Adaptive LQR controller for Networked Control Systems subjected to random communication delays

Srinivasan, Seshadhri; Vallabhan, Mishiga; Ramaswamy, Srini; Kotta, Ülle

doi:10.1109/acc.2013.6579931

Cited by 14 publications

(2 citation statements)

References 16 publications

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“…In [3], it has been shown that most of these timing uncertainties can be modelled as delays. These delays have been modelled using empirical distribution [16,17], Markov Chain [15], Markov Chain Monte Carlo [13], non-liner models [20]. This investigation extends the delay models within CPS and then tries to simulate CPS performance with network imperfections.…”

Section: Introductionmentioning

confidence: 99%

On simulating processor schedules and network protocols within CPS using TrueTime

Balasubramaniyan

Subathra

Hemesh

et al. 2015

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

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Abstract-Studying cyber-physical system (CPS) for a given network protocol and processor schedules is a challenging task. This investigation illustrates the role of TrueTime a MATLAB package for simulating CPS encapsulating information on processor schedules, and network protocols. Properties of CPS such as temporal behaviors, performance and stability can be studied using the TrueTime tool. Furthermore, these simulations can be used to benchmark design and propose design alternatives. The procedure to use the toolbox for simulations and instantiate various blocks to study CPS is illustrated in the paper. Finally, an illustrative example has been presented of CPS simulation with wireless network. Our example demonstrates that TrueTime is a useful tool for studying CPS performance, timing behavior, schedulability analysis, and studying suitable network protocols.

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Section: Introductionmentioning

confidence: 99%

On simulating processor schedules and network protocols within CPS using TrueTime

Balasubramaniyan

Subathra

Hemesh

et al. 2015

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

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“…Recently, stochastic models have gained significant attention. Time-varying delays have been modeled to be stochastic using empirical distribution [8], Markov chain [9], Markov Chain Monte Carlo [10] and Hidden Markov Model (HMM) [11]. Empirical models capture delays using stochastic distribution (For e.g.…”

Section: Introductionmentioning

confidence: 99%

Estimating Random Delays in Modbus Over TCP/IP Network Using Experiments and General Linear Regression Neural Networks with Genetic Algorithm Smoothing

Sreram

Subathra

et al. 2015

Proceedings of the International Conference on Soft Computing Systems

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Time-varying delays adversely affect the performance of networked control systems (NCS) and in the worst-case can destabilize the entire system. Therefore, modelling network delays is important for designing NCS. However, modelling time-varying delays is challenging because of their dependence on multiple parameters such as length, contention, connected devices, protocol employed, and channel loading. Further, these multiple parameters are inherently random and delays vary in a non-linear fashion with respect to time. This makes estimating random delays challenging. This investigation presents a methodology to model delays in NCS using experiments and general regression neural network (GRNN) due to their ability to capture non-linear relationship. To compute the optimal smoothing parameter that computes the best estimates, genetic algorithm is used. The objective of the genetic algorithm is to compute the optimal smoothing parameter that minimizes the mean absolute percentage error (MAPE). Our results illustrate that the resulting GRNN is able to predict the delays with less than 3% error. The proposed delay model gives a framework to design compensation schemes for NCS subjected to time-varying delays.

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Adaptive Controller for Networked Control Systems Subjected to Random Communication Delays

Srinivasan

Saravanakumar

Subathra

et al. 2017

Soft Computing Applications

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Adaptive LQR controller for Networked Control Systems subjected to random communication delays

Cited by 14 publications

References 16 publications

On simulating processor schedules and network protocols within CPS using TrueTime

On simulating processor schedules and network protocols within CPS using TrueTime

Estimating Random Delays in Modbus Over TCP/IP Network Using Experiments and General Linear Regression Neural Networks with Genetic Algorithm Smoothing

Adaptive Controller for Networked Control Systems Subjected to Random Communication Delays

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