Event-Triggered Consensus of Linear Multiagent Systems With Time-Varying Communication Delays

Deng, Chao; Er, Meng Joo; Yang, Guang‐Hong; Wang, Weining

doi:10.1109/tcyb.2019.2922740

Cited by 146 publications

(43 citation statements)

References 43 publications

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“…On the premise of assumption that the signal phase is fixed and queued vehicles are released with a saturated flow rate, experimental results show that the proposed prediction model has a small error which can be accepted. However, in fact, during the practical applications, there is an inevitable time delay because of the information acquisition and processing, i.e., coordination between systems and communication delay [21], [22]. In this case, the accuracy of the prediction model will decrease.…”

Section: Discussionmentioning

confidence: 99%

A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

et al. 2020

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The arrival time of bus at the stop is critical data in design of bus operational strategies. Especially for real-time control strategies (i.e., signal priority system), bus arrival time is usually predicted to assess the next bus operation condition in the future (i.e., bus bunching and reliability of transit service) and then can be used as a decision basis of current control actions. The signalized intersection and surrounding traffic are the key factors in bus travel time prediction, but most previous approaches focus on the impact of signal control on bus delay only. This paper proposes a prediction model for arrival time at bus stop under the influence of both upstream signalized intersection and surrounding traffic flow. Considering the affected range of signalized intersection and the dynamic variation of bus speed, bus running processes are evaluated separately (including processes from a given detection point to stop line, through intersection, and from intersection to bus stop). In the proposed models, bus speed is deduced according to the change of traffic density at different locations to reflect the micro-impact of surrounding traffic flow on bus operation. The observed bus travel time is collected from actual investigations at two bus stops incorporating signalized intersection in Jinan City and compared with the predicted travel time in the proposed model. The results show that the proposed model has a low mean relative error. In addition, through the analysis of the maximum relative error, it also can be seen that vehicle queuing with random arrival of vehicles at stop line makes a gap between the prediction and the actual situation, which will be the focus of further research. INDEX TERMS Public transit, bus arrival time, signalized intersection, bus speed, traffic density.

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Section: Discussionmentioning

confidence: 99%

A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

et al. 2020

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“…3. The propagation delay between the master and reference slave is not measured by a constant value and instead varies at each measurement; thus, it is necessary to appropriately deal with the time-varying characteristic of the propagation delay in the measurement [17], [18]. Hence, the procedure of the propagation delay measurement is repeated 1,000 times, and the averaged propagation delay is selected as the propagation delay measurement between the master and reference slave.…”

Section: A Compensation For Propagation Delay Between Master and Refmentioning

confidence: 99%

Accuracy Improvement of Master–Slave Synchronization in EtherCAT Networks

Park

Kim

et al. 2020

IEEE Access

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In the study, we develop a method to improve the accuracy of master-slave synchronization in EtherCAT networks. The method involves two key compensations that are not considered in the EtherCAT protocol. First, the propagation delay between the master and reference slave is measured, and the system time of the reference slave is then compensated for the measured propagation delay. Second, the bias component of the synchronization error between the master and reference slave is periodically estimated, and system time of each slave is then compensated for the estimated bias component. The entire method is implemented as part of the master application without modifying the EtherCAT protocol or requiring excessive computation load or large memory space. Thus, the developed method is advantageous because it can be immediately applied at low cost to existing EtherCAT networks. By performing extensive experiments based on a Linux-based EtherCAT master, we demonstrate that the developed method significantly improves the accuracy of the master-slave synchronization in EtherCAT networks.

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“…Then, Li [19], [20] proposed an adaptive design of neural control for nonlinear systems with input saturation and time-varying delays. Besides, considering time delays, Wang [21] addressed the regulation cooperative control of uncertain chaotic systems and Deng [22] developed a novel periodic switching controller to solve the event-triggered consensus problem of linear multiagent systems with time-varying delays. In terms of the filtering designing, Yang [23] addressed a non-fragile H  filter for continuous-time fuzzy systems and the command filter method was provided avoiding the repeated derivatives of the virtual control laws in papers [24], [25].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Collision Avoidance System for Autonomous Surface Vessel Using Fuzzy Logic

Meng

Zhang

et al. 2020

IEEE Access

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Numerous researches have been done to develop ASV (Autonomous Surface Vessel) collision avoidance systems. Most of the systems used static methods but did not apply a knowledge base where solutions can be reused and adapted to solve a new case. In this paper, an algorithm of autonomous collision avoidance is proposed considering steering dynamic for ASV. The process of this learning method is to recall the FCBR (Fuzzy Case Base Reasoning) containing basic expert knowledge in the form of stored cases. The solutions will be retrieved from the knowledge base to find a NH (New Heading) command for collision avoidance. Moreover, to execute the NH, a design of adaptive fuzzy ASV heading control system based on command filter is conducted considering the input saturation constraints and external disturbances. T-S fuzzy logic is employed to approximate nonlinear uncertainties existing in the heading control system adopting the MLP (Minimal Learning Parameter) technique. Finally, simulations prove that the method is effective to retrieve the past similar cases for the new collision avoidance situation and give its solution for ASV to track adjusted heading.

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Event-Triggered Consensus of Linear Multiagent Systems With Time-Varying Communication Delays

Cited by 146 publications

References 43 publications

A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

Accuracy Improvement of Master–Slave Synchronization in EtherCAT Networks

A Real-Time Collision Avoidance System for Autonomous Surface Vessel Using Fuzzy Logic

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