Adaptive Fuzzy Output-Feedback Controller Design for Nonlinear Systems via Backstepping and Small-Gain Approach

2014 ◽  
Vol 44 (10) ◽  
pp. 1714-1725 ◽  
Author(s):  
Zhi Liu ◽  
Fang Wang ◽  
Yun Zhang ◽  
Xin Chen ◽  
C. L. Philip Chen
Author(s):  
Ce Liu ◽  
Junyong Zhai

This article concentrates on the output feedback controller design for a class of stochastic nonlinear systems with unknown homogeneous growth rates. First, a full-order observer is proposed coupling with a dynamic gain so as to obtain system state estimates. Then, an adaptive output feedback controller is put forward by the homogeneity theory and adding a power integrator technique. Combined with the stochastic Barbalat’s lemma, the signals of the closed-loop system are demonstrated to be bounded and all the system states are proved to converge to the origin in probability. Besides, the results are also expanded to the controller design of upper-triangular stochastic nonlinear system. Two simulation results indicate usefulness of the designed control algorithm.


2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xingang Zhao

This paper is concerned with the problem of designingH∞controllers via static output feedback controller for a class of complex nonlinear systems, which is approximated by continuous-time affine fuzzy models. A decomposition method is presented to divide the output-space into different operating regions and interpolation regions. Based on this partition, a novel piecewise controller with affine terms via static output feedback is designed. By using a dilated linear matrix inequality (LMI) characterization, some nonconvex conditions are converted into convex ones to make the asymptotic stability andH∞performance of the closed-looped system. The effectiveness of the proposed method is illustrated by a numerical example.


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