Adaptive fuzzy decentralized output feedback control for stochastic nonlinear large-scale systems

Dynamic output feedback control with output quantizer for nonlinear uncertain T‐S fuzzy systems with multiple time‐varying input delays and unmatched disturbances

“…The Lyapunov-Krasovskii stability theory is one of the most popular analysis tools for investigating the stability of the fuzzy closed-loop system, and stability conditions in the form of Lyapunov-Krasovskii inequalities are reported in [10]. Because the adaptive fuzzy or T-S-fuzzy-model-based control schemes are universal approximators and have the ability to approximate nonlinear functions, some adaptive fuzzy or fuzzy-affinemodel-based control schemes [11,12] have been developed to deal with unknown nonlinear uncertainties. Hence, the T-S fuzzy model technique is popular as a general tool to model nonlinear functions in intelligent control fields [12].…”

“…It should be pointed out that the time-delay problem is usually time-varying and uncertain [13]. Therefore, the superiority of T-S fuzzy sets over conventional fuzzy sets in dealing with the uncertain grades of fuzzy membership functions for the various applications is shown in [10][11][12][13]. Unlike the above literatures, not only is the T-S fuzzy model employed to approximate the uncertain system in this paper, but the output space is also partitioned into operating regions and interpolation regions based on the structural information in the fuzzy rules.…”

Adaptive fuzzy dynamic surface tracking control for a class of nonlinear systems with unknown distributed time delays and backlash-like hysteresis

“…Moreover, assumptions 3 and 4 are both common, which can be found in many existing literature. 25,15,17,23,36 Due to the assumption of jðtÞj M (M may be a large constant), the stability of the closed-loop control system of this article is built in the sense of semi-globally uniformly ultimately bounded (SUUB). 15,17 Moreover, since the states of the system (1) are unmeasurable, (1) can be rewritten as follows…”

Adaptive fuzzy vector control for a doubly-fed induction motor

“…As in the literature [23][24][25][26], one assumes that the used fuzzy systems do not violate the universal approximator property on the compact set D x i , which is assumed large enough so that the input vector of those fuzzy systems remains in D x i under the closedloop control system. It is hence reasonable to assume that the fuzzy approximation error is bounded as follows:…”