A course in fuzzy systems and control
A course in fuzzy systems and control
Practical genetic algorithms
Design of a GA-based fuzzy PID controller for non-minimum phase systems
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-objective Evolutionary Design of Fuzzy Autopilot Controller
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Design and Implementation of Fuzzy Sliding-Mode Controller for a Wedge Balancing System
Journal of Intelligent and Robotic Systems
Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers
Engineering Applications of Artificial Intelligence
Adaptive Fuzzy Sliding Mode Controller for the Kinematic Variables of an Underwater Vehicle
Journal of Intelligent and Robotic Systems
Robust Fuzzy Output Sliding Control without the Requirement of State Measurement
Journal of Intelligent and Robotic Systems
Multiobjective evolution based fuzzy PI controller design for nonlinear systems
Engineering Applications of Artificial Intelligence
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The fuzzy sliding mode control based on the multi-objective genetic algorithm is proposed to design the altitude autopilot of a UAV. This case presents an interesting challenge due to non-minimum phase characteristic, nonlinearities and uncertainties of the altitude to elevator relation. The response of this autopilot is investigated through various criteria such as time response characteristics, robustness with respect to parametric uncertainties, and robustness with respect to unmodeled dynamics. The parametric robustness is investigated with reduction in significant longitudinal stability coefficients. Also, a nonlinear model in presence of the coupling terms is used to investigate the robustness with respect to unmodeled dynamics. In spite of a designed classic autopilot, it is shown by simulation that combining of the sliding mode control robustness and the fuzzy logic control independence of system model can guarantee the acceptable robust performance and stability with respect to unmodeled dynamics and parametric uncertainty, while the number of FSMC rules is smaller than that for the conventional fuzzy logic control.