Model Predictive Flocking Control for Second-Order Multi-Agent Systems With Input Constraints

This paper develops a model predictive flocking control scheme for second-order multi-agent systems with input constraints. By penalizing both the control effort and the irregularity of the position distribution to a desired lattice formation, a decentralized controller is designed based only on neighboring measurements. Geometric properties of the optimal path are used to provide conditions guaranteeing convergence to a rigid alpha-lattice flock avoiding inter-agent collision. Finally, numerical simulation is carried out to demonstrate the effectiveness of the proposed design.