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PD-Based Optimal ADRC with Improved Linear Extended State Observer.
Zhang, Zhen; Cheng, Jian; Guo, Yinan.
Afiliação
  • Zhang Z; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
  • Cheng J; Research Institute of Mine Big Data, China Coal Research Institute, Beijing 100013, China.
  • Guo Y; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Entropy (Basel) ; 23(7)2021 Jul 13.
Article em En | MEDLINE | ID: mdl-34356430
Taking dead-zone nonlinearlity and external disturbances into account, an active disturbance rejection optimal controller based on a proportional-derivative (PD) control law is proposed by connecting the proportional-integral-derivative (PID) control, the active disturbance rejection control (ADRC) and particle swarm optimization (PSO), with the purpose of providing an efficient and practical technology, and improving the dynamic and steady-state control performances. Firstly, in order to eliminate the negative effects of the dead-zone, a class of 2-order typical single-input single-out system model is established after compensating the dead-zone. Following that, PD control law is introduced to replace the state error feedback control law in ADRC to simplify the control design. By analyzing the characteristics of the traditional linear extended state observer, an improved linear extended state observer is designed, with the purpose of improving the estimation performance of disturbances. Moreover, employing PSO with a designed objective function to optimize parameters of controller to improve control performance. Finally, ten comparative experiments are carried out to verify the effectiveness and superiority of the proposed controller.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article