Discrete-time sliding mode controller subject to real-time fractional delays and packet losses for networked control system

Shah, Dipesh; Mehta, Axaykumar

doi:10.1007/s12555-016-0761-8

Cited by 38 publications

(14 citation statements)

References 22 publications

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“…▪ Remark 5. Conditions (48)(49)(50)(51)(52)(53) of Theorem 3 are sufficient criteria for designing containment controller in the form of LMIs. Notice that when we solve these LMIs of Theorem 3 by MATLAB, the computational complexity will not increase with the raise of the number of followers.…”

Section: Proof It Is Effortless To Derive Thatmentioning

confidence: 99%

“…Actually, in networked environment, which is quite complex, 44‐49 each agent is controlled via remote controller through wireless communication. On account of anthropogenic attacks, 50 noisy interfere, packet disorders and immoderate network links, packet losses will ineluctably occur in communication networks 51‐53 . Thus, it is full of significant to take packet losses into account when considering the containment of MASs.…”

Section: Introductionmentioning

confidence: 99%

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Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Zhang

Dong

2020

Intl J Robust & Nonlinear

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This article investigates the sampled-data containment control issue for nonlinear multiagent systems (MASs) with packet losses in the framework of Takagi-Sugeno fuzzy model. In the process of designing fuzzy sampled-data containment controller, a crucial factor, that is, asynchronous premise variables, is tackled via the proposed novel asynchronous premise rebuilding approach. Based on the fact that there may be packet losses in communication networks, the fuzzy sampled-data MAS is concerned as a switched version with two subsystems. Then, in the light of average dwell-time method and Lyapunov stability theory, sufficient conditions of designing fuzzy sampled-data containment controller are derived such that the states containment can be achieved for considered systems in spite of packet losses. Finally, both a numerical simulation and a Caltech multivehicle wireless test bed simulation are presented to confirm the effectiveness of the proposed control scheme.

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Section: Proof It Is Effortless To Derive Thatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Zhang

Dong

2020

Intl J Robust & Nonlinear

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“…Moreover, many research methods for networked control systems. Many methods are available to examine networked control systems, and to compensate for the impact of delay and packet loss on the system [12]- [15]. The considered system is intended to control complex nonlinear systems with unknown mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Tension Networked Control Strategy for Carbon Fiber Multilayer Diagonal Loom

Liu

et al. 2020

IEEE Access

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With the aim of solving warp tension control problems in the let-off system and opening system in the carbon fiber multilayer diagonal loom, dynamic models of warp tension are established in this study based on dynamic analysis. Adaptive fuzzy PID control is applied to the tension-networked control, and an adaptive fuzzy PID controller is designed to solve the problems of network induced delay, packet loss, and network bandwidth occupancy. A TrueTime simulation structure of the tension control system is also built. Compared with PID control, adaptive fuzzy PID control exhibited the advantages of a small overshoot, small fluctuation of output tension, good tracking effect, and stability in simulations under different conditions, including a 30-ms network induced delay, 10% packet loss, and 30% network bandwidth occupancy. The results of the simulations show that adaptive fuzzy PID control has better control effect than PID control, and is more suitable for tension-networked control systems in the carbon fiber multilayer diagonal loom. INDEX TERMS Carbon fiber multilayer diagonal loom, tension control, networked control systems, adaptive fuzzy PID control.

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“…However, nowadays even if the plant itself is described in continuous time, it is usually controlled by a digital device (eg, programmable logic controller or a microcontroller). Therefore, to fully take into account the impact of discretization of the control signal, the discrete time sliding mode control has been proposed 14‐22 . Unfortunately, discrete time model reference control has not been studied in detail.…”

Section: Introductionmentioning

confidence: 99%

Reference model based discrete time sliding mode control of communication networks

Leśniewski

BartoszewiczAndrzej

2020

Adaptive Control & Signal

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In this work, we propose a new sliding mode controller based on a reference model for controlling data transmission rates in a connection-oriented communication network. In the proposed approach, we build a model of the network, which is controlled by a linear quadratic (LQ) optimal controller. Then, the sliding variable of the real network is forced to follow the reference sliding variable generated by this model. This method enables us to preserve the favorable properties of the optimal controller, such as reducing and smoothing out the initial flow rates. Moreover, once the reference model attains the vicinity of the desired state, the designed sliding mode controller is able to react more rapidly to changes in the available bandwidth, ensuring better robustness. Due to this fact, the considered controller can ensure full bottleneck link bandwidth utilization with smaller memory buffers than the LQ optimal one. This important advantage is shown both analytically, and in computer simulations K E Y W O R D S communication systems, model reference control, sliding mode control 1 INTRODUCTION Sliding mode control is a well-developed control approach that has been quite thoroughly studied over the years. The majority of publications deal with continuous time systems, 1-8 and in some of them, model reference sliding mode schemes are proposed for such systems. 9-13 However, nowadays even if the plant itself is described in continuous time, it is usually controlled by a digital device (eg, programmable logic controller or a microcontroller). Therefore, to fully take into account the impact of discretization of the control signal, the discrete time sliding mode control has been proposed. 14-22 Unfortunately, discrete time model reference control has not been studied in detail. Therefore, in this work a model reference based discrete time sliding mode controller is proposed. The controller will be used to determine transmission rates in a connection-oriented communication network, 23-27 such as networks operating under MultiProtocol Label Switching standard. It is assumed, that during data transmission from multiple sources to multiple destinations a single bottleneck occurs, due to an insufficient transmission capability of a single network link. In such a case, the data is stored at the node immediately before this link, which will be called the bottleneck node. This node must then send information to the data sources, to limit their transmission rates and prevent data loss due to buffer overflows. The proposed methodology begins with building a mathematical model of the network. The next step is designing an optimal controller 28,29 which will control this model. The sliding variable evolution obtained from the model will then be

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Discrete-time sliding mode controller subject to real-time fractional delays and packet losses for networked control system

Cited by 38 publications

References 22 publications

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Tension Networked Control Strategy for Carbon Fiber Multilayer Diagonal Loom

Reference model based discrete time sliding mode control of communication networks

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