Research on Adaptive Control of Down Pressure Based on RBF Neural Network

机译：基于RBF神经网络的下压力控制研究

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The structural field of grate cooling system in industry is relatively complex. In the actual control process, the traditional PID adaptive ability is not strong, because most of the controlled objects have the characteristics of large lag, resulting in unsatisfactory control effect. In order to overcome these problems, according to the working characteristics of grate cooler, the PID control method of RBF neural network is applied to the chip removal and pressure reduction control system of cement industry. The simulation results show that the PID controller with adaptive performance of RBF neural network is effective.

机译：工业炉篦冷却系统的结构场比较复杂。在实际控制过程中，传统的PID自适应能力不强，因为大多数受控物体都有大滞后的特点，导致控制效果不令人满意。为了克服这些问题，根据格栅冷却器的工作特性，RBF神经网络的PID控制方法应用于水泥工业的芯片去除和减压控制系统。仿真结果表明，具有RBF神经网络的自适应性能的PID控制器是有效的。

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《Youth Academic Annual Conference of Chinese Association of Automation》|2021年|402-405|共4页
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Yunzhao Xie; Xiaohong Wang; Hongliang Yu; Rongfeng Zhang;
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Industries; PI control; Simulation; Neural networks; Process control; Production; Pressure control;

机译：行业;PI控制;仿真;神经网络;过程控制;生产;压力控制;

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1. RBF Neural Network Backstepping Sliding Mode Adaptive Control for Dynamic Pressure Cylinder Electrohydraulic Servo Pressure System [J] . Deng Pan, Zeng Liangcai, Liu Yang Complexity . 2018,第1期

机译：动态压力缸电液伺服压力系统的RBF神经网络Backstepping滑模自适应控制
2. RBF Neural Network Backstepping Sliding Mode Adaptive Control for Dynamic Pressure Cylinder Electrohydraulic Servo Pressure System [J] . Deng Pan, Zeng Liangcai, Liu Yang Complexity . 2018,第3期

机译：RBF神经网络逆时位滑动模式动态压力缸电液伺服压力系统的自适应控制
3. RBF Neural Network Backstepping Sliding Mode Adaptive Control for Dynamic Pressure Cylinder Electrohydraulic Servo Pressure System [J] . Deng Pan, Zeng Liangcai, Liu Yang Complexity . 2018,第2期

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4. Rail pressure control of common rail diesel engine based on RBF neural network adaptive PID controller [C] . Ji Zhongming, Xie Xuan, Sun Zhiqiang, 2011 International Conference on Electronic Mechanical Engineering and Information Technology . 2011

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5. RBF neural network based generalized predictive control for nonlinear stochastic systems. [D] . Xin, Qi. 2013

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6. Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network [O] . Songwei Zeng, Haigen Hu, Lihong Xu, 2012

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7. RBF Neural Network Backstepping Sliding Mode Adaptive Control for Dynamic Pressure Cylinder Electrohydraulic Servo Pressure System [O] . Pan Deng, Liangcai Zeng, Yang Liu 2018

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Research on Adaptive Control of Down Pressure Based on RBF Neural Network

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