非线性时滞系统的稳定自适应控制(英文)

Stable adaptive neural network control of nonlinear time delay systems

针对模型不确定性的连续时间时滞系统,提出了一种新的神经网络自适应控制。系统的辨识模型是由神经网络和系统的已知信息组合构成,在此基础上,建立时滞系统的预测模型。基于神经网络预测模型的自适应控制器能够实现期望轨线的跟踪,理论上证明了闭环系统的稳定性。连续搅拌釜式反应器仿真结果表明了该控制方案的有效性。

In this paper a new adaptive neural network controller is presented for a class of continuous-time nonlinear time delay systems subject to modeling uncertainty. The neural network model requires a priori knowledge about plant dynamics to provide prediction models for time delay systems. An adaptive controller based on neural networks was developed to produce the desired tracking performance in uncertain conditions. Stability of the closed-loop system is proved by the Lyapunov method. The effectiveness of the proposed scheme was demonstrated through its application to the control of a continuous stirred tank reactor.