Download Hierarchical Type-2 Fuzzy Aggregation of Fuzzy Controllers by Leticia Cervantes, Oscar Castillo PDF
By Leticia Cervantes, Oscar Castillo
This e-book makes a speciality of the fields of fuzzy common sense, granular computing and in addition contemplating the keep watch over region. those components can interact to resolve numerous keep watch over difficulties, the assumption is this blend of parts may permit much more advanced challenge fixing and higher effects. during this ebook we attempt the proposed approach utilizing benchmark difficulties: the complete flight keep an eye on and the matter of water point regulate for a three tank approach. whilst fuzzy good judgment is used it make it effortless to played the simulations, those fuzzy platforms aid to version the habit of a true platforms, utilizing the bushy structures fuzzy ideas are generated and with this may generate the habit of any variable counting on the inputs and linguistic worth. accordingly this paintings considers the proposed structure utilizing fuzzy structures and with this enhance the habit of the advanced keep an eye on problems.
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Extra resources for Hierarchical Type-2 Fuzzy Aggregation of Fuzzy Controllers
4. The error of each table is calculated using Eq. 1 but with different types of membership function. 5, in this table we illustrate 30 evolutions for each valve using Eq. 1 this is to observe the behavior of each valve. 6. 7. 218 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 The last tables show the behavior of each valve using the proposed method and using the optimization, and then we decided to test the aggregator using a type-2 fuzzy system and the optimization to observe the behavior and compared the results.
32 Internal structure of the pilot control control. In this work the block of the pilot was changed for the fuzzy systems and a part of the PID to achieve the control. The internal structure of the pilot control of the simulation plant is shown in Fig. 32. Having the simulation plant and the fuzzy system, the simulation was performed using triangular membership functions in all the inputs and the outputs of the fuzzy system. 1 Simulation Results in the Second Case of Study Using F-16 Airplane First the simulation was performed with a 20,000 ft value for the altitude, 650 ft/s in velocity, 10 deg for the elevator disturbance deflection, in aileron disturbance deflection of 10 deg and in rudder disturbance deflection of 10 deg.
Previously, type-1 fuzzy systems were used and results were obtained, these outputs are used as new inputs to the granular type-1 fuzzy system, this is to obtain new outputs and to have improved results with this method. The granular type-1 fuzzy system is shown in Fig. 12. In the aggregator above to obtain the rules we used a genetic algorithm using a Michigan approach because it is the classical approach, having proven itself and undergone more development and we wanted that each rule would represent by a gen, and 10 genes are considered to optimize the fuzzy rules, all the genes have 3 values (1, 2 and 3), for example gen 1 has values between 1 and 3 (1 is open, 2 medium and 3 is closed), for the ﬁrst input.