Data Science Institute of City University of Macau, Macau, China.
Department of Network Information of Shandong University of Art & Design, Jinan, Shandong, China.
PLoS One. 2020 Jun 17;15(6):e0234356. doi: 10.1371/journal.pone.0234356. eCollection 2020.
In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multi-kernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.
针对具有诱导时滞和随机丢包的多包传输航空发动机分布式控制系统的强非线性特点，提出了一种基于滑动窗口策略和多核 LS-SVM 丢包在线补偿的神经网络 PID 控制律滑模控制器。首先，将系统模型中的时滞项等效转换，建立无延时的多包传输离散系统模型；进一步将多核函数的构造转化为核函数系数优化问题，利用混沌自适应人工鱼群算法求解，然后通过滑动窗口多核 LS-SVM 对多包传输的数据丢包进行在线预测补偿。然后，提出了一种基于神经网络的比例积分微分控制律的滑模控制器设计方法。并通过神经网络的非线性映射实现 PID 控制律参数的在线调整。最后，使用 Truetime 对该方法进行仿真。结果表明，当丢包率为 30%和 60%时，多核 LS-SVM 的丢包预测平均误差分别比组合核 LS-SVM 降低了 29.21%和 44.66%，与其他五种控制律方法相比，所提出的神经网络 PID 控制律滑模控制器的抖动幅度降低。该控制器可以保证快速的响应速度，表明该方法可以实现对航空发动机网络控制系统的更好跟踪控制。
