Memory-event-triggering H_∞ reliable control for networked jacket platforms against earthquakes and stochastic actuator faults

Abstract

This paper deals with the problems of networked modelling and memory-event-triggering $H_{\mathrm{\infty }}$ reliable control for steel jacket-type platforms in network environments. First, a networked model of the jacket platform against earthquakes and probabilistic actuator faults is established. Based on this model, a memory-based event-triggering communication scheme is introduced to account for the constrained communication resources. A distinct feature of the proposed triggering scheme is that it has great potential to identify and trigger the significantly changed data than the existing ones without ‘memory’. Then, by formulating the network-based closed-loop platform system as a stochastic delay system, some sufficient conditions are derived for co-designing the desired controller and the triggering scheme. Finally, comparative simulation results are provided to demonstrate the effectiveness and merits of the proposed triggering and control co-design method. It is shown that compared with some existing event-triggering control methods, the proposed memory-event-triggering $H_{\mathrm{\infty }}$ reliable controller is effective to mitigate vibrations of the jacket platform and is potentially advantageous to save more network bandwidth and control cost.

Keywords:

• Networked control
• marine
• offshore platform
• vibration control
• event-triggered control
• earthquake

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the Key Project of Natural Science Foundation of Zhejiang Province of China [grant number LZ19F030001], the National Natural Science Foundation of China [grant number 61773356], the National Key Research and Development Program of China [grant number 2017YFC0804604], and the Fundamental Research Funds for the Provincial Universities of Zhejiang [grant number 2020YW42].

Notes on contributors

En-Zhi Cao

En-Zhi Cao is currently a Master candidate with the College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China. His current research interests include networked control systems and vibration control systems.

Bao-Lin Zhang

Bao-Lin Zhang received the Ph.D. degree in physical oceanography from Ocean University of China, Qingdao, China, in 2006. He is currently a professor with the College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China. His current research interests include network-based control systems, time-delay systems, and vibration control systems.

Zhihui Cai

Zhihui Cai received the Ph.D. degree in mathematics from Zhejiang University, Hangzhou, China, in 2009. He is currently an associate professor with the Department of Applied Mathematics, China Jiliang University, Hangzhou, China. His current research interests include neural networks and time-delay systems.

Binrui Wang

Binrui Wang received the Ph.D. degree in pattern recognition and intelligent system from Northeastern University, Shenyang, China, in 2005. Currently, he is a professor and a director with Engineering Training Center, China Jiliang University, Hangzhou, China. His research interests include intelligent control and humanoid robot.

Qing Li

Qing Li received the Bachelor of Engineering degree in electric automation from Harbin Institute of Technology, Harbin, China, in 1982. He is currently a professor with the College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China. His research interests include dynamic measurement and control, sensing technology, and geohazard monitoring.