This paper contributes to an efficiently computational algorithm of collaborative learning model predictive control for nonlinear systems and explores the potential of subsystems to accomplish the task collaboratively. The collaboration problem in the control field is usually to track a given reference over a finite time interval by using a set of systems. These subsystems work together to find the optimal trajectory under given constraints in this study. We implement the collaboration idea into the learning model predictive control framework and reduce the computational burden by modifying the barycentric function. The properties, including recursive feasibility, stability, convergence, and optimality, are proved. The simulation is presented to show the system performance with the proposed collaborative learning model predictive control strategy.

本文为非线性系统的协同学习模型预测控制的高效计算算法做出了贡献，并探索了子系统协同完成任务的潜力。控制领域的协作问题通常是通过使用一组系统在有限的时间间隔内跟踪给定的参考。在本研究中，这些子系统协同工作以在给定约束条件下找到最佳轨迹。我们将协作思想实施到学习模型预测控制框架中，并通过修改重心函数来减少计算负担。证明了递归的可行性、稳定性、收敛性和最优性等性质。仿真展示了系统性能与所提出的协作学习模型预测控制策略。