Research on Distributed Cooperative Control Strategy of Microgrid Hybrid Energy Storage Based on Adaptive Event Triggering

Distributed collaborative control strategies for microgrids often use periodic time to trigger communication,which is likely to enhance the burden of communication and increase the frequency of controller updates,leading to greater waste of communication resources.In response to this problem,a distributed cooperative control strategy triggered by an adaptive event is proposed.By introducing an adaptive event triggering mechanism in the distributed controller,the triggering parameters are dynamically adjusted so that the distributed controller can communicate only at a certain time,the communication pressure is reduced,and the DC bus voltage deviation is effectively reduced,at the same time,the accuracy of power distribution is improved.The MATLAB/Simulink modeling and simulation results prove the correctness and effectiveness of the proposed control strategy.

Observer-based event-triggered networked multi-drives speed consensus

The problem of having an identical speed when dealing with multiple motors always exists in the industry.There are several methods to address the problem,but all the methodologies have two common drawbacks.Firstly,the control design requires continuous information on the desired speed and the actual speed of motors;secondly,it is sometimes difficult to directly measure the speed variables.In the proposed study,both of these drawbacks are addressed by designing an observer-based event-triggered networked multi-agent system.The proposed method uses the leader following consensus approach with a centralized event triggering control design so that whenever a follower's speed diverges from that of the leader,an event is triggered,which communicates and resets all the agents to the leader's speed.Moreover,an observer is designed such that the ith agent uses its jth neighbor agent and observer speed information to estimate the leader's speed.The stability of the proposed design is formulated by Lyapunov stability,while the simulation results endorse the design concepts and energy saving.

Event-triggered adaptive finite time trajectory tracking control for an under actuated vessel considering unknown time-varying disturbances

This paper aims to solve the finite-time trajectory tracking problem of underactuated surface ships under the influence of dynamic uncertainty,unknown external time-varying interference and limited communication resources.An event-triggered robust adaptive finite-time trajectory tracking control method for underactuated ships is designed by combining the existing trajectory tracking control methods and engineering needs in actual navigation.It can obviously improve the tracking accuracy of the ship,and complete the tracking task safely and efficiently.This scheme transforms the composite uncertain vector synthesized by uncertain parameters and external disturbances into a linear parameterized form.Next,considering the limitation of communication resources,a relative threshold event triggering mechanism is introduced to further extend the triggering time interval on the basis of the fixed threshold triggering scheme.Then,an event-triggered robust adaptive finite-time trajectory tracking control scheme is designed for underactuated ships,and a rigorous stability proof is provided through Lyapunov stability theory.Simulation experiment is carried out in MATLAB and the results show that the finite-time trajectory tracking control scheme proposed in this paper can effectively solve the problems of dynamic uncertainty.unknown time-varying external interference and limitation of communication resources.This paper can provide theoretical support for the autonomous navigation of intelligent ships.

Event Type Recognition Based on Trigger Expansion

Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types. Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning.

Interacting multiple model-based ETUKF for efficient state estimation of connected vehicles with V2V communication

Accurate prediction of the motion state of the connected vehicles,especially the preceding vehicle(PV),would effectively improve the decision-making and path planning of intelligent vehicles.The evolution of vehicle-tovehicle(V2V)communication technology makes it possible to exchange data between vehicles.However,since V2V communication has a transmission interval,which will result in the host vehicle not receiving information from the PV within the time interval.Furthermore,V2V communication is a time-triggered system that may occupy more communication bandwidth than required.On the other hand,traditional estimation methods of the PV state based on individual models are usually not applicable to a wide range of driving conditions.To address these issues,an event-triggered unscented Kalman filter(ETUKF)is first employed to estimate the PV state to strike a balance between estimation accuracy and communication cost.Then,an interactive multi-model(IMM)approach is combined with ETUKF to form IMMETUKF to further improve the estimation accuracy and applicability.Finally,simulation experiments under different driving conditions are implemented to verify the effectiveness of IMMETUKF.The test results indicated that the IMMETUKF has high estimation accuracy even when the communication rate is reduced to 14.84%and the proposed algorithm is highly adaptable to different driving conditions.