摘要
针对工业控制系统中存在的一系列非线性和时变性,以及传统自适应控制方法中的瞬态响应差等问题,提出了一种基于粒子群优化(PSO)的神经网络多模型切换自适应控制方法。利用粒子群优化算法对神经网络权重进行调节得到最优权值,基于BP神经网络和多模型设计自适应控制方案,构造合理的切换准则,使得系统在任意时刻都可以选择最优控制器对系统进行控制,并利用神经网络良好的逼近能力有效提高自适应控制的效果。最后,通过MATLAB仿真结果验证,所提的优化算法收敛快,精度高,有较好的网络泛化和逼近能力,能够很好地跟踪控制系统的输出。
In view of the nonlinearity and time variability of industrial control systems, as well as the poor transient response in traditional adaptive control, presents a neural network multi-model switching adaptive control method basing on particle swarm optimization. Firstly, the PSO algorithm was used to adjust the neural network weights to achieve the optimal value. Then an adaptive control strategy was designed basing on the BPNN and multiple models. The optimal controller can be selected to control the system through the constructed rational switching rules. The good approximation ability of neural network can improve the performance of adaptive control. The performance through PSO optimization are studied through simulation methods using MATLAB, which verifies that the proposed method can significantly improve the overall performance of the system: fast convergence, high precision, good network generalization and approximation ability, and can precisely track the output of the control system.
作者
王丽丽
辛玲
Wang Lili;Xin Ling(College o£Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《电子测量技术》
北大核心
2021年第3期99-103,共5页
Electronic Measurement Technology
基金
山东省自然科学基金(2015ZRB019FA)项目资助。
关键词
瞬态响应
自适应控制
神经网络
粒子群优化
transient response
adaptive control
neural network
particle swarm optimization
