Analysis of direct action fuzzy PID controller structures
Analysis of direct action fuzzy PID controller structures
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DOI：
10.1109/3477.764871
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发表时间：
1999-06
期刊：
影响因子：
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通讯作者：
G. Mann;Bao-Gang Hu;R. Gosine
中科院分区：
文献类型：
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作者：
G. Mann;Bao-Gang Hu;R. Gosine
模糊 PID 控制器的大部分研究工作集中在 Mamdani (1974) 提出的传统双输入 PI 或 PD 型控制器上。然而，由于定义模糊规则库时涉及大量参数，模糊 PID 控制器设计仍然是一项复杂的任务。本文研究了不同的模糊 PID 控制器结构，包括 Mamdani 型控制器。通过以不同的形式表达模糊规则，可以清楚地识别每个PLD结构。为了分析的目的，定义了类线性模糊控制器。开发了一个简单的分析程序来推导出三输入模糊推理的封闭形式解。该解决方案用于识别解离形式中每种结构类型的模糊 PID 作用。单输入单输出非线性模糊推理的解说明了非线性调整的效果。然后，模糊 PID 控制器的设计被视为两级整定问题。第一级调整非线性 PID 增益，第二级调整线性增益，包括模糊变量的比例因子。通过为每种类型分配最少数量的规则，可以推导并明确呈现线性和非线性增益。不同模糊 PID 结构的整定特性根据其功能行为进行评估。本文提出的规则解耦和单输入规则结构比传统的模糊 PHD 结构提供了更大的灵活性和更好的功能特性。
The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.