Performance improvement in leader-following consensus of multi-agent systems via MPC-based reset output feedback...

This paper presents a novel approach to address the leader-following output consensus problem in linear multi-agent systems using a reset Model Predictive Control (MPC)-based dynamic output feedback controller. The proposed method integrates continuous and discrete dynamics within a hybrid control framework, ensuring Zeno-free behavior and superior transient performance. The controller design process involves two key steps: First, the D-stability approach is employed to configure the gain matrices of the dynamic output feedback controller without reset, positioning the base closed-loop poles within a predefined region aligned with control objectives. The D-stability conditions are formulated as linear matrix inequalities (LMIs), providing a systematic foundation for controller design. Second, at each reset instant, determined using the zero-crossing detection approach, an optimization problem based on a model predictive strategy is solved to obtain the optimal after-reset values of the controller states by minimizing a quadratic cost function. This comprehensive approach achieves leader-following output consensus with improved transient performance, and networked implementation capabilities. The effectiveness of the proposed method is demonstrated through theoretical analysis and simulation results, highlighting significant advantages over traditional methods.