Online Delay Estimation and Adaptive Compensation in Wireless Networked System: An Embedded Control Design

Rajkumar, Santosh Mohan; Chakraborty, Sayan; Dey, Rajeeb; Deb, Dipankar

doi:10.1007/s12555-018-0612-x

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Observed over time, using a series of realistic measurements involving disturbances such as statistical noise, the Kalman Filter essentially minimizes the estimated error covariance while the initially assumed conditions are satisfied. Over each time interval, estimating a joint probability distribution gives the approximations of unknown variables [34,35]. These estimates are a great deal more accurate than those derived with only a single measurement.…”

Section: Kalman Filter Estimation and Lqg Controlmentioning

confidence: 99%

Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

et al. 2021

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This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink.

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Section: Kalman Filter Estimation and Lqg Controlmentioning

confidence: 99%

Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

et al. 2021

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“…For the purpose of compensating delays that are large, a useful tool is the prediction approach, which has seen popular growth since it was first proposed in 1959 [3]. Nevertheless, a lot of research on the predictor is limited to a centralized controller of a single plant [4][5][6][7][8][9]. In [10][11][12][13][14][15][16][17][18], the network-dependent control of interconnected systems under communication time-delay considers predictor-free stabilization where the delays are disregarded when the decentralized controllers are designed; thus, the delay length cannot be "large".…”

Section: Introductionmentioning

confidence: 99%

Decentralized Predictor Stabilization for Interconnected Networked Control Systems with Large Uncertain Delays and Event-Triggered Input

Zhu,

Zhang,

Jiang

2024

Electronics

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In this article, we propose a control scheme with predictors in a decentralized manner for coupled networked control systems (NCSs) under uncertain, large time-delays and event-triggered inputs. The network-induced delays are handled via the prediction; thus, the delay value is allowed to be large, and the burden of the network is relieved by the event-triggered input. Two methods are employed to deal with the large delay issue: the state and output feedback. When the state of each subsystem is measurable, full-state feedback is used, whereas when the plant state cannot be measured, output feedback is employed with the help of an observer, which is more common in practice. Instead of treating the interactive plants like a global system, the exponential stability of the coupled systems, under decentralized predictors with asynchronous sampled-data feedback, is analyzed in a decentralized way. Finally, the proposed methods are verified via an example of three interconnected cart–pendulum systems, while such systems would not be stabilizable by the traditional approach when the network-induced delays are relatively large.

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“…Indeed, given the stable steady-state energy supply demanded to the base-load CPESs, manoeuvring capabilities were designed for seldom operations, mainly triggered by safety needs (i.e., safe shutdowns) [4] and with limited safety margins or capabilities to satisfy flexible operation during frequent and fast-changing demand scenarios. Since the base-load CPESs are normally expected to operate under stable steady-state conditions, for which any change of the cyber part setting can be easily detected and corrected without losing control of the system [5][6][7], aging of cyber parts is not a concern, whereas under frequent, fast-changing transients, as it is in the case of load-following CPESs here considered, aging of the cyber part cannot be neglected [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

2021

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Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply.

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Online Delay Estimation and Adaptive Compensation in Wireless Networked System: An Embedded Control Design

Cited by 11 publications

References 22 publications

Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

Decentralized Predictor Stabilization for Interconnected Networked Control Systems with Large Uncertain Delays and Event-Triggered Input

Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

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