Abstract
In this article, an approach to improving the performance of robot continuous-path operation is proposed. This approach utilizes a multilayer feedforward neural network to compensate for model uncertainty associated with the robotic operation. Closed-loop stability and performance are analyzed. It is shown that the closed-loop system is stable in the sense that all signals are bounded; it is further proved that the performance of the closed-loop system is improved in the sense that certain error measure of the closed-loop system decreases as the network learning process is iterated. These analytical results are confirmed by computer simulation. The effectiveness of the proposed approach is demonstrated through a laboratory experiment.
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© 1996 Kluwer Academic Publishers
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Chen, P.C.Y., Mills, J.K., Smith, K.C. (1996). Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks. In: Franklin, J.A., Mitchell, T.M., Thrun, S. (eds) Recent Advances in Robot Learning. The Kluwer International Series in Engineering and Computer Science, vol 368. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0471-5_4
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DOI: https://doi.org/10.1007/978-1-4613-0471-5_4
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