IEEE Trans Cybern. 2016 Jul;46(7):1511-23. doi: 10.1109/TCYB.2015.2451116. Epub 2015 Jul 24.
In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.
本文提出了一种新的自适应鲁棒在线构造模糊控制（AR-OCFC）方案，采用在线构造模糊逼近器（OCFA）来处理具有不确定性和未知干扰的跟踪水面车辆的问题。本文的主要贡献如下：1）与以前的自组织模糊神经网络不同，OCFA 使用解耦的距离度量来动态分配每个维度中可区分的和稀疏的模糊集，并且能够简洁地自我构建具有高可解释性的 T-S 模糊规则；2）通过采用从 Lyapunov 综合中得出的改进的基于投影的自适应律，设计了基于 OCFA 的主导自适应控制器（DAC），该自适应律可以保证合理的模糊划分；3）通过稳定消除和解耦自适应补偿，分别保证闭环系统的稳定性和鲁棒性，从而有助于构建辅助鲁棒控制器（ARC）；4）由 DAC 和 ARC 组成的 AR-OCFC 可以保证全局渐近闭环系统，并且所有信号都是有界的。仿真研究和与现有固定结构和动态结构自适应控制方案的综合比较表明，AR-OCFC 在跟踪和逼近精度方面具有优越的性能。
