Output‐feedback‐based sliding mode control for networked control systems subject to packet loss and quantization

Li, Jiarui; Niu, Yugang

doi:10.1002/asjc.2191

Cited by 26 publications

(24 citation statements)

References 27 publications

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“…The Proof of Theorem 1. Applying the optimal control theory to the transformed system (11) with performance index (15), the optimal near-optimal vibration controller is designed as…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…[13] investigated the optimal packet length and inter-node distance to be utilized in a SG over six different environments aiming at maximising the network lifetime to save power consumption; Ref. [14] proposed an event-triggered controller for a class of T-S systems based on the predictive packet; based on a compensating scheme for the packet dropout in the sensor-to-controller channel, a sliding mode controller was designed based on output information and state observer in [15]. Learning from the above studies, while discussing the controller design for networked vehicle active suspension, time delay, and packet dropout in the sensor-to-controller network channel and controller-to-actuator network channel must be taken into consideration synchronously.…”

Section: Introductionmentioning

confidence: 99%

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CAN-Based Vibration Control for Networked Vehicle Active Suspension with Both Network-Induced Delays and Packet-Dropouts

et al. 2022

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Ride comfort and driving safety are highly vulnerable to the undesirable excessive vibrations caused by road surface irregularities and the imperfect in-vehicle network (IVN). The main contribution of this paper consists of proposing a near-optimal vibration control approach for networked vehicle active suspension under irregular road excitations in a discrete-time domain, in which the uncertain time delay and packet dropout in CAN are taken into consideration. More specially, by virtue of two buffers of the sensor-to-controller network channel and the controller-to-actuator network channel in CAN, by introducing a designed state-transformation-based method, the original vibration control problem under the constraints of the irregular road excitations and imperfect CAN is transformed into a two-point boundary value (TPBV) problem without advanced and delayed items. After that, the near-optimal vibration control approach is presented to isolate the vehicle body from the road excitations and compensate the time delay and packet dropout from CAN synchronously. The stability condition of the networked vehicle active suspension under the proposed vibration controller is obtained based on the Lyapunov function. In numerous scenarios with different road roughnesses and network-induced time delays and packet dropouts, the simulation results illustrate the effectiveness and superiority of the proposed near-optimal vibration controller.

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“…The Proof of Theorem 1. Applying the optimal control theory to the transformed system (11) with performance index (15), the optimal near-optimal vibration controller is designed as…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CAN-Based Vibration Control for Networked Vehicle Active Suspension with Both Network-Induced Delays and Packet-Dropouts

et al. 2022

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“…For the current research study, there are mainly two approaches to deal with the problem of data packet loss. The first approach is to use a Bernoulli probability distribution sequence to describe the data packet loss as a random variable, which takes a value from

{0, 1}

[14–17]. The stability of the NCS whose communication channel was influenced by data packet loss was discussed [14], and a Bernoulli variable was used to model the data packet loss.…”

Section: Introductionmentioning

confidence: 99%

H∞ control for a networked Markovian jump system subject to data packet loss based on the observer

Wang

Sun

et al. 2021

IET Cyber-Syst and Robotics

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This study researches the H ∞ control issue for a networked Markovian jump system with data packet loss occurring not only in the channel from sensor to controller but also in the channel from controller to actuator via an observer. The mathematical model for the closed-loop networked Markovian jump system with data packet loss is established. The necessary and sufficient conditions for the closed-loop system to be stochastically stable are derived. The design approach of the controller and the minimal performance index of the external disturbance suppression are also given in the case that the transition possibilities of the system modes and the data packet loss are both partially unavailable. Finally, two numerical examples are used to illustrate the effectiveness of the proposed method. K E Y W O R D Sdata packet loss, Markovian jump system, networked control system, observer | INTRODUCTIONAs the networked control system (NCS) has the advantages of easy installation and maintenance, as well as convenient expansion, the application range of the NCS has become more and more extensive, including the fields of industrial systems, aerospace, robot manufacturing, remote sensing technology and so on [1][2][3][4]. Coupled with the increasing development of information technology, the NCS will be used in more fields in the future [5][6][7][8]. However, data packet loss and time-delay induced by the network would make the system's performance degrade and may even lead to instability, and these are two basic issues of the NCS [9][10][11][12][13].The research of data packet loss is a significant direction of NCS research, which has attracted considerable attention. For the current research study, there are mainly two approaches to deal with the problem of data packet loss. The first approach is to use a Bernoulli probability distribution sequence to describe the data packet loss as a random variable, which takes a value from f0; 1g [14][15][16][17]. The stability of the NCS whose communication channel was influenced by data packet loss was discussed [14], and a Bernoulli variable was used to model the data packet loss. Sliding mode control based on the observer for the NCS with quantisation was taken into consideration [15], in which data packet loss occurred when the measurement output was transmitted, and a compensating scheme was put forward to cope with influences of modelled data packet loss, which was treated as a Bernoulli process. A novel H ∞ approach for the NCS with multiple data packet losses was studied [16], and a control system containing two Bernoulli variables was applied to model the NCS, which contained not only measurement data packet loss but also control data packet loss. The problem of linear quadratic Gaussian control for the NCS with data packet loss was discussed [17], where dataThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Quantization is necessary for many applications that rely on data processing [23]. A codebook for the feature quantization of the features calculated from ECG signals was set up before training the HMM model.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

Pan

2020

Asian Journal of Control

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In this paper, a Fuzzy Hidden Markov Model (FHMM) for electrocardiogram (ECG)-based emotion recognition is proposed. The FHMM model is modeled using the fuzzy membership of each class of feature vectors to compute the elements of the matric in the model. Each element in the matric is determined by the two nearest classes of feature vectors with fuzzy classification to avoid the winner-takes-all situation that usually happens in tradition discrete Hidden Markov Models (HMM) and that reduces the accuracy of the modeling results. The FHMM modeling proposed in this paper can improve the precision of traditional discrete HMM modeling. Also, the features for emotion recognition, which are selected according to those used in prior research, are calculated from ECG signals. The selected features form a feature vector and these are calculated for all ECG signals. Some feature vectors are used to train the FHMM model and the remaining are used for testing. Experimental results show that the proposed FHMM model can achieve impressive improvements on the three indices, sensitivities (Se%), positive predictive value (PPV%), and total classification accuracy (TCA%), compared to traditional HMM. Moreover, compared to some existing studies, the recognition rates of the proposed method are higher. These results verify the efficiency of the proposed method for emotion recognition from ECG signals.

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Output‐feedback‐based sliding mode control for networked control systems subject to packet loss and quantization

Cited by 26 publications

References 27 publications

CAN-Based Vibration Control for Networked Vehicle Active Suspension with Both Network-Induced Delays and Packet-Dropouts

CAN-Based Vibration Control for Networked Vehicle Active Suspension with Both Network-Induced Delays and Packet-Dropouts

H∞ control for a networked Markovian jump system subject to data packet loss based on the observer

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

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