基于BP神经网络的PID控制器参数的自整定仿真研究
时间: 2023-12-15 12:03:40 浏览: 31
本文基于BP神经网络的PID控制器参数的自整定仿真研究,旨在探索如何使用BP神经网络来自动调整PID控制器的参数,从而提高控制系统的性能。
首先,我们介绍了PID控制器的基本原理和参数调整的方法。然后,我们介绍了BP神经网络的基本原理和训练方法。接下来,我们将PID控制器和BP神经网络结合起来,提出了一种基于BP神经网络的PID控制器参数自整定方法。
在仿真实验中,我们使用MATLAB软件搭建了一个水平双摆控制系统,并采用了传统的Ziegler-Nichols方法和基于BP神经网络的自整定方法来调整PID控制器的参数。实验结果表明,基于BP神经网络的自整定方法可以在较短的时间内获得更好的控制效果,同时具有更高的鲁棒性和适应性。
总之,本文提出的基于BP神经网络的PID控制器参数自整定方法具有很大的应用价值和研究意义,可以为控制系统的优化和改进提供有力的支持。
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基于bp神经网络pid自整定仿真研究
摘要:本文介绍了一种基于BP神经网络的PID自整定方法,并通过仿真实验验证了其有效性。首先,利用MATLAB软件建立了PID控制系统的模型,并将其分别用传统PID控制和BP神经网络PID控制进行仿真。然后,通过比较两种控制方法的控制效果和稳定性,验证了BP神经网络PID控制的优越性。最后,对BP神经网络PID控制的各项参数进行了分析和优化,得到了更优的控制效果。该方法可以为工业控制系统的自动化升级提供新的思路和方法。
关键词:BP神经网络;PID控制;自整定;仿真研究
Abstract: This paper introduces a PID self-tuning method based on BP neural network, and verifies its effectiveness through simulation experiments. First, the PID control system model was established using MATLAB software, and simulation was carried out using traditional PID control and BP neural network PID control respectively. Then, by comparing the control effect and stability of the two control methods, the superiority of BP neural network PID control was verified. Finally, the parameters of BP neural network PID control were analyzed and optimized, and better control effect was obtained. This method can provide new ideas and methods for the automation upgrade of industrial control systems.
Keywords: BP neural network; PID control; self-tuning; simulation research.
基于bp神经网络的pid自整定仿真研究
摘要:针对传统PID控制器参数调整困难、调整时间长、调整效果不理想的问题,本文提出了一种基于BP神经网络的PID自整定控制算法。该算法将传统PID控制器中的比例、积分、微分三个参数视为输入层的神经元,将PID控制器输出的控制量作为输出层的神经元,通过对训练样本的学习,使得神经网络具有良好的自适应能力,可以根据不同的控制对象及控制要求自动调整PID参数,从而实现对控制系统的自整定。通过MATLAB仿真验证,该算法具有良好的控制性能和鲁棒性,可以应用于多种控制对象的控制系统中。
关键词:BP神经网络;PID控制器;自整定;MATLAB仿真
Abstract: In view of the difficulties in parameter adjustment, long adjustment time and unsatisfactory adjustment effect of traditional PID controller, this paper proposes a PID self-tuning control algorithm based on BP neural network. In this algorithm, the three parameters of proportion, integral and derivative in the traditional PID controller are regarded as the neurons of the input layer, and the control quantity output by the PID controller is regarded as the neurons of the output layer. By learning the training samples, the neural network has good adaptability and can automatically adjust the PID parameters according to different control objects and control requirements, so as to achieve self-tuning of the control system. Through MATLAB simulation verification, the algorithm has good control performance and robustness, and can be applied to control systems of various control objects.
Keywords: BP neural network; PID controller; self-tuning; MATLAB simulation.