Optimization of control parameters based on genetic algorithms for spacecraft attitude tracking with input constraints

This paper studies the attitude tracking control problem for rigid spacecraft with input constraint, parameter uncertainties and harmonic disturbances. By combining a nonlinear proportional-derivative (PD) control, a novel adaptive control strategy whose parameters are optimized by a genetic algorithm with hyperbolic tangent function is proposed to guarantee the globally asymptotic convergence of the attitude tracking process. In particular, the nonlinear proportional-derivative (PD) control term is utilized to achieve better tracking performance with shorter settling time and smaller/no overshoot. By considering the strong connection between the complex controller parameters and the tracking performances, the genetic algorithms are adopted to determine the optimal set of controller parameters and optimal value of time acted as fitness goals of the algorithm. Finally, the effectiveness of the proposed control method is shown by the numerical simulations.