in this paper, an intelligent controller is applied to control omni-directional robots motion. first, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named lolimot. then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. this emotional lea...

There are many methods in identification developing every day. But identification of dynamic systems has still remained a complex open problem. One of the new effective methods of identification in nonlinear problems is identification with Neurofuzzy approach. Compared with classic neural network and wavelet network this method is faster and more accurate, demonstrated with an example in this p...

This document describes the implementation of a conical tank control system using an adaptive neurofuzzy system. For implementation, indirect approach is used where controller optimized model obtained during plant identification carried out data operation. Furthermore, includes training neuro fuzzy-systems and application to tank. Regarding identification, preliminary takes place for different ...

Neurofuzzy systems have been developed as grey box modelling technique ideal for the task of system identiication. Neurofuzzy models combine the mathematical structure of Associative Memory Networks (AMNs) with the transparency of fuzzy systems. This produces a modelling technique to which mathematical analysis can be applied, while being more transparent than traditional black box models. Unfo...

A self-tuning neurofuzzy integrating controller is derived in this paper for offset eliminating purpose. CARIMA plant model is used and the control law produces integral control terms in a natural way. Neurofuzzy networks are chosen to implement the direct self-tuning nonlinear integrating controller. The performance of the self-tuning integrating neurofuzzy controller is illustrated by example...

In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional l...

An online fault detection and isolation scheme for nonlinear systems based on neurofuzzy modelling and pattern matching is developed in this paper. The system is first modelled offline by a neurofuzzy network using data obtained under normal operating conditions. Another neurofuzzy network is then used to model the residual, which is the difference between the output of the system and that from...

Understanding data is usually done extracting fuzzy or crisp logical rules using neurofuzzy systems, decision trees and other approaches. Prototypebased rules are an interesting alternative providing in many cases simpler, more accurate and more comprehensible description of the data. Algorithm for generation of threshold prototype-based rules are described and a comparison with neurofuzzy syst...

The paper focuses on the problem of robust fault detection using neurofuzzy model based strategies. The main objective of the work is to show how to employ bounding error approach to determine the uncertainty of the neurofuzzy model and next utilize this knowledge for robust fault detection. The paper presents also how to tackle the problem of choosing the right structure of the neurofuzzy mode...

Model-reference adaptive control with neurofuzzy methodology is derived in this paper. Associate memory network(AMN) is investigated in detail to be the possible implementation as the direct self-tuning nonlinear controller. The essence of the neurofuzzy controller has been discussed and the local stability of the system is reached. The performance of the model-reference adaptive neurofuzzy con...