Search results for "Fuzzy control system"
showing 6 items of 96 documents
A novel identification method for generalized T-S fuzzy systems
2012
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from the publisher:http://dx.doi.org/10.1155/2012/893807 In order to approximate any nonlinear system, not just affine nonlinear systems, generalized T-S fuzzy systems, where the control variables and the state variables, are all premise variables are introduced in the paper. Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm. The simulation results show the effectiveness of the proposed algorithm
Robust Observer Design for Takagi-Sugeno Fuzzy Systems with Mixed Neutral and Discrete Delays and Unknown Inputs
2012
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from the publisher:http://dx.doi.org/10.1155/2012/635709 A robust observer design is proposed for Takagi-Sugeno fuzzy neutral models with unknown inputs. The model consists of a mixed neutral and discrete delay, and the disturbances are imposed on both state and output signals. Delay-dependent sufficient conditions for the design of an unknown input T-S observer with time delays are given in terms of linear matrix inequalities. Some relaxations are introduced by using intermediate variables. A numerical example is given to illustrate the effectiveness of the given results.
Fuzzy Variable Structure Control for Uncertain Systems with Disturbance
2012
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from the publisher:http://dx.doi.org/10.1155/2012/105074 This paper focuses on the fuzzy variable structure control for uncertain systems with disturbance. Specifically, the fuzzy control is introduced to estimate the control disturbance, the switching control is included to compensate for the approximation error, and they possess the characteristic of simpleness in design and effectiveness in attenuating the control chattering. Some typical numerical examples are presented to demonstrate the effectiveness and advantage of the fuzzy variable structure controller proposed.
Fuzzy dynamic sliding mode control design for high order disturbed systems
2013
In this paper, the problem of fuzzy dynamic sliding mode control design is investigated for a class of disturbed systems. Specifically, the fuzzy controller is constructed based on one feedback signal to estimate the unknown nonlinear terms and to develop the sliding mode control according to the fuzzy rules. Furthermore, it possesses the characteristic of simplicity in design and effectiveness in attenuating chattering. Finally, a numerical example is included to demonstrate the effectiveness and advantage of the proposed method.
Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system
2012
Published version of an article in the journal: The International Journal of Advanced Manufacturing Technology. Also available from the publisher at: http://dx.doi.org/10.1007/s00170-012-4057-8 Nowadays, material planning and control strategies are becoming continuously complex tasks spanning from individual plants to logistic networks. In fact, this is the consequence of increasing intricacy in product variants and their respective convolution in networks’ structures. Customers ask for specific products with individual characteristics that force companies for more clever performances by more flexibility. For doing so, the existing planning and control systems, which work based on central m…
Fuzzy Systems Based on Multispecies PSO Method in Spatial Analysis
2012
We present a method by using the hierarchical cluster-based Multispecies particle swarm optimization to generate a fuzzy system of Takagi-Sugeno-Kang type encapsulated in a geographical information system considered as environmental decision support for spatial analysis. We consider a spatial area partitioned in subzones: the data measured in each subzone are used to extract a fuzzy rule set of above mentioned type. We adopt a similarity index (greater than a specific threshold) for comparing fuzzy systems generated for adjacent subzones.