Robust Sampling Based Model Predictive Control with Sparse Objective Information
Robust Sampling Based Model Predictive Control with Sparse Objective Information
复制标题
具有稀疏目标信息的基于鲁棒采样的模型预测控制
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Evangelos A. Theodorou
中科院分区：
文献类型：
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作者：
Grady Williams;Brian Goldfain;P. Drews;Kamil Saigol;James M. Rehg;Evangelos A. Theodorou
- 我们提出了一个随机模型预测控制的算法框架，能够优化具有稀疏，不连续梯度信息的成本函数的非线性系统。所提出的框架结合了基于采样的模型预测控制与基于线性化的轨迹优化方法的贝内。由此产生的算法包括一个新的利用基于管的模型预测控制。我们在各种模拟任务上展示了强大的算法性能
—We present an algorithmic framework for stochastic model predictive control that is able to optimize non-linear systems with cost functions that have sparse, discontinuous gradient information. The proposed framework combines the benefits of sampling-based model predictive control with linearization-based trajectory optimization methods. The resulting algorithm consists of a novel utilization of Tube-based model predictive control. We demonstrate robust algorithmic performance on a variety of simulated tasks