Abstract
Typical solutions based on nonlinear constrained optimization-based strategies are hard to find and usually demand for higher level of computation. In this paper two techniques for transforming the initial nonlinear optimization into an approximate convex optimization are presented and tested for a rigid manipulator modeled with a feedforward neural network. The results have shown that the overall performance is enhanced when performing an approximate feedback linearization.
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© 1996 Springer-Verlag Berlin Heidelberg
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Botto, M.A., te Braake, H.A.B., da Costa, J.S. (1996). Solving nonlinear MBPC through convex optimization: A comparative study using neural networks. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_101
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DOI: https://doi.org/10.1007/3-540-61510-5_101
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