结合神经网络学习子模型的非线性PID多控制器

A. Zayed, Amir Hussain
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引用次数: 4

摘要

本文提出了一种新的基于非线性自适应PID的多控制器,并结合了神经网络学习子模型。未知非线性对象由线性时变子模型和基于非线性神经网络的学习子模型组成的等效模型表示。所提出的多控制器方法为设计人员提供了一个选择,要么使用传统的PID自整定控制器,要么使用基于PID的极点放置控制器,要么通过轻击开关使用新提出的基于PID的极点零放置控制器。使用非线性植物模型的仿真结果证明了所提出的多控制器在跟踪随期望响应速度的设定点变化,惩罚过度控制行为以及将其应用于非最小相位和不稳定系统方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel non-linear PID based multiple-controller incorporating a neural network learning sub-model
The paper proposes a new non-linear adaptive PID based multiple-controller incorporating a neural network learning sub-model. The unknown non-linear plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a non-linear neural-networks based learning sub-model. The proposed multiple-controller methodology provides the designer with a choice of using either a conventional PID self-tuning controller, a PID based pole-placement controller, or a newly proposed PID based pole-zero placement controller through the flick of a switch. Simulation results using a non-linear plant model demonstrate the effectiveness of the proposed multiple-controller, with respect to tracking set-point changes with the desired speed of response, penalising excessive control action, and its application to non-minimum phase and unstable systems.
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