Abstract
A six-degree of freedom (dof) robotic manipulator from Stanford family is controlled with an intelligent control system designed by using Elman network and generalized predictive control (GPC) algorithm. Three of Elman networks are trained by using GPC based data. They are used in parallel form to improve the reliability of the system by error minimization. At the end of parallel implementation, the results of networks are evaluated by using torque equations to select the network with best result. Simulation based test results showed that the proposed controller improves the performance of the system.
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Akbas, A. (2005). Intelligent Predictive Control of a 6-Dof Robotic Manipulator with Reliability Based Performance Improvement. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_36
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DOI: https://doi.org/10.1007/11508069_36
Publisher Name: Springer, Berlin, Heidelberg
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