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IC697MSC802

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现代工程技术、生态或社会环境等领域的研究对象往往是十分复杂的系统,对这类系统难以用常规的数学方法来建立准确的数学模型,需要用学习、推理或统计意义上的模型来描述实际系统,这就导致了智能控制的研究。智能控制的主要目标是使控制系统具有学习和适应能力。现在,智能控制理论虽然取得了不少研究成果,但智能控制的理论体系还不够成熟。最近,基于模糊推理的系统建模[36]、神经网络模型参考自适应控制、神经网络内模控制[37]、神经网络非线性预测控制、混沌神经网络控制[38]等方面已有不少重要研究成果。智能控制理论有着广泛的应用,例如,基于神经动态规划的直升机的镇定控制[39]和航天轨道操作器的基于知识的分层控制[40]等。模糊推理、神经网络和遗传算法均具有模拟人类思维结构的方式的特点,将三者结合是智能控制研究的主要方向之一[26,41]。

2 传统与现代控制理论的局限性

传统控制器都是基于系统的数学模型建立的,因此,控制系统的性能好坏很大程度上取决于模型的性,这正是传统控制的本质。现代控制理论可以解决多输入、多输出(MIMO)控制系统地分析和控制设计问题,但其分析与综合方法也都是在取得控制对象数学模型基础上进行的,而数学模型的程度对控制系统性能的影响很大,往往由于某种原因,对象参数发生变化使数学模型不能准确地反映对象特性,从而无法达到期望的控制指标,为解决这个问题,自适应控制、鲁棒控制的研究便成为控制理论的研究热点。20世纪80年代由加拿大学者Zames等人创始的H∞控制理论是鲁棒控制理论的重要发展。但这些方法本质上还是没有摆脱基于数学模型的定量化思想。传统控制,包括经典反馈控制、现代控制理论等,在应用中遇到不少难题。机理建模所不可避免的模型误差将导致估计器工作效果时好时坏,难以设计可靠、稳定的控制系统。


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