Design of a Fuzzy Controller Using a Genetic Algorithm for Stator Flux Estimation
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
A multi-crossover genetic approach to multivariable PID controllers tuning
Expert Systems with Applications: An International Journal
Design and analysis of direct-action CMAC PID controller
Neurocomputing
Fuzzy logic controller design methodology for Cartesian robot control
International Journal of Computer Applications in Technology
A rule base modification scheme in fuzzy controllers for time-delay systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An error-based on-line rule weight adjustment method for fuzzy PID controllers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Automatica (Journal of IFAC)
Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration
Engineering Applications of Artificial Intelligence
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Describes a methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and, genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning parameters. The nonlinear proportional gain is explicitly derived in the error domain. A conservative design strategy is proposed for realizing a guaranteed-PID-performance (GPP) fuzzy controller. This strategy suggests that a fuzzy PID controller should be able to produce a linear function from its nonlinearity tuning of the system. The proposed PID system is able to produce a close approximation of a linear function for approximating the GPP system. This GPP system, incorporated with a genetic solver for the optimization, will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria. Two indexes, linearity approximation index (LAI) and nonlinearity variation index (NVI), are suggested for evaluating the nonlinear design of fuzzy controllers. The proposed control system has been applied to several first-order, second-order, and fifth-order processes. Simulation results show that the proposed fuzzy PID controller produces superior control performance to the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation