Afshar Puya, Wang Hong, Chai Tianyou
Control Systems Centre, School of Electrical and Electronic Engineering, The University of Manchester, M60 1QD, Manchester, The United Kingdom.
IEEE Trans Neural Netw. 2009 Mar;20(3):471-82. doi: 10.1109/TNN.2008.2010351. Epub 2009 Feb 13.
In this paper, a new method for adaptive control of general nonlinear and non-Gaussian unknown stochastic systems has been proposed. The method applies the minimum entropy control scheme to decrease the closed-loop randomness of the output under an iterative learning control (ILC) basis. Both modeling and control of the plant are performed using dynamic neural networks. For this purpose, the whole control horizon is divided into a certain number of time domain subintervals called batches and a pseudo-D-type ILC law is employed to train the plant model and controller parameters so that the entropy of the closed-loop tracking error is made to decrease batch by batch. The method has the advantage of decreasing the output uncertainty versus the advances of batches along the time horizon. The analysis on the proposed ILC convergence is made and a set of demonstrable experiment results is also provided to show the effectiveness of the obtained control algorithm, where encouraging results have been obtained.
本文提出了一种用于一般非线性和非高斯未知随机系统自适应控制的新方法。该方法在迭代学习控制（ILC）的基础上应用最小熵控制方案来降低输出的闭环随机性。利用动态神经网络对被控对象进行建模和控制。为此，将整个控制时域划分为若干个称为批次的时域子区间，并采用伪D型ILC律来训练被控对象模型和控制器参数，使得闭环跟踪误差的熵逐批减小。该方法具有随着时间域上批次的推进降低输出不确定性的优点。对所提出的ILC收敛性进行了分析，并给出了一组可证明的实验结果以表明所获得控制算法的有效性，取得了令人鼓舞的结果。
