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Data-Driven Computational Optimizal Control for Uncertain NonLinear Systems

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Technical Report,09 Apr 2018,09 Oct 2019

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University of California Santa Cruz Santa Cruz United States

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This report describes the development of the foundations of new computational algorithms for optimal control of high-dimensional stochastic dynamical systems. The proposed optimal control architecture emphasizes the role of data-driven probability density function PDF equations instead of nonlinear dynamics in the control loop. This paradigm shift opens the possibility to integrate advanced numerical methods for high-dimensional PDF equations with optimization algorithms to mitigate the effects of uncertainty in high-dimensional nonlinear control systems. This effort developed scalable software and fast algorithms to compute the numerical solution to of high-dimensional PDF equations developed a systematic methodology to compute the numerical solution to data-driven PDF equations integrated the numerical algorithms to solve high-dimensional PDF equations into the proposed data-driven computational optimal control framework and demonstrated the effectiveness of the proposed data-driven control strategies in several applications.

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  • Operations Research

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