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首页> 外文会议>The Second International Conference on Information Jul 24-27, 2002 Beijing, China >Finding the Fuzzy Satisfying Solutions to Constrained Predictive Control Systems
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Finding the Fuzzy Satisfying Solutions to Constrained Predictive Control Systems

机译：寻找约束预测控制系统的模糊满意解

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摘要

Knowledge-based control tries to integrate the knowledge of human operators or process engineers into the controller design. Fuzzy control, one of the most popular techniques, has been successfully applied to a large number of consumer products and industrial processes. This prescriptive approach is closely related to predictive control. The formulation of the control problem as a confluence of fuzzy goals and fuzzy constraints leads to a generalization of the objective function used in model-based predictive control. This cost function is usually a sum of an error measure of one or more output variables. Using fuzzy goals and constraints it is possible to aggregate them using fuzzy operators, e.g. t-norms, s-norms or the convex sum. This paper investigates the use of fuzzy decision making in predictive control, the use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors.
机译：基于知识的控制试图将操作员或过程工程师的知识整合到控制器设计中。模糊控制是最流行的技术之一，已成功应用于大量消费品和工业过程。这种说明性方法与预测控制密切相关。将控制问题表述为模糊目标和模糊约束的融合导致对基于模型的预测控制中使用的目标函数的泛化。该成本函数通常是一个或多个输出变量的误差度量的总和。使用模糊目标和约束可以使用模糊运算符将它们汇总。 t范数，s范数或凸和。本文研究了预测控制中模糊决策的使用，预测控制中模糊目标和模糊约束的使用，使控制目标的聚合比通常的平方误差加权和更为灵活。

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