基于小波神经网络的网络化模型预测控制
Networked Model Predictive Control Using a Wavelet Neural Network
摘要：
在这项研究中，我们使用一个小波神经网络与前馈组件和模型预测控制器的在线非线性系统识别通信网络。利用小波神经网络对非线性系统进行在线辨识。模型预测控制器（MPC）使用模型来预测系统在扩展的预测范围内的未来输出，并通过最小化控制器代价函数来计算最优的未来输入。李雅普诺夫理论被用来证明MPC的稳定性。我们将该方法应用于无人驾驶自主车辆的在线识别和控制。仿真结果表明，在存在固定或随机网络时延的情况下，扩展预测时域的MPC都能有效地控制系统。
英文摘要：
In this study, we use a wavelet neural network with a feedforward component and a model predictive controller for online nonlinear system identification over a communication network. The wavelet neural network (WNN) performs the online identification of the nonlinear system. The model predictive controller (MPC) uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. The Lyapunov theory is used to prove the stability of the MPC. We apply the methodology to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with extended prediction horizon can effectively control the system in the presence of fixed or random network delay.
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