This paper is concerned with stabilization for a class of Takagi-Sugeno fuzzy neural networks (TSFNNs) with time-varying delays. An impulsive control scheme is employed to stabilize a TSFNN. We firstly establish the model of TSFNNs by using fuzzy sets and fuzzy reasoning and propose the problem of impulsive stabilization for this model. Then, we present several stabilization conditions based on...

This paper presents a switch methodology together with a sum of squares (SOS) techniques to synthesize a nonlinear controller for a polynomial Takagi-Sugeno (TS) fuzzy model. A polynomial T-S fuzzy model adopts a polynomial representation of the nonlinear dynamics in its consequent part, which make it less susceptible to linearization errors. With respect to polynomial T-S fuzzy models, a fuzzy...

To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy inference method based on production term for T-S Fuzzy model is explained. Then, compared with exist...

The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track a reference engine rotational speed and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the engine is simulated and simulation results presented. ANFIS implements a first order Sugeno-style fuzzy system. It is a method for tuning an existin...

This study introduces a fuzzy control design method for nonlinear systems with a guaranteed model reference tracking performance. First, the Takagi and Sugeno (TS) fuzzy model is employed to represent a nonlinear system. Next, based on the fuzzy model, a fuzzy observer-based fuzzy controller is developed to reduce the tracking error as small as possible for all bounded reference inputs. The adv...

This study presents an adaptation method for Sugeno fuzzy inference systems that maintain the readability and interpretability of the fuzzy model during and after the learning process. This approach can be used for modelling of dynamical systems and for building adaptive model-based control algorithms for chemical processes. The gradient-descent based learning algorithm can be used on-line to f...

A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi–Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS’s fuzzy model is applied to...

This paper presents a decentralized control problem for stabilizing the nonlinear large-scale descriptor (LSD) systems that use proportional-plus-derivative state (PD) feedback scheme. The have great focus on research because they can contain more physical characteristics than standard state-space systems. At first, each subsystem in LSD system be represented by Takagi-Sugeno (T-S) with interco...