Distributed MPC algorithm with row-stochastic weight matrix over non-ideal time-varying directed communication

Distributed model predictive control (DMPC) approaches have achieved remarkable results in complex multiple subsystems network applications, such as unmanned aerial vehicle and sensor control networks. However, most of the existing DMPC algorithms require that the communication network of subsystems is time-invariant or undirected with local constraints, by ignoring the cooperation of multiple subsystems with global constraint in the non-ideal communication network, which greatly limits the applicability of the algorithms. To this end, the authors develop a fully DMPC algorithm of linear system with global constraint over time-varying unbalanced directed communication. Considering the uncertainty of communication network, this algorithm can handle the non-ideal communication network (e.g. communication noise, communication delay). Specifically, the row-stochastic weight matrix is adopted to improve the independent controllability of subsystems. Under reasonable assumptions, it is proved that the algorithm can converge to the optimal solution while guaranteeing the recursive feasibility and exponential stability of closed-loop system. Finally, the simulation experiments are shown to substantiate the convergence and robustness of the proposed algorithm.