Based on a Takagi–Sugeno (T–S) fuzzy model and an inverse system method, this paper deals with the problem of actuator fault estimation for a class of nonlinear dynamic systems. Two different estimation strategies are developed. Firstly, T–S fuzzy models are used to describe nonlinear dynamic systems with an actuator fault. Then, a robust sliding mode observer is designed based on a T–S fuzzy m...

In this paper, a robust adaptive fuzzy neural network sliding mode (AFNNSM) control design is proposed to maximize the captured energy for a variable speed wind turbine and to minimize the efforts of the drive shaft. Fuzzy neural network (FNN) is used to improve the mathematical system model, by the prediction of model unknown function, which is used by the Sliding mode control approach (SMC) a...

The purpose of designing the active suspension systems is providing comfort riding and good handling in different road disturbances. In this paper a novel control method based on adaptive neuro fuzzy system in active suspension system is proposed. Choosing the proper data base to train the ANFIS has an important role in increasing the suspension system’s performance. The data base which is used...

This paper presents the dynamic modeling and fuzzy sliding mode control for rigid-flexible multibody systems. To investigate the dynamic stiffening of rigid-flexible systems, a firstorder approximate model of a flexible spacecraft system is formulated by using Hamilton’s principles and assumed mode method, taking into account the second-order term of the coupling deformation field. For highly f...

An adaptive sliding-mode control system, which is insensitive to uncertainties, is proposed to control the position of an induction motor drive. The designed sliding mode control presents an adaptive switching gain to relax the requirement for the bound of uncertainties. The switching gain is adapted using a simple algorithm which do not implies a high computational load. Stability analysis bas...