Data-Driven Computational Optimizal Control for Uncertain NonLinear Systems
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 09, 2019
- Accession Number
- AD1090460
Entities
People
- Daniele Venturi
- Qi Gong
Organizations
- University of California, Santa Cruz