Polytopic invariant and contractive sets for closed-loop discrete fuzzy systems

In this work a procedure for obtaining polytopic lambda-contractive sets for Takagi Sugeno fuzzy systems is presented, adapting well-known algorithms from literature on discrete-time linear difference inclusions (LDI) to multi-dimensional summations. As a complexity parameter increases, these sets tend to the maximal invariant set of the system when no information on the shape of the membership functions is available. lambda-contractive sets are naturally associated to level sets of polyhedral Lyapunov functions proving a decay-rate of lambda. The paper proves that the proposed algorithm obtains better results than a class of Lyapunov methods for the same complexity degree: if such a Lyapunov function exists, the proposed algorithm converges in a finite number of steps and proves a larger lambda-contractive set.This work has been supported by Projects DPI2011-27845-C02-01 and DPI2011-27845-C02-02, both from Spanish Government.Arino, C.; Perez, E.; Sala Piqueras, A.; Bedate, F. (2014). Polytopic invariant and contractive sets for closed-loop discrete fuzzy systems. Journal of The Franklin Institute. 351(7):3559-3576. https://doi.org/10.1016/j.jfranklin.2014.03.014S35593576351

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120918~20120922[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Japan,Toky

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[conferencetype]]國際[[conferencedate]]20120918~20120921[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Dynamical Modeling and Control of Multiphase Heat Transport Systems Based on Loop Heat Pipes

Effective heat transport systems in aerospace are based on multiphase loop heat pipes (LHPs). For a precise thermal control of the electronics, electrical heaters are additionally used to control the operating temperature of the LHP. This work focusses on the dynamical modeling and model-based control design for LHP-based heat transport systems. The results of this work can be used for the optimization of current control parameters and the efficient control design for future LHP applications

Dynamical Modeling and Control of Multiphase Heat Transport Systems Based on Loop Heat Pipes

Effective heat transport systems in aerospace are based on multiphase loop heat pipes (LHPs). For a precise thermal control of the electronics, electrical heaters are additionally used to control the operating temperature of the LHP. This work focusses on the dynamical modeling and model-based control design for LHP-based heat transport systems. The results of this work can be used for the optimization of current control parameters and the efficient control design for future LHP applications

Dynamical Modeling and Control of Multiphase Heat Transport Systems Based on Loop Heat Pipes

Effective heat transport systems in aerospace are based on multiphase loop heat pipes (LHPs). For a precise thermal control of the electronics, electrical heaters are additionally used to control the operating temperature of the LHP. This work focusses on the dynamical modeling and model-based control design for LHP-based heat transport systems. The results of this work can be used for the optimization of current control parameters and the efficient control design for future LHP applications

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields