In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based control is proposed for the tracking of a Micro-Electro Mechanical Systems (MEMS) gyroscope sensor. The ANFIS is used to train parameters of the controller for tracking a desired trajectory. Numerical simulations for a MEMS gyroscope are looked into to check the effectiveness of the ANFIS control scheme. It proves that the sy...

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...

In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training eramples by a ...

In this paper, the discrete-time nonlinear systems identification and control based on an adaptive filter are introduced. This adaptive filter is mplemented using the adaptive network called Multi Input Fuzzy Rules Emulated Network (MIFREN). Inspired by the neuro-fuzzy network, the tructure of MIFREN resembles the human knowledge in the form of fuzzy IF-THEN rules. The initial value of MIFREN’s...

Fuzzy logic systems technology, particularly fuzzy control and fuzzy modeling techniques, is one of the most successful practical applications of fuzzy set and logic theory. In the past few years, the trend has been to combine fuzzy logic systems technology with artificial neural network technology to produce so called neural-fuzzy or fuzzy-neural systems. The objective is to take the advantage...

In this paper, a Fractional Adaptive Fuzzy Logic Control (FAFLC) strategy based on active fractional sliding mode (FSM) theory is considered to synchronize chaotic fractional-order systems. Takagi-Sugeno fuzzy systems are used to estimate the plant dynamics represented by unknown fractional order functions. One of the main contributions in this work is to combine an adaptive fractional order PI...

In this paper, an adaptive H interval type-2 fuzzy controller is proposed for a class of unknown nonlinear discrete-time systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 fuzzy control scheme and H control approach are incorporated to implement the main objective of controlling the plant to track a reference trajectory....

This paper addresses the problem of designing fin control for the ship roll stabilization. A novel adaptive robust fuzzy control (ARFC) algorithm is presented for ship roll nonlinear system with unstructured uncertainties. In the algorithm, the Takagi-Sugeno type fuzzy logic systems are employed to approximate uncertain functions in the systems, and a systematic procedure is developed for the s...

In this paper, an adaptive H interval type-2 fuzzy controller is proposed for a class of unknown nonlinear discrete-time systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 fuzzy control scheme and H control approach are incorporated to implement the main objective of controlling the plant to track a reference trajectory....

The paper presents a study upon the possibility to use adaptive-network-based fuzzy inference method (ANFIS) in the identification of distributed parameter systems, implementing a distributed sensor network in the system. Some main properties of different identification methods are presented with possible application. The fuzzy systems, implemented using rule bases, fuzzy values, membership fun...