摘要
为解决大功率交流伺服系统存在非线性和参数时变等不确定性的问题,提出一种混沌搜索的自适应变异粒子群优化小波神经网络的预测模型。建立交流伺服电机数学模型,利用不同变异方法使粒子趋近于不同的搜索区域,引入混沌优化算法改进粒子群,采用基于混沌搜索的AMPSO-WNN算法,以提高全局收敛的概率和速度。仿真结果表明:优化后模型的预测精度高于优化前,且改进后算法具有较强的函数逼近能力,网络性能得到了显著提高,局部极小值问题得到了有效解决。
In order to solve the problem of nonlinearity and parameter time-varying uncertainty in high-power AC servo system, a predictive model of adaptive mutation particle swarm optimization wavelet neural network for chaotic search is proposed. The mathematical model of AC servo motor is established. Different mutation methods are used to make the particles close to different search areas. Chaos optimization algorithm is introduced to improve the particle swarm. The AMPSO-WNN algorithm based on chaotic search is used to improve the probability and speed of global convergence. The simulation results show that the prediction accuracy of the optimized model is higher than before, and the improved algorithm has strong function approximation ability, the network performance is improved significantly, and the local minimum value problem is effectively solved.
作者
李俊杰
侯远龙
侯润民
李佳恬
Li Junjie;Hou Yuanlong;Hou Runmin;Li Jiatian(School of Mechanical Engineering,Nanjing University of Science&Technology,Nanjing 210094,China)
出处
《兵工自动化》
2020年第3期61-65,86,共6页
Ordnance Industry Automation
关键词
小波神经网络
自适应变异粒子群算法
交流伺服控制
系统辨识
混沌搜索
wavelet neural network
adaptive mutation particle swarm algorithm
AC servo control
system identification
chaotic search
