fuzzy filter for non‐linear sampled‐data systems under imperfect premise matching

Extended Disturbance Observer-Based Integral Sliding Mode Control for Nonlinear System via T–S Fuzzy Model

“…Content may change prior to final publication. Moreover, by applying the above result and first inequality in (7), an upper bound of T −1 T can be formulated as follows:…”

“…The key idea of the T-S fuzzy modelling technique is that it allows us to represent a nonlinear system as a linear combination of state space models and its corresponding membership functions, which indicates that it is possible to apply linear control theories [1], [2] to the T-S fuzzy model. Due to its powerful advantages, various studies, including fuzzy filter [7], [8], [39], fuzzy tracking control [9], [41], observer-based control [10], and sampleddata approach [11], have been successfully developed and well established. Besides foregoing studies, robust control techniques based on the T-S fuzzy model [6], [12]- [17], [34], [39], [44] also have been greatly studied since guaranteeing a certain stability of the system is a crucial issue in designing a controller.…”

Disturbance observer‐based integral fuzzy sliding‐mode control and its application to wind turbine system

“…Given the aforementioned advantages, various studies have focused on an analysis of the T–S fuzzy model, including a fuzzy filter [16], robust control [17–19], tracking control [20], and observer‐based control [21, 22]. In many previous studies [16–22], the parallel distribution compensation (PDC) method [23] has been applied to a stability analysis. The main feature of the PDC method is the design of fuzzy controller membership functions similar to those of the T–S fuzzy model.…”

Intelligent digital redesign for T–S fuzzy systems: sampled‐data filter approach

“…These constraints make it difficult to apply the Kalman filter in many practical situations. To overcome this problem, the H ∞ filter technique has received much attention in recent years [5][6][7][8][9][10][11]. The H ∞ filter is designed by minimising the estimation error in the bounded disturbance and noise of the worst case.…”

“…Because of these features, the H ∞ filter has been studied for various systems such as time-delay systems [5,6], interconnected systems [7], and non-linear systems [8,9]. In particular, the H ∞ filter combined with the Takagi-Sugeno (T-S) fuzzy model is one of the remarkable filtering techniques [10,11] because non-linear system can be represented by a set of linear models interpolated by membership functions.…”