Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems
Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems
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DOI：
10.1007/s11071-010-9876-2
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发表时间：
2011-07
影响因子：
5.6
通讯作者：
Yi‐Shao Huang;De-Qun Zhou
中科院分区：
文献类型：
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作者：
Yi‐Shao Huang;De-Qun Zhou
在Huang等人的工作中，基于局部控制器可以共享各自参考模型的先验信息这一前提条件，得到了非线性大系统的协调分散混合自适应输出反馈模糊控制方案。在缺乏协调假设的情况下，针对一类不确定大系统，我们致力于设计一种经典的分散间接和直接相结合的自适应模糊控制器。为每个子系统提出的输出反馈和自适应机制仅取决于其单独的输出，而不考虑任何其他输出参考。该算法既不需要著名的严格正实(SPR)条件，也不需要高增益观测器(HGO)来实现。证明了闭环系统的跟踪误差收敛到原点的可调邻域。对相关倒立摆的仿真结果验证了分散控制器改进的有效性。
In the previous work of Huang et al., a coordinated decentralized hybrid adaptive output feedback fuzzy control scheme of large-scale nonlinear systems is obtained predicated upon this prerequisite assumption that the local controllers can share the a priori information about their individual reference models. In this note, we concentrate in the absence of the coordination assumption on developing a classical decentralized combined indirect and direct adaptive fuzzy controller for a class of uncertain large-scale nonlinear systems. The output feedback and adaptation mechanisms proposed for each subsystem hinges just upon its individual output, regardless of any other output reference. Neither the famous strictly positive real (SPR) condition nor a high-gain observer (HGO) is required to realize the overall output feedback algorithm. The tracking errors of the closed-loop large-scale system are shown to converge to tunable neighborhoods of the origin. Simulation results on correlated inverted pendulums verify the validity of the decentralized controller modification.