Many process control problems encapsulate multiple and often conflicting objective criteria spanning different levels of relative importance. In this paper, we consider a class of multi-objective receding horizon optimal control problems and propose a novel multi-objective model predictive control (MO-MPC) scheme for nonlinear systems subject to constraints and several prioritized (economic) criteria. Combining the lexicographic optimization and the receding horizon principle, a prioritized MO-MPC scheme without terminal constraints is presented to solve economically optimal control problems of the constrained nonlinear system. The results on recursive feasibility and stability of the MO-MPC are established in the context of economy optimization and no terminal constraints. Particularly, for the systems without state constraints, the computational burden of the MO-MPC is reduced due to the removal of terminal constraints. Using an intuitive optimizaiton, the feasible set of initial states is offline estimated to move out the initial feasibility condition. The proposed MO-MPC strategy is verified by the multiple control problems of a coupled-tank system and a six-order fluidized bed combustion process.

中文翻译：

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无终端约束的优先多目标模型预测控制及其在非线性过程中的应用

许多过程控制问题包含多个且经常相互冲突的客观标准，跨越不同的相对重要性级别。在本文中，我们考虑了一类多目标后退水平最优控制问题，并提出了一种新的多目标模型预测控制（MO-MPC）方案，用于受约束和几个优先（经济）标准约束的非线性系统。结合词典优化和后退时域原理，提出了一种无终端约束的优先MO-MPC方案来解决约束非线性系统的经济最优控制问题。MO-MPC 递归可行性和稳定性的结果是在经济优化和无终端约束的背景下建立的。特别是对于没有状态约束的系统，由于去除了终端约束，MO-MPC 的计算负担减少了。使用直观的优化，初始状态的可行集被离线估计以移出初始可行性条件。提出的 MO-MPC 策略通过耦合罐系统和六阶流化床燃烧过程的多重控制问题得到验证。