Article: Event-triggered adaptive PID control for nonlinear dynamic process Journal: International Journal of Systems, Control and Communications (IJSCC) 2025 Vol.16 No.3 pp.242 - 260 Abstract: Proportional-integral-derivative (PID) control has been extensively employed for nonlinear dynamic process due to its simple control mechanism, high reliability, and easy implementation. However, it is difficult to determine the control parameters of the conventional PID controllers, which makes it difficult to adapt to the changes in nonlinear dynamic process. To address the challenges of low precision and excessive updates in nonlinear dynamic process control, an innovative event-triggered adaptive PID (EAPID) control method is proposed in this paper. Firstly, an adaptive PID controller based on the long short-term memory neural network is designed to enhance control precision. The network parameters are updated online using the back-propagation through time (BPTT) algorithm and the momentum term is introduced to update the controller parameters to improve the control accuracy. Secondly, an event-triggered mechanism is exploited to ensure the stability of the system, so that the controller is updated only when the triggering mechanism is violated, reducing computational resource consumption. Finally, the effectiveness of the proposed control method is validated by two numerical examples. The comparison results with other methodologies demonstrate the effectiveness and superiority of the proposed EAPID control method. Inderscience Publishers - linking academia, business and industry through research

Title: Event-triggered adaptive PID control for nonlinear dynamic process

Authors: Cong Xu; Wuyu Zhou

Addresses: School of Automobile and Electromechanical Engineering, Xinyang Vocational and Technical College, Xinyang, Henan, China ' School of Automobile and Electromechanical Engineering, Xinyang Vocational and Technical College, Xinyang, Henan, China

Abstract: Proportional-integral-derivative (PID) control has been extensively employed for nonlinear dynamic process due to its simple control mechanism, high reliability, and easy implementation. However, it is difficult to determine the control parameters of the conventional PID controllers, which makes it difficult to adapt to the changes in nonlinear dynamic process. To address the challenges of low precision and excessive updates in nonlinear dynamic process control, an innovative event-triggered adaptive PID (EAPID) control method is proposed in this paper. Firstly, an adaptive PID controller based on the long short-term memory neural network is designed to enhance control precision. The network parameters are updated online using the back-propagation through time (BPTT) algorithm and the momentum term is introduced to update the controller parameters to improve the control accuracy. Secondly, an event-triggered mechanism is exploited to ensure the stability of the system, so that the controller is updated only when the triggering mechanism is violated, reducing computational resource consumption. Finally, the effectiveness of the proposed control method is validated by two numerical examples. The comparison results with other methodologies demonstrate the effectiveness and superiority of the proposed EAPID control method.

Keywords: PID control; long short-term memory network; LSTM; event-triggered control; nonlinear dynamic process.

DOI: 10.1504/IJSCC.2025.147362

International Journal of Systems, Control and Communications, 2025 Vol.16 No.3, pp.242 - 260

Received: 20 Nov 2024
Accepted: 07 Mar 2025
Published online: 14 Jul 2025 *

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Title: Event-triggered adaptive PID control for nonlinear dynamic process

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