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An Iterative Data-Driven Linear Quadratic Method to Solve Nonlinear Discrete-Time Tracking Problems
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DOI：10.1109/TAC.2021.3056398.png)
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摘要
En 中文
The objective of this article is to introduce a novel data-driven iterative linear quadratic (LQ) control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from LQ stochastic optimal tracking problems. This algorithm is then coupled with iterative LQ-methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications.
Keyword:
Optimal control
Heuristic algorithms
Dynamic programming
Approximation algorithms
Q-factor
Stochastic processes
Mathematical model
Data-driven control design
dynamic programming
linear quadratic (LQ) control
optimal control
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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