Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach

Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inab...

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Vydáno v:Mathematical biosciences and engineering : MBE Ročník 20; číslo 5; s. 8561 - 8582
Hlavní autoři: Djordjevic, Vladimir, Tao, Hongfeng, Song, Xiaona, He, Shuping, Gao, Weinan, Stojanovic, Vladimir
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States AIMS Press 01.01.2023
Témata:
adaptive dynamic programming
data-driven control
event-triggered control
hydraulic servo actuator
event-triggered control
data-driven control
adaptive dynamic programming
hydraulic servo actuator
ISSN:1551-0018, 1551-0018
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Abstract Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.
AbstractList Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.
Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.
Author Tao, Hongfeng
Stojanovic, Vladimir
He, Shuping
Djordjevic, Vladimir
Gao, Weinan
Song, Xiaona
Author_xml – sequence: 1
  givenname: Vladimir
  surname: Djordjevic
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  organization: Faculty of Mechanical and Civil Engineering, University of Kragujevac, 36000 Kraljevo, Serbia
– sequence: 2
  givenname: Hongfeng
  surname: Tao
  fullname: Tao, Hongfeng
  organization: Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
– sequence: 3
  givenname: Xiaona
  surname: Song
  fullname: Song, Xiaona
  organization: School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China
– sequence: 4
  givenname: Shuping
  surname: He
  fullname: He, Shuping
  organization: Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
– sequence: 5
  givenname: Weinan
  surname: Gao
  fullname: Gao, Weinan
  organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
– sequence: 6
  givenname: Vladimir
  surname: Stojanovic
  fullname: Stojanovic, Vladimir
  organization: Faculty of Mechanical and Civil Engineering, University of Kragujevac, 36000 Kraljevo, Serbia
BackLink https://www.ncbi.nlm.nih.gov/pubmed/37161212$$D View this record in MEDLINE/PubMed
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data-driven control
adaptive dynamic programming
hydraulic servo actuator
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Snippet Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an...
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SubjectTerms adaptive dynamic programming
data-driven control
event-triggered control
hydraulic servo actuator
Title Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach
URI https://www.ncbi.nlm.nih.gov/pubmed/37161212
https://www.proquest.com/docview/2811939095
https://doaj.org/article/04b1228dd6774bc9bc4fe788cb07c0a7
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