Near Optimal Control Based on the Tensor-Product Technique

Xiangdong Liu; Xing Xin; Zhen Li; Zhen Chen

doi:10.1109/TCSII.2016.2592986

Near Optimal Control Based on the Tensor-Product Technique

Xiangdong Liu
, Xing Xin
, Zhen Li
, Zhen Chen

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

20 Citations (Scopus)

Abstract

This brief investigates the near optimal control problem and targets at the rigid body attitude dynamic system that are characterized by its typical nonlinearity. In order to apply the linear matrix inequality-based optimization, a second-order linear-parameter-varying model is specifically derived from the attitude system by means of the tensor-product transformation. Furthermore, an adaptive attitude tracking control law is designed to achieve both the offline optimization and online control simplification. The final control law is provided in a combined form. As a result, the dynamic response of the system mainly depends on the near optimal virtual controller and the stability can be further guaranteed by the adaptive tracking controller. Numerical simulations are conducted to verify the effectiveness of the proposed algorithms.

Original language	English
Article number	7516605
Pages (from-to)	560-564
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	64
Issue number	5
DOIs	https://doi.org/10.1109/TCSII.2016.2592986
Publication status	Published - May 2017

Keywords

Adaptive control
linearization
nonlinear control
optimal control
tensor product (TP)

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10.1109/TCSII.2016.2592986

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title = "Near Optimal Control Based on the Tensor-Product Technique",

abstract = "This brief investigates the near optimal control problem and targets at the rigid body attitude dynamic system that are characterized by its typical nonlinearity. In order to apply the linear matrix inequality-based optimization, a second-order linear-parameter-varying model is specifically derived from the attitude system by means of the tensor-product transformation. Furthermore, an adaptive attitude tracking control law is designed to achieve both the offline optimization and online control simplification. The final control law is provided in a combined form. As a result, the dynamic response of the system mainly depends on the near optimal virtual controller and the stability can be further guaranteed by the adaptive tracking controller. Numerical simulations are conducted to verify the effectiveness of the proposed algorithms.",

keywords = "Adaptive control, linearization, nonlinear control, optimal control, tensor product (TP)",

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AU - Liu, Xiangdong

AU - Xin, Xing

AU - Li, Zhen

AU - Chen, Zhen

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Y1 - 2017/5

N2 - This brief investigates the near optimal control problem and targets at the rigid body attitude dynamic system that are characterized by its typical nonlinearity. In order to apply the linear matrix inequality-based optimization, a second-order linear-parameter-varying model is specifically derived from the attitude system by means of the tensor-product transformation. Furthermore, an adaptive attitude tracking control law is designed to achieve both the offline optimization and online control simplification. The final control law is provided in a combined form. As a result, the dynamic response of the system mainly depends on the near optimal virtual controller and the stability can be further guaranteed by the adaptive tracking controller. Numerical simulations are conducted to verify the effectiveness of the proposed algorithms.

AB - This brief investigates the near optimal control problem and targets at the rigid body attitude dynamic system that are characterized by its typical nonlinearity. In order to apply the linear matrix inequality-based optimization, a second-order linear-parameter-varying model is specifically derived from the attitude system by means of the tensor-product transformation. Furthermore, an adaptive attitude tracking control law is designed to achieve both the offline optimization and online control simplification. The final control law is provided in a combined form. As a result, the dynamic response of the system mainly depends on the near optimal virtual controller and the stability can be further guaranteed by the adaptive tracking controller. Numerical simulations are conducted to verify the effectiveness of the proposed algorithms.

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KW - linearization

KW - nonlinear control

KW - optimal control

KW - tensor product (TP)

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ER -

Near Optimal Control Based on the Tensor-Product Technique

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