Adaptive sliding mode control of heliostat in solar tower power plant based immersion and invariance

In this paper, an adaptive sliding mode control method based on immersion and invariance (ASMCI&I) is proposed for the problem of unknown internal parameters and uncertain external perturbations of the heliostat system in a solar tower power plant in order to achieve fast and accurate tracking of the desired trajectory. First, a model of the fixed heliostat system containing unknown internal parameters and uncertain external perturbations is established, and a sliding-mode control strategy is designed, which employs an adaptive law to estimate the internal unknown parameters and solve the unknown parameters existing in the system. Immersion and invariance are employed to track the external uncertain perturbations and to address the effects of the external perturbations on the system. Finally, the fixed heliograph altitude angle and azimuth angle systems are analyzed and simulated. The results show that, compared with the traditional sliding mode control, the adaptive law designed in this paper can well solve the problem of unknown parameters existing inside the system and ensure that the final tracking effect of the system is accurate. And the principle of immersion and invariance can track the external perturbation well, improve the transient response of the heliostat system, stabilize the system faster, and reduce the influence on the actuator. In addition, the traditional sliding mode control loses stability in the extreme case of large perturbations, while the method proposed in this paper can still accurately track the perturbations in a very short time to ensure the stability of the heliostat system.

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Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1117/12.3037889
Primary Topic
Solar Thermal and Photovoltaic Systems
Type
article
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article

Adaptive sliding mode control of heliostat in solar tower power plant based immersion and invariance

Qiyue Xie, Xing Zhang
Solar Thermal and Photovoltaic Systems
article

Adaptive sliding mode control of heliostat in solar tower power plant based immersion and invariance

Qiyue Xie, Xing Zhang
article en

Abstract

In this paper, an adaptive sliding mode control method based on immersion and invariance (ASMCI&I) is proposed for the problem of unknown internal parameters and uncertain external perturbations of the heliostat system in a solar tower power plant in order to achieve fast and accurate tracking of the desired trajectory. First, a model of the fixed heliostat system containing unknown internal parameters and uncertain external perturbations is established, and a sliding-mode control strategy is designed, which employs an adaptive law to estimate the internal unknown parameters and solve the unknown parameters existing in the system. Immersion and invariance are employed to track the external uncertain perturbations and to address the effects of the external perturbations on the system. Finally, the fixed heliograph altitude angle and azimuth angle systems are analyzed and simulated. The results show that, compared with the traditional sliding mode control, the adaptive law designed in this paper can well solve the problem of unknown parameters existing inside the system and ensure that the final tracking effect of the system is accurate. And the principle of immersion and invariance can track the external perturbation well, improve the transient response of the heliostat system, stabilize the system faster, and reduce the influence on the actuator. In addition, the traditional sliding mode control loses stability in the extreme case of large perturbations, while the method proposed in this paper can still accurately track the perturbations in a very short time to ensure the stability of the heliostat system.

Changsha University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 90%
Solar Thermal and Photovoltaic Systems
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Adaptive sliding mode control of heliostat in solar tower power plant based immersion and invariance — Qiyue Xie, Xing Zhang · (2024) | TGRS Research Map | TGRS