Compensation of time-varying delay acting in networked control systems using adaptive Smith predictor and parallel fuzzy-PI controller

Dragolj, Edin; Brankovic, Aida; Velagić, Jasmin; Osmić, Nedim

doi:10.1109/icmech.2011.5971258

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In Oyama and Nonaka, 27 a time-delay compensation method based on Smith predictor is proposed, which converts the time delay into state delay, and achieves stable path tracking for non-holonomic vehicles with time delay. In Dragolj et al, 28 in order to reduce the impact of delay in NCSs, improved control strategies of Smith predictive controller and parallel fuzzy PI (proportional integral) controller are proposed, respectively. In Wang et al, 29 to reduce the data transmission delay and dropout in the control system of autonomous ground vehicles, a robust H∞ state feedback controller is proposed.…”

Section: Introductionmentioning

confidence: 99%

Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

Xia

Chen

et al. 2020

Science Progress

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Accurate and real-time position of preview point is significant to trajectory tracking control of vision-guided intelligent vehicle. The unavoidable delay of road automatic identification system weakens trajectory tracking control performance, and even deteriorates the vehicle stability. Therefore, a compensator for the delay of road automatic identification system was proposed which combines the current statistical model and adaptive Kalman predictor to estimate the state of preview point position. The trajectory tracking sliding mode controller of intelligent vehicle is established through a 2–degrees of freedom vehicle dynamic model and motion model by using MATLAB/Simulink and CarSim. The trajectory tracking performance under 20–100 ms delay is analyzed. The simulation results show that the trajectory tracking performance of intelligent vehicle will be affected by the delay of road automatic identification system, reducing tracking accuracy. And when the delay is too large, it will deteriorate the vehicle stability and safety. In addition, the simulation results also verify the effectiveness of current statistical–adaptive Kalman predictor compensator at different delays.

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Section: Introductionmentioning

confidence: 99%

Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

Xia

Chen

et al. 2020

Science Progress

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“…A similar approach but in a decentralized system is proposed in [14]. A Smith predictor and a parallel fuzzy PI controller was designed in [15].…”

Section: Variable Time Delaysmentioning

confidence: 99%

Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic

Blaho¹,

́n²,

Viszus³

et al. 2015

Journal of Electrical Engineering

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The networked control systems (NCS) have grown in popularity in recent years. Despite their advantages over the traditional control schemes, some of their drawbacks emerged as well (time delays, packet losses). There are several ways of dealing with the time delays and packet losses in NCS, but only a few authors have ever used type-2 fuzzy controllers for this purpose to our knowledge. This paper is aimed at dealing with the negative effects that occur in NCS, by using type-2 fuzzy control systems. It is presented that this approach can be successfully used to decrease the effects of time delays and packet losses. A type-2 fuzzy controller has been designed and compared to a type-1 fuzzy controller. The intervals of type-2 fuzzy controller were optimized via genetic algorithm.K e y w o r d s: networked control systems, packet loss, time delay, true time, type-2 fuzzy logic

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Hybrid ABF-APSO Algorithm with Application to Tuning of Fuzzy PID Controller for Wireless HART Networked Control System

Hassan¹,

Ibrahim²,

Saad³

et al. 2018

AUTOSOFT

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Compensation of time-varying delay acting in networked control systems using adaptive Smith predictor and parallel fuzzy-PI controller

Cited by 3 publications

References 12 publications

Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic

Hybrid ABF-APSO Algorithm with Application to Tuning of Fuzzy PID Controller for Wireless HART Networked Control System

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