No abstract

This paper presents a new method to design nonlinear feedback linearization controller for polymer electrolyte membrane fuel cells (PEMFCs). A nonlinear controller is designed based on nonlinear model to prolong the stack life of PEM fuel cells. Since it is known that large deviations between hydrogen and oxygen partial pressures can cause severe membrane damage in the fuel cell, feedback linearization is applied to the PEM fuel cell system, thus the deviation can be kept as small as possible during disturbances or load variations. To obtain an accurate feedback linearization controller, tuning the linear parameters are always important. So in proposed study NSGA_II method was used to tune the designed controller in aim to decrease the controller tracking error. The simulation result showed that the proposed method tuned the controller efficiently.

No abstract

In this paper a fuzzy-quaternion controller is designed for attitude control of a satellite, then the fuzzy memberships are tuned in an intelligent way by using particle swarm optimization (PSO) algorithm. Due to the satellite nonlinear behavior, classic methodologies cannot control satellite. The simulation result show that the designed controller can accurately control the satellite attitude in severe maneuvers. To evaluate the controller robustness in presence of uncertainties, 20 percent uncertainties were considered in inertias of momentum through the simulations. The simulation results show that the optimized fuzzy logic controller (OFLC) can control the satellite in large maneuvers in desirable time. In addition, the simulation results demonstrated that the proposed design is robust against uncertainties and have quite better performance than quaternion proportional-derivative (PD) controller in satellite motion control. Nomenclature AE= Direction cosine error matrix α = Angle between primary Euler vector and its latter (angle error) ANFIS = Adaptive network based fuzzy inference system CoA = Center of area e = Euler axis FLC = Fuzzy logic controller Kdi = Derivative control gain Kpi = Proportional control gain MFs = Membership functions PD = Proportional-Derivative control PSO = Particle swarm optimization Ti = Torque θ = Principal rotation angle

Bird's formation flight is one of the best types of cooperation in nature. The bird's flight was the motivation of humans for flying. After one century of flight development, bird's formation flight was the motivation of humans for aircraft's formation flight. The closeness of aircrafts in formation flight and the effect of disturbances such as vortex make the formation flight control a challenging issue for control designers. This paper introduces a novel integration between guidance commands and system controller inputs. In recent papers the control system inputs were derived from approximate equations, and this approximation caused maneuver limitation. To tackle this problem, a new method is introduced, which employs proportional-integral-derivative (PID) controller in the integration block. This integrated guidance and control system employs the pure pursuit guidance to determine the unmanned aerial vehicle's acceleration command. A two-loop dynamic inversion technique is used for designing attitude and velocity controller, while the acceleration feedback control is used between the guidance system and attitude controller, which leads to increase in maneuverability of unmanned aerial vehicle's formation flight. The simulation results show that the proposed method can control the UAV's formation with sufficient accuracy in severe maneuvers.