Earticle

In this study, the distributed secondary voltage control of islanded microgrids with multi-agent consensus algorithm is investigated. As an alternative to a time-triggered approach, an event-triggered scheme is proposed to reduce the communication load among inverter-based distributed generators (DGs). The proposed aperiodic control scheme reduced unnecessary utilization of limited network bandwidth without degrading control performance. By properly establishing a distributed triggering condition in DG local controller, each inverter is only required to send voltage information when its own event occurs. The compensation of voltage amplitude deviation can be realized, and redundant data exchange related to fixed high sampling rate can be avoided. Therefore, an efficient use of communication infrastructure can be realized, particularly when the system is operating in steady state. The effectiveness of the proposed scheme is verified by simulations on a microgrid test system.

In this paper, we are concerned with to design an event-triggered control for the global exponential stability of linear systems involving impulses at its' triggering times. Sufficient conditions are derived using the Lyapunov method and these conditions are presented in the form of linear matrix inequalities(LMIs). By solving these LMIs, event-triggered mechanism matrix and control gain can be efficiently obtained. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed approach.

In this paper, a problem of event-triggered model predictive control is investigated for continuous-time Takagi-Sugeno (T-S) fuzzy systems with input quantization. To efficiently utilize network resources, event-trigger is employed, which transmits limited signals satisfying the condition that the measurement of errors is over the ratio of a certain level. Considering sampling and quantization, continuous Takagi-Sugeno (T-S) fuzzy systems are regarded as a sector bounded continuous-time T-S fuzzy systems with input delay. Then, a model predictive controller (MPC) based on parallel distributed compensation (PDC) is designed to optimally stabilize the closed loop systems. The proposed MPC optimize the objective function over infinite horizon, which can be easily calculated and implemented solving linear matrix inequalities (LMIs) for every event-triggered time. The validity and effectiveness are shown that the event triggered MPC can stabilize well the systems with even smaller average sampling rate and limited actuator signal guaranteeing optimal performances through the numerical example.

This paper presents an event-triggered model predictive controller for adaptive cruise control system with sampled and quantized-data. Unlike existing works, a longitudinal continuous-time model is used for the predictive control of the system. To efficiently utilize network resources, event-trigger scheme is employed, which allows limited sensor and actuator signal satisfying the condition that the measurement of errors is over the ratio of a trigger level. The proposed control gain is obtained by solving a convex problem satisfying several linear matrix inequalities at every sampling times. Simulation results are given to show the effectiveness of the proposed design method.