

The application of Internal Model Control for process control has received much attention during the past decade. In this paper, the application of IMC for robot control is investigated. Although the IMC approach is shown to be more robust as compared to conventional robot control approaches, such as the computed-torque approach, its performance degrades in the presence of large modelling uncertainties and external disturbances. In this work, a neuro-based adaptive internal model control scheme is proposed. Within the framework of this control structure, a back-propagation neural network algorithm is incorporated into a fixed structure internal model controller for robot control. Simulation results, based on a two-link robot confirm the effectiveness of the proposed control algorithm even in the presence of large modelling uncertainties and external disturbances. 1

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.1276
Provided by: CiteSeerX
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