Fuzzy control is a model free approach, i.e., it does not require a mathematical model of the system under control. An observer-based indirect adaptive fuzzy neural tracking control equipped with VSS and H∞ control algorithms is developed for nonlinear SISO systems involving plant uncertainties and external disturbances. Three important control methods, i.e., adaptive fuzzy neural control schem...

This paper deals with control design and stability analysis for a class of nonlinear systems with system uncertainties as well as input constraint when using the T-S fuzzy model. As a result, it achieves an adaptive fuzzy controller, which consists of two components: one is corresponding with the regular state feedback controller based on the LMI approach in order to deal with the known part in...

Motivated by the need to develop more effective methods of controlling uncertain non-linear systems, this paper focus on developing an adaptive fuzzy model-based controller, in which the optimisation variables remain in the fuzzy domain. The scheme uses an on-line fuzzy identification scheme, which is able to generate a fuzzy relational model using the training data from the system. The propose...

an asymptotically stable direct adaptive fuzzy pi sliding modecontroller is proposed for a class of nonlinear uncertain systems. in contrast toother existing approaches of handling disturbances, the proposed approachdoes not require this bound to be known, only requiring that it exists.moreover, a pi control structure is used to attenuate chattering. the approachis applied to stabilize an open-...

In this paper we have applied radial basis function neural networks for equalization of wireless channel in the presence of additive noise. We have been tested the adaptive filter algorithms of simple Kalman filter, extended Kalman filter, rbfn Kalman filter, rbfn extended Kalman filter, fuzzy Kalman filter and fuzzy extended Kalman filter which might be considered applicable for wireless chann...

Several scientific forecasting models for presidential elections have been suggested. However, most of these models are based on traditional statistics approaches. Since the system is linguistic, vague, and dynamic in nature, the traditional rigorous mathematical approaches are inappropriate for the modeling of this kind of humanistic system. This paper presents a combined neural fuzzy approach...

In this paper, a TSK-type compensatory neural fuzzy network (TCNFN) for classification applications is proposed. The TCNFN model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 3 of the TCNFN model contains adaptive compensatory fuzzy operations, which make fuzzy logic systems more adaptive and effective. Furthermore, a self-adaptive learning algorithm,...

This study is concerned with the problem of decentralized adaptive faulttolerant control for a class of large-scale non-affine and nonlinear systems with external disturbances and actuator failures including loss of effectiveness and bias. Based on fuzzy logic systems (FLSs) to approximate the appropriate nonlinear functions, a novel decentralized adaptive fuzzy fault-tolerant control scheme is...

design for a brain the origin of adaptive behavior PDF adaptive dynamic and resilient systems mobile services and systems PDF applications of complex adaptive systems PDF an introduction to genetic algorithms complex adaptive systems PDF signals and boundaries building blocks for complex adaptive systems PDF intelligent complex adaptive systems author ang yang apr-2011 PDF an introduction to fu...

Y. J. Zhang, L. B. Wu, H. Y. Zhao, X. D. Hu, W. Y. Zhang, D. Y. Ju, School of Materials and Metallurgy, University of Science and Technology Liaoning, China This paper studies the adaptive fuzzy control problem of the molten steel level for a class of twin roll strip casting systems. Based on fuzzy logic systems (FLSs) and the mean value theorem, a novel adaptive tracking controller with parame...