The paper addresses the distributed optimal consensus control problem for constrained heterogeneous multi-agent systems within a model predictive control (MPC) framework. The approach optimizes both the control input sequence and the dynamically feasible consensus equilibrium simultaneously, resulting in a coupled optimization problem at each prediction step. A distributed primal-dual algorithm is developed, and locally verifiable conditions for its convergence are derived. Sufficient terminal conditions are established to guarantee recursive feasibility and asymptotic consensus of the closed-loop system.
Important for developing distributed control methods for large-scale systems that require coordination of heterogeneous agents under constraints and optimization of common objectives.
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