Search

This paper is concerned with the problem of model reduction for discrete-time Takagi–Sugeno (T–S) fuzzy state-delay systems with finite-frequency input signals. A new finite-frequency model reduction algorithm is proposed, which can get a better approximation performance than the existing full-frequency methods. Firstly, a finite-frequency H ∞ performance index is defined in the frequency domain. Then, a stability condition and a finite-frequency H ∞ performance analysis condition are developed by the aid of Jensen׳s inequality and Parseval׳s theorem, respectively. Based on these conditions, sufficient model reduction conditions are derived for discrete-time T–S fuzzy state-delay systems. An optimization algorithm is proposed to obtain a stable reduced-order model satisfying the finite-frequency performance specification. Finally, the effectiveness of the proposed method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This study addresses the problem of releasing the conservatism of stability criteria for continuous-time T–S fuzzy systems on the basis of an extended approach. Recently, previous stability conditions have been relaxed by exploring the positive and negative properties of the time derivatives of normalized fuzzy weighting functions but the underlying approach is not very mature. By modeling the membership space of the time derivatives of normalized fuzzy weighting functions via selected binary numbers and decimal numbers, their positive and negative properties are more fully considered and some redundant terms are transformed as a positive role instead of being simply removed. Thus, less conservative stability criteria can be achieved. Finally, the effectiveness of the proposed approach is illustrated by numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, the problem of observer-based output feedback control design of discrete-time Takagi–Sugeno (T–S) fuzzy systems is investigated based on a multi-samples method. In order to ameliorate the existing results in the literature, a fuzzy observer-based output feedback controller, which relies on multi-samples normalized fuzzy weighting functions, is developed for relaxing the design conditions of observer-based output feedback control. Moreover, thanks to the special form of the developed observer-based output feedback controller, it is shown that all the designed parameters can be solved via a set of strict linear matrix inequalities, i.e., the drawback induced by the usual two-step method is resolved by means of a compromising way. Finally, the benefit of the proposed result is validated by using some numerical experiments. [ABSTRACT FROM AUTHOR]

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H ∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional regression problems is proposed, which is called multi-objective evolutionary hierarchical fuzzy regression system (MOEHFRS). Different from the existing hierarchical fuzzy systems with fixed topology, the proposed MOEHFRS improves the accuracy of the model, reduces the total number of rules, and eliminates unnecessary features by flexibly constructing topology. In the process of topology evolution, MOEHFRS can exchange and combine different sub-fuzzy systems to achieve the selection and reuse of important features, as well as the elimination of unnecessary features, which improves the diversity of topology and enables the model to be well applied to high-dimensional regression problems. Different combinations of sub-fuzzy systems will result in different performance and number of rules. A new multi-objective evolutionary optimization algorithm is proposed to simultaneously optimize the number of rules and the accuracy of the model, which achieves the balance between complexity and accuracy of MOEHFRS. The proposed method is validated on 13 real world regression datasets and compared with other 5 methods. The results show that MOEHFRS is effective and advanced in terms of accuracy, number of rules, retention of important features and universality in different regression problems. • Develop a multi-objective evolutionary hierarchical fuzzy regression system. • Propose a encoding method and evolutionary strategy for hierarchical fuzzy systems. • The system has good performance in complexity and precision. • Our approach is compared with other 5 methods on 13 regression datasets. [ABSTRACT FROM AUTHOR]