Convergence Rate Improvement for High-Order Nonlinear Multi-Agent Systems by Reset Mechanism

This paper designs an output-based reset controller for high-order nonlinear multi-agent systems to accelerate the states’ convergence rate. Based on the output consensus error, some specific reset conditions are developed, by which the reset integrator possesses the same gain direction with this consensus error. Cooperating with such a reset integrator, the controller can respond to the variation of consensus error more sensitively and faster convergence rate can be thus achieved. Additionally, the hybrid system frame is established by means of the well-designed closed-loop state, in which jump actions caused by reset integrators and timer variables also are modeled. Sufficient conditions are derived based on Lyapunov stability analysis theory for the leader-following consensus. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results.