Robust Mixed H2/H∞ Control for An Uncertain Wireless Sensor Network Systems with Time Delay and Packet Loss

Shi, Ye; Wang, Jianhui; Fang, Xiaoke; Huang, Yueyang; Gu, Shen

doi:10.1007/s12555-018-0508-9

Cited by 11 publications

(6 citation statements)

References 36 publications

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“…MATLAB LMI toolbox is utilized to determine the control rule. The numerical example and simulation results confirmed the efficiency of the suggested strategy [9]. Finally, the fuzzy logic controller has been introduced to control the DC motor over Wi-Fi network using MATLAB and true-time.…”

mentioning

confidence: 56%

Time delay effect reduction on the wireless networked control system using an optimized FOPI-FOPD controller

Hasan

Oglah

Marie³

2023

IJPEDS

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Wireless Networked Control System (WNCS) is made up of an actuator, sensor, and controller that communicates through a wireless network rather than typical point-to-point cable connections. Lower maintenance costs, greater flexibility, and increased safety are the main WNCS advantages, so as a result, it has attracted a lot of researchers. Nevertheless, time delays and packet losses in wireless data transmission are classified as complicated problems, which impair WNCS output accuracy and may influence the overall system stability. Integer-Order PI-PD (PI-PD) and Fractional-Order PI-PD (FOPI-FOPD) controllers are proposed to reduce the impact of the control signal transmission's time delay and improve system performance. Matlab Simulink and True-time simulator are used to simulate the WNCS, and ZigBee protocol is used in transceiving the control signal between the controller and the system. Rotary Inverted Pendulum (RIP) acted as the controller's objective. The Grey Wolf Optimization (GWO) technique is utilized to evaluate the best controller parameters. Xbee S2 modules are used to implement the signal transmission process over ZigBee protocol. The FOPI-FOPD controller outperforms the PI-PD controller in the simulation and experimental results in decreasing the influence of time delay on system stability

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mentioning

confidence: 56%

Time delay effect reduction on the wireless networked control system using an optimized FOPI-FOPD controller

Hasan

Oglah

Marie³

2023

IJPEDS

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“…For remote modern cycle control, Melody et al [11] introduced a corresponding essential (PI) regulator structure in 2006 and incorporated a fundamental assessment of the regulator's dependability. A multi-bounce caused increment scheduler was proposed by Nikolakopoulous et al [12] in 2007 for the remote arranged control framework. The proposed control framework is based on the (client-server) system.…”

Section: Figure 1: Wncs Area By Venn's Scheme Representationmentioning

confidence: 99%

Performance Analysis of Wireless Monitoring Control System for ESP in Cement Plants

DeKheel,

O. Mustafa,

Marie

2024

IRJIET

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There is still a deficiency in the field of closedloop wireless control systems in terms of full real-time implementation and actualization. This paper explores the architecture, design, and implementation of Wireless Networked Control Systems (WNCS), emphasizing their operational problems. In particular, it investigates the implementation of an XBee platform-enabled Wireless Networked Control System to control an Electrostatic Precipitator (ESP) model using PC connection. The architecture of this control system is client-server. A realworld case study about the planning, creation, and implementation of a wireless monitoring control system for ESP is carefully scrutinized and assessed. The True time 1.5 simulator is used to illustrate simulation results, which provide insight into how different system factors affect overall performance and stability. The article also highlights how to use Matlab and Simulink capabilities to improve ZigBee network utilization for comprehensive feedback WNCS.

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“…However, the paper assumes that the dynamics of the plant are considered to be accurately known, the states are measurable, and the communication delay is bounded, which limits the applicability of this method. From a control perspective, robust controllers [103], [104] and MPC [105] are well-known to be robust against packet loss and delay in NCSs. However, the performance of these methods usually depends on the dynamic model accuracy and availability, which may strongly vary for real systems.…”

Section: A Communication Errorsmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

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Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

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Robust Mixed H2/H∞ Control for An Uncertain Wireless Sensor Network Systems with Time Delay and Packet Loss

Cited by 11 publications

References 36 publications

Time delay effect reduction on the wireless networked control system using an optimized FOPI-FOPD controller

Time delay effect reduction on the wireless networked control system using an optimized FOPI-FOPD controller

Performance Analysis of Wireless Monitoring Control System for ESP in Cement Plants

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

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