Wang Shiwei, Yu D L
Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
Neural Netw. 2008 Jan;21(1):102-12. doi: 10.1016/j.neunet.2007.10.006. Epub 2007 Dec 3.
In the application of variable structure control to engine air-fuel ratio, the ratio is subjected to chattering due to system uncertainty, such as unknown parameters or time varying dynamics. This paper proposes an adaptive neural network method to estimate two immeasurable physical parameters on-line and to compensate for the model uncertainty and engine time varying dynamics, so that the chattering is substantially reduced and the air-fuel ratio is regulated within the desired range of the stoichiometric value. The adaptive law of the neural network is derived using the Lyapunov method, so that the stability of the whole system and the convergence of the networks are guaranteed. Computer simulations based on a mean value engine model demonstrate the effectiveness of the technique.
在将变结构控制应用于发动机空燃比时，由于系统不确定性，如未知参数或时变动力学特性，空燃比会出现抖振现象。本文提出一种自适应神经网络方法，用于在线估计两个不可测量的物理参数，并补偿模型不确定性和发动机时变动力学特性，从而大幅降低抖振，并将空燃比调节在化学计量比所需范围内。利用李雅普诺夫方法推导了神经网络的自适应律，从而保证了整个系统的稳定性和网络的收敛性。基于均值发动机模型的计算机仿真验证了该技术的有效性。
