2004
DOI: 10.1109/tfuzz.2003.819828
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New Fuzzy Control Model and Dynamic Output Feedback Parallel Distributed Compensation

Abstract: A new fuzzy modeling based on fuzzy linear fractional transformations model is introduced. This new representation is shown to be a flexible tool for handling complicated nonlinear models. Particularly, the new fuzzy model provides an efficient and tractable way to handle the output feedback parallel distributed compensation problem. We demonstrate that this problem can be given a linear matrix inequality characterization and hence is immediately solvable through available semidefinite programming codes. The c… Show more

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Cited by 51 publications
(45 citation statements)
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“…This is a well-known modelling task prior to designing robust controllers, see [26]. If the uncertain parameters are known this leads to gain-scheduling LPV solutions; the application of these ideas to the fuzzy modelling context was proposed in [27]. It can be shown that, in some cases, LFT allows representing rational expressions of nonlinearities with a lower number of rules than TS systems.…”
Section: Fuzzy Modellingmentioning
confidence: 99%
See 1 more Smart Citation
“…This is a well-known modelling task prior to designing robust controllers, see [26]. If the uncertain parameters are known this leads to gain-scheduling LPV solutions; the application of these ideas to the fuzzy modelling context was proposed in [27]. It can be shown that, in some cases, LFT allows representing rational expressions of nonlinearities with a lower number of rules than TS systems.…”
Section: Fuzzy Modellingmentioning
confidence: 99%
“…By 2005, there existed a large body of literature on stability analysis and design of T-S fuzzy control systems. Improvements have appeared in piecewise/gridpoint approaches [37,38], non-PDC design [39,40,41], multi-step non-monotonic Lyapunov functions [42], and LinearFractional transformation approaches to fuzzy modelling [27], with clear links to the LPV gain-scheduling concepts [18]. Widely used relaxations of the double-summation problem (which apply to many fuzzy results) appeared in [43,44], although conservatism remained.…”
Section: Model-based Fuzzy Control Designmentioning
confidence: 99%
“…This objective can be achieved by different approaches. It is possible to solve specific problems arising from the parallel modelization, such as the stabilization of decentralized fuzzy systems [34], or the output compensation [35]. Facing the problem from an architecture viewpoint, it is evident the gain in maximizing fuzzy control deployment when the architecture supports the effective distribution of pieces of the global control flow over the different computers (see [36][37][38][39] just to cite few works), but it is also immediately clear the difficulties in managing in a high-level way the (re)configuration and (re)distribution of control tasks in dynamic computer network.…”
Section: Conclusive Remarksmentioning
confidence: 99%
“…In certain practical situations, there is a strong need to construct dynamic output-feedback (DOF) controller instead of static controller in order to obtain better performance and dynamical behavior of state response. Thus, more recently, there have been a number of approaches to design DOF controllers for (fuzzy) control systems [5,11,12,17,18,21,23]. Utilizing the dynamic parallel distributed compensation (DPDC) scheme, [12] presented a systematic framework for designing DOF controllers for continuous-time T-S fuzzy systems; the corresponding discrete-time version was developed and successfully applied to a vehicle with triple trailers in [21].…”
Section: Introductionmentioning
confidence: 99%