Hard Constraints for Prioritized Objective Nonlinear MPC
Hard Constraints for Prioritized Objective Nonlinear MPC
复制标题
DOI：
10.1007/978-3-540-72699-9_17
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
C. E. Long;E. Gatzke
中科院分区：
文献类型：
--
作者：
C. E. Long;E. Gatzke
本文提出了一种非线性模型预测控制(NMPC)算法，该算法使用硬变量约束来考虑控制目标的优先级。传统的按优先级排序的目标方法可能需要复杂的混合整数规划的解。这项工作的表述依赖于一个相对较小的纯连续非线性规划(NLP)逻辑序列的可行性和解。提出的离散控制目标协调问题的求解方法等价于整体混合整数非线性规划问题的求解。在模拟的多变量气压储罐网络上验证了该算法的性能。
This paper presents a Nonlinear Model Predictive Control (NMPC) algorithm that uses hard variable constraints to allow for control objective prioritization. Traditional prioritized objective approaches can require the solution of a complex mixed-integer program. The formulation presented in this work relies on the feasibility and solution of a relatively small logical sequence of purely continuous nonlinear programs (NLP). The proposed solution method for accomodation of discrete control objectives is equivalent to solution of the overall mixed-integer nonlinear programming problem. The performance of the algorithm is demonstrated on a simulated multivariable network of air pressure tanks.