Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity - Yu, Tianyu; Zhao, Fei; Xu, Zuhua; Zhao, Jun; Chen, Xi (ISA transactions, 2025)
To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs. In the online stage, all the agents build their sensitivity equations based on new information. Three variants of sensitivity equation are developed based on the level of communication load capacity, and the corresponding computation strategies are proposed. After solution, the background inputs are corrected and implemented. The optimality and robustness of the proposed algorithm are rigorously derived. Finally, the superiority of this DMPC scheme is demonstrated in the multi-vehicle system with two different topologies. •A distributed nonlinear model predictive control algorithm is proposed via parametric sensitivity.•The proposed method applies a two-stage strategy for input computation.•The local optimization problems are formulated based on the overall communication topology.•Three variants of sensitivity equation and their computation strategies are developed according to the communication load capacity.•The proposed algorithm not only achieves consensus control but also enjoys excellent real-time performance.