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基于微分包含的绳系卫星时间最优释放控制

文浩 金栋平 胡海岩

文浩 金栋平 胡海岩. 基于微分包含的绳系卫星时间最优释放控制[J]. 力学学报, 2008, 40(1): 135-140. DOI: 10.6052/0459-1879-2008-1-2007-063
引用本文: 文浩 金栋平 胡海岩. 基于微分包含的绳系卫星时间最优释放控制[J]. 力学学报, 2008, 40(1): 135-140. DOI: 10.6052/0459-1879-2008-1-2007-063
Hao Wen, Dongping Jin, Haiyan Hu. Time-optimal deployment of a tethered subsatellite based on differential inclusion[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(1): 135-140. DOI: 10.6052/0459-1879-2008-1-2007-063
Citation: Hao Wen, Dongping Jin, Haiyan Hu. Time-optimal deployment of a tethered subsatellite based on differential inclusion[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(1): 135-140. DOI: 10.6052/0459-1879-2008-1-2007-063
文浩 金栋平 胡海岩. 基于微分包含的绳系卫星时间最优释放控制[J]. 力学学报, 2008, 40(1): 135-140. CSTR: 32045.14.0459-1879-2008-1-2007-063
引用本文: 文浩 金栋平 胡海岩. 基于微分包含的绳系卫星时间最优释放控制[J]. 力学学报, 2008, 40(1): 135-140. CSTR: 32045.14.0459-1879-2008-1-2007-063
Hao Wen, Dongping Jin, Haiyan Hu. Time-optimal deployment of a tethered subsatellite based on differential inclusion[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(1): 135-140. CSTR: 32045.14.0459-1879-2008-1-2007-063
Citation: Hao Wen, Dongping Jin, Haiyan Hu. Time-optimal deployment of a tethered subsatellite based on differential inclusion[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(1): 135-140. CSTR: 32045.14.0459-1879-2008-1-2007-063

基于微分包含的绳系卫星时间最优释放控制

Time-optimal deployment of a tethered subsatellite based on differential inclusion

  • 摘要: 考虑系绳弹性的影响,建立了绳系卫星系统三维动力学模型,研究了在状态和控制约束下的绳系卫星非线性时间最优控制问题. 为缩减系统变量,控制律设计没有采用通常的状态空间模型,而是基于二阶微分包含,将连续时间最优控制问题离散为大规模动态规化问题,最后通过数值模拟验证了该方法的有效性.
    Abstract: This paper presents the nonlinear time-optimal controlfor the three-dimensional deployment process of a tethered subsatellitemodel, which also takes tether elasticity and the mission-relatedstate-control constraints into consideration. Instead of the commonly usedstate-space model, a second-order differential inclusion formulation isexploited in this work to achieve a significant reduction of the number ofsystem variables. The optimal control is solved by discretizing the optimalcontrol problem based on a direct algorithm, and numerically solving theresulting large-scale optimization problem via an NLP (nonlinearprogramming) solver. The case studies in the paper well demonstrate theperformance of the proposed strategy.
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出版历程
  • 收稿日期:  2007-01-28
  • 修回日期:  2007-09-19
  • 刊出日期:  2008-01-17

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