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Satellite attitude dynamics, nonlinear systems with high dimension and are nonlinear and chaotic. In this paper, attitude control and synchronization two identical chaotic satellite with different initial conditions based on the control design is proposed. Using the Lyapunov theory stability controller has been demonstrated. Finally, according to the simulation results, the synchronization is complete, the control signal is low that changes are the ability to build and implement.

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Purpose This paper aims to describe a novel type of attitude control system (ACS) in different configurations. This servomechanism is compared with control moment gyro (CMG) in significant parameters of performance for ACS of rigid satellite. Design/methodology/approach This new actuator is the fluid containing one or more rings and fluid flow is supplied by pump. The required torque control is obtained by managing fluid angular velocity. The cube-shaped satellite with three rings of fluid in the principle axes is considered for modeling. The satellite is considered rigid and nonlinear dynamics equation is used for it. In addition, the failure of the pyramid-shaped satellite with an additional ring fluid is discussed. Findings The controller model for four fluid rings has more complexity than for three fluid rings. The simulation results illustrated that four fluid rings need less energy for stabilization than three fluid rings. The performance of this type of actuator is compared with CMG. At last, it is demonstrated that performance parameters are improved with fluid ring actuator. Research limitations/implications Fluid ring actuator can be affected by environmental pressure and temperature. Therefore, freezing and boiling temperature of the fluid should be considered in system designation. Practical implications Fluid ring servomechanism can be used as ACS in rigid satellites. This actuator is compared by CMG, the prevalent actuator. It has less displacement attitude maneuver. Originality/value The results provide the feasibility and advantages of using fluid rings as satellite ACS. The quaternion error controller is used for this model to enhance its performance.

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

A genetic algorithm is used together with calculus of variations to optimize an interplanetary trajectory for the Bryson-Ho Earth-to-Mars orbit transfer problem. The global search properties of genetic algorithm combine with the local search capabilities of calculus of variations to produce solutions that are superior to those generated with the calculus of variations alone, and these solutions require less user interaction than previously possible. The genetic algorithm is not hampered by ill-behaved gradients and is relatively insensitive to problems with a small radius of convergence, allowing it to optimize trajectories for which solutions had not yet been obtained. The use of the calculus of variations within the genetic algorithm optimization routine increased the precision of the final solution to levels uncommon for a genetic algorithm.

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