基于超混沌对角递归神经网络的非线性模型预测控制

• 作 者: Samira Johari Mahdi Yaghoobi Hamid R.Kobravi FANG Jing-hua
• 机 构: Department of Control,Mashhad Branch,Islamic Azad University,Mashhad 9187147578,Iran;Department of B
• 出 处: 中南大学学报（英文版）
• 发表日期: 2022年1期
• 关键词: nonlinear model predictive control diagonal recurrent neural network chaos theor

非线性模型预测控制器使用非线性预测模型来预测受控制系统的行为.在此,提出了一个超混沌对角递归神经网络数组,用于在前进窗口中建模和预测控制器下非线性系统的行为.为了改善超混沌对角线递归神经网络参数的收敛性,以更好地进行系统建模,可使用隐藏层中的逻辑映像来调整混沌程度.提出了一种基于超混沌对角递归神经网络的非线性模型预测控制方法.该方法借助改进的梯度下降法获得控制信号.将该控制器用于控制具有硬非线性.输入约束以及存在包括外部干扰在内的不确定性的连续搅拌反应器,仿真结果表明该方法在轨迹跟踪和干扰抑制方面的优越性能.神经网络的参数收敛和可忽略的预测误差、以及保证的稳定性和较高的跟踪性能是该方案的最大优势.“,”Nonlinear model predictive controllers (NMPC) can predict the future behavior of the under-controlled system using a nonlinear predictive model. Here, an array of hyper chaotic diagonal recurrent neural network (HCDRNN) was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window. In order to improve the convergence of the parameters of the HCDRNN to improve system \' s modeling, the extent of chaos is adjusted using a logistic map in the hidden layer. A novel NMPC based on the HCDRNN array (HCDRNN-NMPC) was proposed that the control signal with the help of an improved gradient descent method was obtained. The controller was used to control a continuous stirred tank reactor (CSTR) with hard-nonlinearities and input constraints, in the presence of uncertainties including external disturbance. The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection. Parameter convergence and neglectable prediction error of the neural network (NN), guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.