Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems

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.

Automated summary

This article addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system and proposes a decoupled data-based control (D2C) algorithm to solve it. Experimental results show that the performance of this algorithm is nearly optimal, and the training time is significantly reduced compared to other algorithms.