分布式预测函数控制优化算法

Distributed Optimization Algorithm for Predictive Functional Control

为解决复杂大规模预测控制系统在线计算复杂的问题,提出了一种基于纳什最优的分布式预测函数控制优化算法。给出了具体的计算过程,同时分析了这种算法的收敛条件。该算法结合预测函数控制输入结构化、计算方便和分布式控制简便等优点,不但解决了控制输入不明的缺点,并且减少了优化变量,能够很好地满足系统的全局稳定性。仿真结果验证了所提出的分布式预测函数控制算法的有效性。

To solve the problem of complex online calculation in large scale predictive control system, the optimization algorithm based on Nash optimality for distributed predictive functional control is proposed. The concrete calculation procedures are given, and the convergence conditions of the algorithm are analyzed. This algorithm combines the features and advantages of structured predictive function control input, easy calculation and easier distributed control; while the demerit of unclear control input is solved, and the number of optimized variables is reduced, this satisfies global stability of the system. The results of simulation verify the effectiveness of the algorithm proposed.