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首页> 外文会议>2013 IEEE International Symposium on Sensorless Control for Electrical Drives and Predictive Control of Electrical Drives and Power Electronics >Is multiple-objective model-predictive control “optimal”?
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Is multiple-objective model-predictive control “optimal”?

机译：多目标模型预测控制是否“最优”？

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摘要

We consider multiple-objective model-predictive control (MPC) of a linear time-invariant (LTI) single-input single-output (SISO) system (for simplicity without input constraints and/or disturbances). The performance index is the sum of weighted convex functionals J = Σi=1nwiJi (with wi ≥ 0). Although, by theory, the overall model-predictive control problem has a unique, globally optimal solution, this does not imply optimality of each sub-performance index Ji. To achieve “desirable” control performance, one has to find “decent” weighting factors wi; often done by “trial-and-error” which should be avoided, since weighting factor design might be not intuitive (even for LTI SISO systems without constraints; as we will show). The inherent difficulty lies in the mismatch between the “human performance index” (the optimality measure in the mind of the control engineer) and the implemented performance index J. In this paper, we illustrate these difficulties for a simple, linear third-order system and present some (old and new) approaches to ease weighting factor design. We do not give full answers but discuss first ideas which are admissible within the theoretical framework of standard MPC of LTI SISO systems.
机译：我们考虑线性时不变（LTI）单输入单输出（SISO）系统的多目标模型预测控制（MPC）（为简单起见，没有输入约束和/或干扰）。性能指标是加权凸函数J =Σi= 1 n wiJi（wi≥0）的总和。尽管从理论上讲，整个模型预测控制问题具有唯一的全局最优解，但这并不意味着每个子性能指标Ji都是最优的。为了获得“理想的”控制性能，必须找到“适当的”加权因子wi。由于权重因子的设计可能不直观（即使对于没有约束的LTI SISO系统，正如我们将要展示的那样），通常应该通过“反复试验”来避免。固有的困难在于“人员绩效指标”（控制工程师心目中的最佳度量）与已实现的绩效指标J之间的不匹配。在本文中，我们说明了简单线性三阶系统的这些困难并提出一些（旧的和新的）方法来简化权重因子设计。我们没有给出完整的答案，而是讨论了LTI SISO系统的标准MPC理论框架内可以接受的第一个想法。

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