论文标题
最佳控制的积分惩罚直接转录方法
An Integral Penalty-Barrier Direct Transcription Method for Optimal Control
论文作者
论文摘要
一些直接转录方法可能无法收敛,例如当有奇异的弧线时。最近,我们引入了一种用于最佳控制问题的收敛直接转录方法,称为罚款栏有限元法(PBF)。 PBF在问题实例上非常弱的假设下收敛。 PBF可以完全避免完全避免置与套在一起,从而避免了搭配点之间的响声。取而代之的是,通过整体二次惩罚项将平等路径约束残差迫使无处不在。我们重点介绍了搭配 - 和惩罚型直接转录方法之间的概念差异。对两种类型的方法的理论收敛结果进行了审查和比较。介绍了用于实施PBF的公式,并提供有关非线性程序(NLP),稀疏性和解决方案的公式的详细信息。数值实验将PBF与鲁棒性,准确性,稀疏性和计算成本进行比较。我们表明,NLP功能的计算成本,稀疏性和构建与相同程度和网格的正交搭配方法大致相同。作为优势,在搭配方法失败的情况下,PBF会收敛。 PBF还允许人们互相交易计算成本,最佳性和违反差异和其他等价方程式。
Some direct transcription methods can fail to converge, e.g. when there are singular arcs. We recently introduced a convergent direct transcription method for optimal control problems, called the penalty-barrier finite element method (PBF). PBF converges under very weak assumptions on the problem instance. PBF avoids the ringing between collocation points, for example, by avoiding collocation entirely. Instead, equality path constraint residuals are forced to zero everywhere by an integral quadratic penalty term. We highlight conceptual differences between collocation- and penalty-type direct transcription methods. Theoretical convergence results for both types of methods are reviewed and compared. Formulas for implementing PBF are presented, with details on the formulation as a nonlinear program (NLP), sparsity and solution. Numerical experiments compare PBF against several collocation methods with regard to robustness, accuracy, sparsity and computational cost. We show that the computational cost, sparsity and construction of the NLP functions are roughly the same as for orthogonal collocation methods of the same degree and mesh. As an advantage, PBF converges in cases where collocation methods fail. PBF also allows one to trade off computational cost, optimality and violation of differential and other equality equations against each other.