Fuzzy model-based control of complex plants

Kiriakidis, Kiriakos

doi:10.1109/91.728444

Cited by 107 publications

(36 citation statements)

References 21 publications

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“…The design approaches for TS systems can be classified into six categories [97]: i) local controller design, where feedback controllers are designed for each local model and combined to obtain the global controller, and some stability criteria is used to check stability [50,99]; ii) stabilization based on a nominal linear model with nonlinearities considered as uncertainties [93,98]; iii) stabilization based on a common quadratic Lyapunov function [52,61,145,163,165,171,189,320,341]; iv) stabilization based on a piecewise quadratic Lyapunov function [93,95,96,149]; v) stabilization based on a fuzzy Lyapunov function [115,322]; vi) adaptive control, when the parameters of the TS fuzzy models are unknown [94,100,225].…”

Section: Gain-scheduling: Lpv Systems and Ts Systemsmentioning

confidence: 99%

Advances in Gain-Scheduling and Fault Tolerant Control Techniques

Rotondo¹

2018

Springer Theses

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This thesis presents some contributions to the state-of-the-art of the fields of gainscheduling and fault tolerant control (FTC).In the area of gain-scheduling, the connections between the linear parameter varying (LPV) and Takagi-Sugeno (TS) paradigms are analyzed, showing that the methods for the automated generation of models by nonlinear embedding and by sector nonlinearity, developed for one class of systems, can be easily extended to deal with the other class. Then, two measures, based on the notions of overboundedness and region of attraction estimates, are proposed in order to compare different models and choose which one can be considered the best one. Later, the problem of designing state-feedback controllers for LPV systems has been considered, providing two main contributions. First, robust LPV controllers that can guarantee some desired performances when applied to uncertain LPV systems are designed, by using a double-layer polytopic description that takes into account both the variability due to the varying parameter vector and the uncertainty. Then, the idea of designing the controller in such a way that the required performances are scheduled by the varying parameters is explored, which provides an elegant way to vary online the behavior of the closed-loop system. In both cases, the problem reduces to finding a solution to a finite number of linear matrix inequalities (LMIs), which can be done efficiently using the available solvers.In the area of fault tolerant control, the thesis first shows that the aforementioned double-layer polytopic framework can be used for FTC, in such a way that different strategies (passive, active and hybrid) are obtained depending on the amount of available information. Later, an FTC strategy for LPV systems that involves a reconfigured reference model and virtual actuators is developed. It is shown that by including the saturations in the reference model equations, it is possible to design a model reference FTC system that automatically retunes the reference states whenever the system is affected by saturation nonlinearities. In this way, a graceful performance degradation in presence of actuator saturations is incorporated in an elegant way. Finally, the problem of FTC of unstable LPV systems subject to actuator saturations is considered. In this case, the design of the virtual actuator is performed in such a way that the convergence of the state trajectory to zero is assured despite the saturations and the appearance of faults. Also, it is shown that it is possible to obtain some guarantees about the tolerated delay between the fault occurrence and its isolation, and that the nominal controller can be designed so as to maximize the tolerated delay.i Resum Aquesta tesi presenta diverses contribucions a l'estat de l'art del control per planificació del guany i del control tolerant a fallades (FTC).Pel que fa al control per planificació del guany, s'analitzen les connexions entre els paradigmes dels sistemes lineals a paràmetres variants en el temps (LPV) i d...

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Section: Gain-scheduling: Lpv Systems and Ts Systemsmentioning

confidence: 99%

Advances in Gain-Scheduling and Fault Tolerant Control Techniques

Rotondo¹

2018

Springer Theses

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“…The stability analysis and design of fuzzy control systems based on the T-S model are discussed in [10,23]. And the robustness in fuzzy model-based control is studied [9,11,25]. Among the reported results [7,[9][10][11]23,25], most of the stability analysis and design methods are based on T-S fuzzy model, yet the e ect of approximation error due to fuzzy modeling is not considered.…”

Section: Introductionmentioning

confidence: 99%

“…The Takagi-Sugeno fuzzy models have been proven to be very useful to model nonlinear system [22]. Some signiÿcant research e orts have been done for fuzzy controllers based on T-S model to guarantee the control performance and system stability [4,7,9,10,15,23,26]. The stability analysis and design of fuzzy control systems based on the T-S model are discussed in [10,23].…”

Section: Introductionmentioning

confidence: 99%

Robust H∞ control of multiple time-delay uncertain nonlinear system using fuzzy model and adaptive neural network

Shou-song

Liu

2004

Fuzzy Sets and Systems

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“…Takagi-Sugeno Fuzzy models (TS) [14] have been applied successfully in non-linear model based techniques [15]. These models may be formulated as an Adaptive Neuro-Fuzzy Inference System (ANFIS) [16].…”

Section: The Sterilization Of Solid Food Plantmentioning

confidence: 99%

Neurofuzzy model based predictive control for thermal batch processes

Escaño

Bordons

Vilas

et al. 2009

Journal of Process Control

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In many cases, it is difficult to derive a precise mathematical model, based on first principles, for a given process. Besides, the computation of the solution of models obtained through this methology may require a large computational effort making them useless for real time tasks like control or optimization. Neurofuzzy modelling, which permits an easy way to derive successful models, is a good alternative which can be employed to overcome such limitations. In this paper, together with the neurofuzzy modelling, several strategies based on non-linear predictive control are presented. The low computational cost associated with neurofuzzy models and controllers makes them suitable candidates to be implemented into industrial Programmable Logic Controllers (PLC). Both the model and controllers are validated and implemented in a pilot plant for the thermal sterilization of solid canned food in steam retorts and based on the results, a comparison between the different predictive control strategies is presented.

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Fuzzy model-based control of complex plants

Cited by 107 publications

References 21 publications

Advances in Gain-Scheduling and Fault Tolerant Control Techniques

Advances in Gain-Scheduling and Fault Tolerant Control Techniques

Robust H∞ control of multiple time-delay uncertain nonlinear system using fuzzy model and adaptive neural network

Neurofuzzy model based predictive control for thermal batch processes

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