Adaptive fuzzy controller for nonlinear systems via genetic algorithm
AEE'08 Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering
GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems
Expert Systems with Applications: An International Journal
Designing fair flow fuzzy controller using genetic algorithm for computer networks
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Robust H∞ fuzzy control of dithered chaotic systems
Neurocomputing
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Based on the concept of sliding-mode control (SMC), the paper designs fuzzy logic controls to achieve the prespecified trajectory tracking for an uncertain nonlinear system. The prespecified trajectory is composed of several nonisoclinal segments in the phase plane and is regarded as the piecewise sliding surface. First, let the uncertain system be approximated by a linguistic fuzzy rule base, then two fuzzy logic controllers are designed to achieve the hitting motion and preserve the system's state traveling on the prespecified trajectory. The main advantage of this control design is that the trial and error of the conventional fuzzy control design disappears. A practical example is given to illustrate the applicability of the algorithm