This work is an attempt to illustrate the usage and effectiveness of soft computing techniques in the estimation and control of multi input and multi output systems. This paper focuses on neuro-fuzzy system ANFIS (Adaptive Neuro Fuzzy Inference system). An Adaptive Network based Fuzzy Interference System architecture extended to cope with multivariable systems has been used. The performance of ...

In this paper, a class of uncertain chaotic systems preceded by unknown backlash nonlinearity is investigated. Combining backstepping technique with fuzzy neural network identifying, an adaptive backstepping fuzzy neural controller (ABFNC) for uncertain chaotic systems with unknown backlash is proposed. The proposed ABFNC system is comprised of a fuzzy neural network identifier (FNNI) and a rob...

This paper describes the design of an adaptive direct control scheme for a class of nonlinear systems. The architecture is based on a fuzzy inference system (FIS) of Takagi Sugeno (TS) type to approximate a feedback linearization control law. The parameters of the consequent part of the fuzzy system are adapted and changed according to a law derived using Lyapunov stability theory. The asymptot...

This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for identification and control of a class of uncertain MIMO nonlinear systems. The proposed controller has the following salient features: (1) Selforganizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) ...

A fuzzy sliding-mode control with rule adaptation design approach with decoupling method is proposed. It provides a simple way to achieve asymptotic stability by a decoupling method for a class of uncertain nonlinear systems. The adaptive fuzzy sliding-mode control system is composed of a fuzzy controller and a compensation controller. The fuzzy controller is the main rule regulation controller...

A novel indirect adaptive backstepping control approach based on type-2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. Ultimately, the adaptive laws, by means of backsteppin...

In this study, an adaptive fuzzy sliding-mode control (AFSMC) system is adopted to control the position of an induction servo motor. The AFSMC system comprises the fuzzy control design and the robust control design. In the fuzzy control design a fuzzy controller is designed to mimic an ideal control law. In the robust control design a robust controller is designed to compensate the approximatio...

In this paper, an adaptive fuzzy controller based on fuzzy neural network is proposed for uncertain nonlinear systems. The main advantages are the simple design, no requirement of system model, and release of fixed universal range of fuzzy output. A fuzzy neural network is applied to on-line identify the control system and provide sufficient information of the adaptive laws for the proposed fuz...

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created...