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Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems
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DOI：10.1109/TAC.2021.3108552.png)
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摘要
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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.
Keyword:
Heuristic algorithms
Trajectory
Approximation algorithms
Stochastic processes
Dynamic programming
Data models
Computational modeling
Reinforcement learning
stochastic control
nonlinear systems
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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