非线性状态空间方法辨识电液伺服控制系统

针对回归神经网络辨识和建立非线性动态系统模型的问题，研究非线性状态空间描述的回归神经网络数学模型。讨论极小均方误差网络训练收敛准则，通过研究Kalman 滤波估计公式中的随机变量，提出一种参数增广的回归神经网络非线性状态方程，无导数的Kalman滤波器用于增广参数估计，人工白噪声强迫网络学习，更新网络权值，避免了扩展Kalman滤波器计算Jacobian信息和基于递度学习算法收敛慢的问题。在电液伺服系统辨识建模的应用中表明，回归神经网络较好地跟踪了液压油缸压力变化，与扩展Kalman滤波估计学习算法相比，新的算法具有较快的收敛和精度。

Nonlinear State Space Approach for Identifying Electro-hydraulic Servo Control System

For the purpose of solving the problems in identification and modeling of nonlinear dynamic system using recurrent neural networks (RNN), a nonlinear state space model is investigated for RNN. The convergence criterion for networks training is discussed under minimum mean square error (MMSE). The stochastic variable in the Kalman filter formulations is researched. A parameter-augmented nonlinear state space equation for RNN is proposed. A derivative-free Kalman filter is employed to estimate the augmented parameters and to update weights of RNN by using artificial white noise to compel RNN to learn. Compared with the extended Kalman filter (EKF), computation of Jacobian information is avoided and the problem of slow convergence rate of algorithm based on gradient learning is also solved. The application of RNN in the identification and modeling of an electro-hydraulic servo system shows that RNN is capable of tracking the dynamic pressure of the hydraulic cylinder. The new algorithm has faster convergence and higher precision compared to the algorithm of extended Kalman filter.