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System identification using a genetic algorithm and its application to internal adaptive model control

机译：基于遗传算法的系统辨识及其在内部自适应模型控制中的应用

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摘要

The requirement for the high-quality control of complex and/or structure-unknown plant is growing in the real-world industrial machine. Indirect Adaptive Control (IAC), which identifies model and updates the controllers automatically, is one promising way expected to meet this requirement. The conventional IAC, however, is required to know the structure of the controlled plant, that is, the order of its transfer function, in advance. This paper presents a new IAC scheme which makes use of Genetic Algorithm (GA) in its identification part. In the proposed framework, the information on the order of the plant is not required since the genetic algorithm searches both the structure of the plant dynamics and its parameters autonomously. A two-degree-of-freedom Internal Mode Control (IMC) is adopted as a basic control architecture since the indirect adaptation can be harmoniously embedded in it. The effectiveness of the proposed scheme is verified through numerical simulations and experiments applied to a velocity control of multimass systems.
机译：在现实世界的工业机器中，对复杂和/或结构未知的工厂进行高质量控制的要求日益增长。间接自适应控制（IAC）可以自动识别模型并自动更新控制器，它有望满足这一要求。但是，要求常规的IAC预先知道被控设备的结构，即其传递函数的顺序。本文提出了一种新的IAC方案，该方案在识别部分使用了遗传算法（GA）。在提出的框架中，由于遗传算法会自动搜索植物动力学的结构及其参数，因此不需要有关植物顺序的信息。采用两自由度内部模式控制（IMC）作为基本控制体系结构，因为可以将间接适应和谐地嵌入其中。通过数值模拟和应用于多质量系统速度控制的实验验证了所提方案的有效性。

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