State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, PR China.
ISA Trans. 2013 Nov;52(6):752-8. doi: 10.1016/j.isatra.2013.07.002. Epub 2013 Jul 30.
In this paper, a new adaptive control approach is presented for multivariate nonlinear non-Gaussian systems with unknown models. A more general and systematic statistical measure, called (h,ϕ)-entropy, is adopted here to characterize the uncertainty of the considered systems. By using the "sliding window" technique, the non-parameter estimate of the (h,ϕ)-entropy is formulated. Then, the improved neuron based controllers are developed for multivariate nonlinear non-Gaussian systems by minimizing the entropies of the tracking errors in closed loops. The condition to guarantee the strictly decreasing entropy of tracking error is presented. Moreover, the convergence in the mean-square sense has been analyzed for all the weights in the neural controllers. Finally, the comparative simulation results are presented to show that the performance of the proposed algorithm is superior to that of PID control strategy.
本文提出了一种新的自适应控制方法，用于具有未知模型的多元非线性非高斯系统。这里采用了一种更通用和系统的统计度量，称为(h,ϕ)-熵，来描述所考虑系统的不确定性。通过使用“滑动窗口”技术，给出了(h,ϕ)-熵的非参数估计。然后，通过最小化闭环中跟踪误差的熵，为多元非线性非高斯系统开发了改进的基于神经元的控制器。给出了保证跟踪误差熵严格递减的条件。此外，分析了神经网络控制器中所有权重的均方收敛性。最后，给出了比较仿真结果，表明所提出算法的性能优于 PID 控制策略。
