Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

Hu, Songlin; Yue, Dong; Chen, Peng; Xie, Xiangpeng; Yin, Xiuxia

doi:10.1016/j.asoc.2015.01.041

Cited by 115 publications

(27 citation statements)

References 57 publications

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“…In this section, an event-triggered PCS based control system is designed for the purpose of reducing transmissions without considering the network induced time delays. The study is based on the structure of Fig.1, which includes a plant, a sensor system, and a controller system, and only communication savings in sensor-controller channel are desirable [25][26][27]. As we know, the regular network control systems usually include a plant, a sensor system, a controller system, and an actuator system, and both the sensorcontroller and controller-actuator channel should be considered.…”

Section: Event-triggered Pcs Based Control Systemmentioning

confidence: 99%

Event‐triggered PCS Based Trajectory Tracking Control of Systems with Perturbation

Song

Wang

Tian

et al. 2017

Asian Journal of Control

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This paper is aimed at reducing network load for saving bandwidth by designing appropriate trigger signals that decide when the transmission should be done. An event‐triggered piecewise continuous systems (PCS) based control for time‐varying trajectory tracking is proposed. By designing the sensor system and controller system, the communication between them is reduced while still retaining a satisfactory closed‐loop behavior of the whole system. The major idea behind a designed sensor system is the use of a Luenberger observer and planning of event‐triggered mechanism (ETM). The main principle behind the designed controller system is the proposal of a new event‐triggered PCS based controller. The development is motivated by consideration of variable network induced time delays. Tracking error is proved to be norm‐bounded in both the original and developed case. Finally, to show the proposed method's performance, we present the simulation results for a mobile cart.

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Section: Event-triggered Pcs Based Control Systemmentioning

confidence: 99%

Event‐triggered PCS Based Trajectory Tracking Control of Systems with Perturbation

Song

Wang

Tian

et al. 2017

Asian Journal of Control

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“…For instance, in [33,34], the authors set the event-triggered threshold to be the sum of a proportional threshold times the quadratic form of the transmitted signals and an additional constant. In [35], the condition of the event-triggered threshold was set the same as in [33,34], but the constant was replaced by an exponential decay function. Recently, Li et al [36] proposed the ETM which consists of a state-dependent term, an exponentially decreasing term and a constant term, and studied a novel decentralised event-triggered dynamic-output-feedback L∞ control for NCSs, but didn't present a specific algorithm to find the ETM parameters to render the network load and control performance to reach an expected level.…”

mentioning

confidence: 99%

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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This paper is concerned with modeling and controlling under the mixed event‐triggered mechanism (ETM) for networked control systems (NCSs) with time‐varying delays and uncertainties. Firstly, an event‐triggered threshold is set by using both state and state‐independent informations in the mixed ETM. Then the event‐triggered NCSs with network‐induced time‐varying delays which exist in both sensor‐to‐controller and controller‐to‐actuator channels are modeled as a general time‐delay system. Based on the piecewise differentiable characteristic of the time‐varying delay and by using the approach of free weighting matrix and reciprocally convex, a less conservative criterion to be globally uniformly ultimately bounded (GUUB) stability and a controller design method are derived. Furthermore, an algorithm is proposed to obtain the desired mixed ETM and state‐feedback controller which can render the network load and control performance to reach an expected level. Compared with the relative and absolute ETMs, the proposed mechanism can effectively improve the transmission efficiency during the whole working time. Finally, a numerical example is given to show the effectiveness of the proposed approach.

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“…Sophisticated modelling methods based on hybrid frameworks [40,41], advanced neural computing models [42][43][44][45][46] and genetic programming frameworks [47][48][49] are required to quantify the multi-physics mechanisms for an improved battery design. There could be another factor such as the sudden mechanical impact (buckling, shearing and compression tests) which can also influence the battery capacity estimation.…”

Section: Interactions Among Multi-physics In Battery Packmentioning

confidence: 99%

“…The interactions between these mechanisms are hardly studied as it is complicated. Sophisticated modelling methods based on hybrid frameworks [40,41], advanced neural computing models [42][43][44][45][46] and genetic programming frameworks [47][48][49] are required to quantify the multi-physics mechanisms for an improved battery design. .00000000000000e-001, 0.00000000000000e+000, 3.00000000000000e-001, 5.00000000000000e-001, 1.00000000000000e+000, 1.00000000000000e+000, 4.00000000000000e-001, 1.00000000000000e+000, 3.00000000000000e-001, 4.00000000000000e-001, 2.00000000000000e-001, 0.00000000000000e+000, 3.00000000000000e-001, 6.00000000000000e-001, 2.00000000000000e-001, 5.00000000000000e-001, 4.00000000000000e-001, 8.00000000000000e-001, 5.00000000000000e-001, 8.00000000000000e-001, 3.00000000000000e-001, 6.00000000000000e-001, 5.00000000000000e-001, 1.00000000000000e+000, 3.00000000000000e-001, 1.00000000000000e+000, 4.00000000000000e-001, 6.00000000000000e-001, 2.00000000000000e-001, 6.00000000000000e-001, 4.00000000000000e-001, 8.00000000000000e-001, 2.00000000000000e-001, 1.00000000000000e+000, 4.00000000000000e-001, 6.00000000000000e-001, 4.00000000000000e-001, 1.00000000000000e+000, 2.00000000000000e-001, 8.00000000000000e-001, 0.00000000000000e+000, 1.00000000000000e+000, 4.00000000000000e-001, 4.00000000000000e-001, 5.00000000000000e-001, 6.00000000000000e-001, 3.00000000000000e-001, 8.00000000000000e-001, 3.00000000000000e-001, 1.00000000000000e-001, 5.00000000000000e-001, 9.99999999999999e-002, 1.00000000000000e-001, 3.00000000000000e-001, 1.00000000000000e-001, 1.00000000000000e-001, 9.99999999999999e-002, 9.99999999999999e-002, 1.00000000000000e-001, 9.99999999999999e-002, 1.00000000000000e-001, 9.99999999999999e-002, 1.00000000000000e-001, 9.99999999999999e-002, 1.00000000000000e-001, 9.99999999999999e-002,…”

Section: Interactions Among Multi-physics In Battery Packmentioning

confidence: 99%

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Garg

Vijayaraghavan

Zhang

et al. 2017

Int. J. Energy Res.

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Summary The improvement in the operating range of electric vehicles can be accomplished by robust modelling of the design and optimization of the energy storage capacity of the battery pack system. In this work, the authors have conducted a comprehensive survey on battery modelling methods and identified critical areas of improvement vital for estimating the battery capacity. This work proposes the artificial intelligence approach of automated neural networks search (ANS) in development of the robust battery capacity models for the lithium ion batteries based on the inputs (temperature and discharge rates). The robustness in the models is introduced by incorporating uncertainties in the inputs (the temperature and discharge rates, the architecture of algorithm and the models). The statistical analysis and validation of the models reveal that the models formulated using an ANS approach outperform the response surface regression models with correlation coefficient achieved as high as 0.97. The uncertainty analysis based on normal distribution of the inputs suggests that the models formulated from ANS are least sensitive to change in the input conditions when compared to response surface regression models. The global sensitivity analysis reveals that the temperature is a dominant factor for accurate battery capacity estimation. Copyright © 2017 John Wiley & Sons, Ltd.

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Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

Cited by 115 publications

References 57 publications

Event‐triggered PCS Based Trajectory Tracking Control of Systems with Perturbation

Event‐triggered PCS Based Trajectory Tracking Control of Systems with Perturbation

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

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