Receding Horizon Differential Dynamic Programming Under Parametric Uncertainty
Receding Horizon Differential Dynamic Programming Under Parametric Uncertainty
复制标题
参数不确定性下的后退时域微分动态规划
DOI：
10.1109/cdc45484.2021.9683370
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Evangelos A. Theodorou
中科院分区：
文献类型：
--
作者：
Yuichiro Aoyama;A. Saravanos;Evangelos A. Theodorou
广义多项式混沌(GPC)理论由于具有传播不确定性演化的能力，已被广泛用于表示系统中的参数不确定性。在最优控制环境中，广义预测控制可以与几种优化技术相结合，以实现有效地处理这种类型的不确定性的控制策略。这种合适的方法是差分动态规划(DDP)，导致了一种继承了后者对高维系统的可伸缩性和快速收敛特性的算法。在本文中，我们扩展了这一组合，目的是获得满足非线性约束的概率保证。特别是，我们利用GPC的能力来表达不确定分布的高阶矩--没有任何高斯假设--并加入了导致涉及状态协方差的表达式的机会约束。此外，我们证明了通过以滚动时间的方式实现我们的算法，我们能够计算出有效地减少轨迹上的不确定性累积的控制策略。通过在差动轮式机器人和四旋翼机器人上执行避障任务的仿真结果，验证了该方法的适用性。
Generalized Polynomial Chaos (gPC) theory has been widely used for representing parametric uncertainty in a system, thanks to its ability to propagate uncertainty evolution. In an optimal control context, gPC can be combined with several optimization techniques to achieve a control policy that handles effectively this type of uncertainty. Such a suitable method is Differential Dynamic Programming (DDP), leading to an algorithm that inherits the scalability to high-dimensional systems and fast convergence nature of the latter. In this paper, we expand this combination aiming to acquire probabilistic guarantees on the satisfaction of nonlinear constraints. In particular, we exploit the ability of gPC to express higher order moments of the uncertainty distribution - without any Gaussianity assumption - and we incorporate chance constraints that lead to expressions involving the state covariance. Furthermore, we demonstrate that by implementing our algorithm in a receding horizon fashion, we are able to compute control policies that effectively reduce the accumulation of uncertainty on the trajectory. The applicability of our method is verified through simulation results on a differential wheeled robot and a quadrotor that perform obstacle avoidance tasks.
DOI：
10.5555/1756006.1953033
发表时间：
2010-03
期刊：
J. Mach. Learn. Res.
影响因子：
--
作者：
Evangelos A. Theodorou;J. Buchli;S. Schaal
通讯作者：
Evangelos A. Theodorou;J. Buchli;S. Schaal