Quadratic Programming Consensus Tracking Control of Uncertain Multiagent Systems via Event-Triggered Mechanism
本文提出一种基于二次规划优化框架的事件触发一致性跟踪控制方法，通过控制李雅普诺夫函数约束设计最优控制器，并利用高斯过程回归处理不确定性，以减少计算频率并提升能效。
This article addresses the consensus tracking control of multiagent systems (MASs) via a quadratic programming (QP) optimization framework, where the control Lyapunov function (CLF) condition serves as a constraint. The optimal controllers, derived through the QP solver, not only ensure the tracking control objective but also minimize the cost functions of agents. To enhance energy efficiency, discontinuous control methods, such as intermittent control strategy and event-triggered mechanism, are employed in the control framework. The CLF-based QP controllers are only updated at specific time instants, in order to reduce the frequency of QP problem-solving. In addition to considering optimization, the proposed methods are extended to uncertain MASs to enhance robustness, where the uncertainty is modeled by Gaussian process regression. In the end, simulation results are provided to demonstrate the feasibility of the theoretical analysis.