Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems.

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters.

Recurrent general type-2 fuzzy neural networks for nonlinear dynamic systems identification.

This paper introduces a recurrent general type-2 Takagi-Sugeno-Kang fuzzy neural network (RGT2-TSKFNN) for the identification of nonlinear systems. In the proposed structure, the general type-2 fuzzy set (GT2FS) and a recurrent fuzzy neural network (RFNN) are combined to obviate the data uncertainties. The fuzzy firing strengths in the developed structure are returned to the network input as internal variables.

Adaptive interval type-2 fuzzy controller for nonlinear networked Wiener systems subject to packet dropout and time-varying delay.

In this paper, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is proposed for a class of nonlinear networked Wiener systems under packet dropout and time varying delay. The proposed AIT2FC compensates the negative effects of the packet dropout and time varying delay in both forward and feedback loops. The structure of the proposed AIT2FC has three parts, an adaptive interval type-2 Takagi-Sugeno (IT2TS) fuzzy controller, an IT2TS fuzzy Wiener model (IT2TS-FWM), and a time-varying delay and packet dropout compensator.

Embedded fuzzy sliding mode control for polymer extrusion process.

The present study introduces the problem of controlling the polymer extrusion (PE) machine by applying a fuzzy sliding mode control (FSMC) structure. The PE is an uncertain and perturbed common industrial process. The problems of the PE often arise from a maladjusted and unsteady feeding rate.

Stable adaptive probabilistic Takagi-Sugeno-Kang fuzzy controller for dynamic systems with uncertainties.

In this study, an adaptive probabilistic Takagi-Sugeno-Kang fuzzy PID (APTSKF-PID) scheme is developed to control nonlinear systems. The proposed controller merges the features of the TSK fuzzy logic system, which possess a superior performance in system size and learning accuracy than the Mamdani-type fuzzy systems and the probabilistic processing method in nonlinear control, which handles the system uncertainties. To achieve controlled system stability, Lyapunov function is used for tuning the controller parameters.

A novel fuzzy Wiener-based nonlinear modelling for engineering applications.

This study proposes a novel fuzzy Wiener structure for identifying engineering systems. The proposed model has a cascade structure; a nonlinear static part preceded by a linear dynamic part. The nonlinear static part is represented by an interval type-2 fuzzy Takagi-Sugeno-Kang (IT2TSK) system in which the antecedents of the rules are described by interval type-2 fuzzy sets (IT2FSs) and a TSK-type system describes the consequents.

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables.

Embedded intelligent adaptive PI controller for an electromechanical system.

In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system.

Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop simulation.

Objective: This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller.

Methods And Materials: The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters.

Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system.

In this paper, the interval type-2 fuzzy proportional-integral-derivative controller (IT2F-PID) is proposed for controlling an inverted pendulum on a cart system with an uncertain model. The proposed controller is designed using a new method of type-reduction that we have proposed, which is called the simplified type-reduction method. The proposed IT2F-PID controller is able to handle the effect of structure uncertainties due to the structure of the interval type-2 fuzzy logic system (IT2-FLS).