基于MLD模型的预测控制可行性与约束优先级研究

混合逻辑动态(MLD)框架为处理约束预测控制不可行和优先级问题提供了新的方法,但其优化算法求解计算量大,求解时间长.通过在目标函数中引入新的惩罚项改进优化算法,将混合整数二次规划(MIQP)问题转化为二次规划(QP)进行求解,减少了计算工作量,提高了求解效率.通过在Wood-Berry塔上的仿真分析,获得了良好的控制性能,分析结果表明了该方法的有效性.

Study of handling feasibility and constraint prioritization issues in predictive control based on MLD model

The mixed logical dynamical(MLD) framework introduces a new way to handle infeasibility and constraint
prioritization issues in predictive control. But the optimal algorithm is characterized with computation complexity and
long resovling time. In this paper, a penalty function method is introduced in the index function, and then the mixed
integer quadratic programming(MIQP) problem is converted into quadratic programming(QP) problem. Thus an improved optimal algorithm is developed. The resovling time is reduced greatly, and computation efficiency is achieved. The proposed algorithm is applied into the Wood-Berry column and promising results are achieved.