1. Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems.
- Author
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Wang, Ran, Parunandi, Karthikeya S., Yu, Dan, Kalathil, Dileep, and Chakravorty, Suman
- Subjects
- *
NONLINEAR dynamical systems , *TRAJECTORY optimization , *DYNAMIC programming , *DYNAMICAL systems - Abstract
This article addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical. This problem is subject to the “curse of dimensionality” associated with the dynamic programming method. This article proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, “open-loop–closed-loop,” approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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