An adaptive PID neural network for complex nonlinear system control
An adaptive PID neural network for complex nonlinear system control
复制标题
用于复杂非线性系统控制的自适应PID神经网络
DOI：
10.1016/j.neucom.2013.03.065
复制
发表时间：
2014-07-05
期刊：
影响因子：
6
通讯作者：
Liu, Hongbo
中科院分区：
文献类型：
--
作者：
Kang, Jun;Meng, Wenjun;Liu, Hongbo
通常，复杂非线性系统的控制问题很难解决。本文提出了一种基于自适应 PID 神经网络和粒子群优化（PSO）算法的有效控制方法。本文引入了 PSO 算法来初始化神经网络，以提高收敛速度，防止权重陷入局部最优状态。为了适应控制系统中初始不确定和变化的参数，我们引入了改进的梯度下降法来调整网络参数。我们根据 Lyapunov 方法分析了控制器的稳定性。对具有强耦合的复杂非线性多输入多输出 (MIMO) 系统进行了仿真。经验结果表明，与其他考虑过的方法相比，所提出的控制器能在更短的时间内获得良好的精度。(c) 2014 Elsevier B.V. 版权所有。保留所有权利。
Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods. (c) 2014 Elsevier B.V. All rights reserved.