Hybrid EDA-based optimal attitude control for a spacecraft in a class of control task

ABSTRACT

In the practical situation, if failure of one of the actuators occurs, there exists the attitude control task of a rigid spacecraft using only two control torques supplied by momentum wheel actuators. Here, this class of control task for a rigid spacecraft is discussed. This nonlinear control problem can be converted to the nonholonomic motion planning optimization problem of a drift-free system. In order to improve the search efficiency of current optimization algorithms, the hybrid estimation of distribution algorithm (EDA) is presented by combing the idea of differential evolution strategy (DES). Then, the optimal attitude control task for the spacecraft using two momentum wheel actuators is achieved. By comparing the proposed algorithm with existing genetic algorithm and evolutionary programming, the simulation results show the accuracy and efficiency of hybrid EDA.

REFERENCES

• 1: Crouch, P. E. 1984. Spacecraft attitude control and stabilization: application of geometric control theory to rigid body models. IEEE Transactions on Automatic Control, 29, 4(Aug. 1984), 87--95.
• 2: Krishnan, H., McClamroch, N. H., and Reyhanoglu, M. 1995. Attitude stabilization of a rigid spacecraft using two momentum wheel actuators. Journal of Guidance, Control and Dynamics, 18, 2(Apr. 1995), 256--263.
• 3: Ge, X. S., Chen, L. Q., and Liu, Y. A. 2004. Nonholonomic motion planning for the attitude of rigid spacecraft with two momentum wheel actuators. Control Theory and Applications, 21, 5(Oct. 2004), 781--784.
• 4: Ge, X. S. and Chen, L. Q. 2004. Attitude control of a rigid spacecraft with two momentum wheel actuators using genetic algorithm. Acta Astronautica, 55, 1(Jan. 2004), 3--8.
• 5: Luo, X. and Sun, Z. Q. 2006. Optimal attitude control for a spacecraft using two momentum wheel actuators based on intensified evolutionary programming. Dynamics of Continuous, Discrete and Impulsive Systems, Series A: Mathematical Analysis, 13, Suppl. S(Dec. 2006), 733--737.
• 6: Pedro Larraanaga , Jose A. Lozano, Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Kluwer Academic Publishers, Norwell, MA, 2001
• 7: Kenneth Price , Rainer M. Storn , Jouni A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series), Springer-Verlag New York, Inc., Secaucus, NJ, 2005
• 8: Cho, D. Y. and Zhang, B. T. 2004. Evolutionary continuous optimization by distribution estimation with variational Bayesian independent component analyzers mixture model. Lecture Notes in Computer Science, 3242(Apr. 2004), 212--221.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Control theory

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

General Terms:
Algorithms, Design

Keywords:
control, differential evolution, estimation of distribution, optimization, spacecraft