Robustness analysis of attitude and orbit control systems for flexible satellites

In this study, an optimisation-based approach is proposed for the robustness analysis of an attitude and orbit control system (AOCS) for flexible satellites. Several optimisation methods, including local gradient-based algorithms, global evolutionary algorithms and hybrid local/global algorithms are applied to the problem of analysing the robustness of a full-authority multivariable controller with respect to several frequency and time domain performance criteria, for a 6 degree of freedom simulation model of a satellite with large sun shields. The results of our study reveal the advantages of optimisation-based worst-case analysis over traditional Monte Carlo simulations for systems with flexible dynamics. In particular, it is shown that frequency domain analysis can provide useful guidance for the formulation of subsequent time domain tests, and that hybrid local/global optimisation algorithms can produce more reliable estimates of worst-case performance, while also reducing the associated computational overheads. The proposed approach appears to have significant potential for improving the industrial flight clearance process for next-generation high-performance satellite control systems.

[1]  Samir Bennani,et al.  Nondiagonal MIMO QFT Controller Design for Darwin-Type Spacecraft With Large Flimsy Appendages , 2008 .

[2]  Behrang Mansoornejad,et al.  A hybrid GA-SQP optimization technique for determination of kinetic parameters of hydrogenation reactions , 2008, Comput. Chem. Eng..

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Thomas F. Coleman,et al.  Optimization Toolbox User's Guide , 1998 .

[5]  Ian Postlethwaite,et al.  Robustness analysis of a reusable launch vehicle flight control law , 2009 .

[6]  Sandrine Le Ballois,et al.  Low-order robust attitude control of an earth observation satellite , 1999 .

[7]  Peggy S. Williams A Monte Carlo Dispersion Analysis of the X-33 Simulation Software , 2001 .

[8]  Ian Postlethwaite,et al.  Nonlinear robustness analysis of flight control laws for highly augmented aircraft , 2007 .

[9]  John Yen,et al.  A hybrid approach to modeling metabolic systems using genetic algorithm and simplex method , 1995, Proceedings the 11th Conference on Artificial Intelligence for Applications.

[10]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[11]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Jongrae Kim,et al.  Clearance of Nonlinear Flight Control Laws Using Hybrid Evolutionary Optimization , 2006, IEEE Transactions on Evolutionary Computation.