Abstract
In this paper, a new comprehensive intelligent control strategy based on connectionist learning is presented, effectively combining genetic algorithms(GA) with neural and wavelet classification. The proposed neural network classifies characteristics of environment, determines the control parameters for compliance control, and in coordination with basic learning compliance control algorithm, reduces the influence of robot dynamic model uncertainties. The effectiveness of the approach is shown by using a simple and efficient GA optimization procedures to tune and optimize the performance of a neural classifier and controller. Some compliant motion simulation experiments with robotic arm placed in contact with dynamic environment have been performed.
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References
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© 2002 Springer-Verlag Wien
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Katić, D., Vukobratović, M. (2002). Advanced Control of Robot Compliance Tasks Using Hybrid Intelligent Paradigms. In: Bianchi, G., Guinot, JC., Rzymkowski, C. (eds) Romansy 14. International Centre for Mechanical Sciences, vol 438. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2552-6_20
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DOI: https://doi.org/10.1007/978-3-7091-2552-6_20
Publisher Name: Springer, Vienna
Print ISBN: 978-3-7091-2554-0
Online ISBN: 978-3-7091-2552-6
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