TUNING OF MODEL PREDICTIVE CONTROL WITH MULTI-OBJECTIVE OPTIMIZATION
TUNING OF MODEL PREDICTIVE CONTROL WITH MULTI-OBJECTIVE OPTIMIZATION
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DOI：
10.1590/0104-6632.20160332s20140212
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发表时间：
2016-06
影响因子：
1.2
通讯作者：
A. S. Yamashita;A. Zanin;D. Odloak
中科院分区：
文献类型：
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作者：
A. S. Yamashita;A. Zanin;D. Odloak
提出了两种基于多目标优化的模型预测控制参数整定方法。这两种方法都考虑闭环响应和输出参考轨迹之间的误差最小化作为调谐目标。第一种方法是基于输出的排序，根据其重要性的工厂操作，它是解决了一个字典式优化算法。第二种方法解决了一个折衷优化问题。前者适用于输入数等于输出数的系统，而后者也适用于非方系统。主要贡献是一个自动调优框架的基础上，一个简单的目标定义。所提出的方法进行了测试的有限时域模型预测控制器在闭环与壳牌重油分馏基准系统的3x3子系统。仿真结果表明，本文提出的方法可以有效地缩短控制器的调试时间。该方法相比，现有的多目标优化的调整方法。运行所提出的调整算法所需的计算时间大大减少相比，现有的方法，而且，它不需要一个后验决策选择一个解决方案，从一组帕累托最优解。
Two multi-objective optimization based tuning methods for model predictive control are proposed. Both methods consider the minimization of the error between the closed-loop response and an output reference trajectory as tuning goals. The first approach is based on the ranking of the outputs according to their importance to the plant operation and it is solved by a lexicographic optimization algorithm. The second method solves a compromise optimization problem. The former is designed for systems in which the number of inputs is equal to the number of outputs, while the latter can also be applied to non-square systems. The main contribution is an automated tuning framework based on a straightforward goal definition. The proposed methods are tested on a finite horizon model predictive controller in closed-loop with a 3x3 subsystem of the Shell Heavy Oil Fractionator benchmark system. The simulation results show that the methods proposed here can be a useful tool to reduce the commissioning time of the controller. The methods are compared to an existing multi-objective optimization based tuning approach. The computational time required to run the proposed tuning algorithms is considerably reduced when compared to the existing approach and, moreover, it does not need an a posteriori decision to select a solution from a set of Pareto optimal solutions.