Abstract
Evolution Strategies (ES) are stochastic optimization techniques obeying an evolutionist paradigm, that can be used to find global optima over a response hypersurface. The current investigation focuses on robust controller synthesis, using the unsupervised learning capabilities of ES’s issued from their evolutionist paradigm. The training process intents to construct a Lyapunov Function which guarantees internal stability, performance and disturbance rejection.
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© 2001 Springer-Verlag London Limited
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Raimúndez, C. (2001). Robust stabilization for the nonlinear benchmark problem (TORA) using neural nets and evolution strategies. In: Isidori, A., Lamnabhi-Lagarrigue, F., Respondek, W. (eds) Nonlinear control in the year 2000 volume 2. Lecture Notes in Control and Information Sciences, vol 259. Springer, London. https://doi.org/10.1007/BFb0110310
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DOI: https://doi.org/10.1007/BFb0110310
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