基于粒子群优化的Elman神经网络无模型控制

摘要

针对一类无法建模或是建模过程比较复杂的离散SISO非线性离散系统，提出了一种基于Elman神经网络和粒子群优化算法的无模型控制方法。该控制方法是在无需知道被控对象动力学模型的情况下，以Elman神经网络作为控制器结构，利用粒子群优化算法在线学习控制器中的所有权值参数，既而得到每一离散时刻的最优控制量。仿真研究表明，该方法控制下的非线性系统输出信号具有较快的反应速度和较小的跟踪误差，同时控制量信号有较好的收敛性与控制精度，这说明了所提出的基于粒子群的Elman神经网络无模型控制方法是有效与合理的。%In this paper, we propose amodel⁃free control method, based on the Elman neural network and the parti⁃cle swarm optimization algorithm, for a class of single⁃input single⁃output ( SISO ) discrete nonlinear systems, whose mathematical model cannot be established or is not easily modeled. In the model⁃free control system, it is not necessary to establish a mathematical model for each object. The Elman neural network is the controller and all the online weight parameters are learned using the particle swarm optimization algorithm. Using the proposed method, we obtain the optimal control variable at each discrete time.Them odel⁃free control method simulation results demon⁃strate that the nonlinear system output signal has a fast response rate and few tracking errors. Moreover, the control variable has good convergence and high control accuracy. These results prove that the proposed method is reasona⁃ble and effective.