| 采摘机械臂的PSO-RBF神经网络自适应控制 |
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| 引用本文： | 董中华,陈鹏飞,李亚龙.采摘机械臂的PSO-RBF神经网络自适应控制[J].机械制造与自动化,2022,51(1):181-183,233. |
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| 作者姓名： | 董中华 陈鹏飞 李亚龙 |
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| 作者单位： | 兰州理工大学 机电工程学院,甘肃 兰州730050 |
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| 摘 要： | 针对采摘机械臂系统的不确定性为控制带来的问题,设计一种PSO-RBF神经网络自适应控制方法。该方法使用径向基函数神经网络来逼近并补偿系统模型误差,用粒子群优化算法来优化RBF的权值参数,确保PSO-RBF控制性能更好。MATLAB仿真结果表明:与RBF神经网络控制相比,PSO-RBF神经网络控制精度和性能更好。
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| 关 键 词： | 机械臂 RBF神经网络 粒子群算法 MATLAB仿真 |
Picking Manipulator Control Based on PSO-RBF Neural Network |
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| DONG Zhonghua,CHEN Pengfei,LI Yalong.Picking Manipulator Control Based on PSO-RBF Neural Network[J].Machine Building & Automation,2022,51(1):181-183,233. |
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| Authors: | DONG Zhonghua CHEN Pengfei LI Yalong |
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| Affiliation: | (School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,China) |
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| Abstract: | Concerning the control problems caused by the uncertainty of the manipulator system,a new PSO-RBF neural network adaptive control method is designed,with which a radial basis function neural network is used to approximate and compensate for system model errors,and particle swarm optimization is applied to optimize RBF weight parameters to ensure better PSO-RBF control performance.The MATLAB simulation experiment results show that PSO-RBF neural network control manifests higher accuracy and better performance than that of RBC. |
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| Keywords: | manipulator RBF neural network particle swarm optimization MATLAB simulation |
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