Parallel Algorithms for Large Scale Optimal Control Programs

目前，还没有并行算法可以解决 最优控制问题的计算机上有效地与一个大的 处理器数量。 本研究将发展两个平行的 算法来解决大规模的最优控制问题， 预期对于大量处理器是高效的。 的 第一种算法，称为混合算法，是以下算法的组合： 微分动态规划（DDP）和一个阶段式 牛顿的方法，两者都是串行的。 的混合并联 算法主要针对无约束最优控制问题设计 问题 第二种算法称为SQP型算法， 通过利用最优控制的特殊特性导出 序列二次规划（SQP）的沿着问题 approach. SQP型算法适用于 无约束和有约束最优控制问题。 无论是 算法，每个处理器被分配来解决一个优化问题。 在一组时间段内的问题。 将制定守则 对于具有中等粒度并行能力的机器（例如，的 Intel iPSC/860和Kendall Square计算机。 典型的 非线性柔性结构的工程应用 将使用10，000个时间段的问题来评估 提出的算法。

Currently, there is no parallel algorithm that can solve optimal control problems efficiently on computers with a large number of processors. This research will develop two parallel algorithms to solve large scale optimal control problems that are expected to be efficient with a large number of processors. The first algorithm, called the Hybrid algorithm, is a combination of the Differential Dynamic Programming (DDP) and a stagewise Newton's method, both of which are serial. The Hybrid parallel algorithm is designed primarily for unconstrained optimal control problems. The second algorithm, called an SQP-type algorithm, is derived by utilizing special features of the optimal control problem along with a Sequential Quadratic Programming (SQP) approach. The SQP-type algorithm is suitable for both the unconstrained and constrained optimal control problem. In both algorithms, each processor is assigned to solve an optimization problem over a group of time periods. Codes will be developed for machines with medium-grain parallel capabilities e.g., the Intel iPSC/860 and Kendall Square computers. A typical engineering application involving a nonlinear flexible structural problem with 10,000 time periods will be used to evaluate the proposed algorithms.