Stability of linear multiagent systems with guaranteed steady-state performance
Gradual advancement of control technology gives rise to the studies of the stability of linear systems. The stability of the linear multiagent system is motivated by increasing utilization of agent dynamics together with the number of control protocols associated with each agent. To that respect, in this report, the idea of event-triggered control and the stability of the linear multiagent system under steady-state performance conditions will be presented. By applying a steady-state condition, the state of an agent in the closed-loop linear system will be discussed using fixed network topology both in discrete-time and continuous-time. Moreover, the system is also analyzed by employing the Lyapunov function methods, and then an average consensus of the system will also be realized. Finally, we will verify the system's average consensus and stability via a simulation example.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Duality and Stability in Complex Multiagent State-Dependent Network Dynamics
Despite significant progress on stability analysis of conventional multiagent networked systems with weakly coupled state-network dynamics, most of the existing results have shortcomings in addressing multiagent systems …
A Modal-Space Method for Online Power System Steady-State Stability Monitoring
This paper proposes a novel approach to estimate the steady-state angle stability limit (SSASL) by using the nonlinear power system dynamic model in the modal space. Through two linear changes of coordinates and a simpli…
Nonlinear Stability of Complex Droop Control in Converter-Based Power Systems
In this letter, we study the nonlinear stability problem of converter-based power systems, where the converter dynamics are governed by a complex droop control. This complex droop control augments the well-known power-fr…
Multistage Economic MPC for Systems with a Cyclic Steady State: A Gas Network Case Study
Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of track…
Model Predictive ControlStructured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees
We study the control of networked systems with the goal of optimizing both transient and steady-state performances while providing stability guarantees. Linear proportional-integral (PI) controllers are almost always use…