文章摘要
杜金祥,岳光,张华君.一种新型全液压矫直机多神经元PID伺服控制器设计与研究[J].机床与液压,2018,46(7):50-54
一种新型全液压矫直机多神经元PID伺服控制器设计与研究
Design and Research of a New Type of Multi Neuron PID Servo Controller of Full Hydraulic Straighter
  
DOI:10.3969/j.issn.1001-3881.2018.07.012
中文关键词: 多神经元  PID  全液压矫直机  伺服控制器
英文关键词: Multiple neuron  PID  Full hydraulic straighter  Servo controller
基金项目:山西省重点研发计划(指南)项目(201603D1210401);“973”国家重点基础研究发展规划前期项目(2011CB612204)
作者单位E-mail
杜金祥 太原工业学院自动化系 dujinxiang99@126.com 
岳光 太原工业学院自动化系  
张华君 太原工业学院自动化系  
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中文摘要:
      针对当前普遍常用的BP神经网络算法的缺陷,即反传算法复杂,收敛速度慢,在解决一些复杂多变非线性及系统多耦合问题时,网络权值收敛局部极小点,导致网络训练失败,反传算法权值迭代出现畸形变化问题。虽然神经网络控制器在软件仿真中运行结果都能满足要求,但是在实际的现场工程应用中很难适应复杂工业现场所要求的控制快速性和及时性。针对以上缺陷,提出了一种新型的多神经元PID神经网络算法,其原理是通过简化PID神经元网络控制器,改善其权值初始化来达到新型多神经元PID控制器控制过程的快速性和响应的及时性,将其应用到全液压矫直机四缸伺服控制器。通过将传统神经元网络控制器和文中所研究的新型多神经元PID控制器位移响应曲线对比分析,该新型多神经元PID伺服控制器具有反传算法大为简化、收敛速度快、网络权值灵活性好、实时性等优点,为多神经元PID控制器的工程化应用提供了理论支持。
英文摘要:
      Nowadays, aiming at the defects commonly used Backward Propagation (BP) neural network Proportional-Integral-Differential (PID) algorithm, namely back propagation algorithm complexity, slow convergence in solving complex nonlinear problems, the network to converge to local minima, leading to failure of network training, while the neural network controller can meet the requirements in the software simulation run results, but in the field of engineering the actual application is difficult to adapt to the rapid and timely control of complex industrial field required. To solve the above problems, a new multi neuron PID neural network algorithm is proposed. Its principle by simplifying the PID neural network controller, its initialization was improved to achieve new neuron PID control process is fast and the timeliness of response, which would be applied to the hydraulic straightening machine four cylinder servo controller. With the new multi neuron PID controller of traditional neural network controller and the displacement response curve of this research compared, the new neuron PID servo controller with BP algorithm is simplified, and the advantages of fast convergence speed, good flexibility, and real time network weights, provide theoretical support and technical reference for the application of multi neuron PID controller engineering.
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