Sampling-based model-predictive controllers have become a powerful optimization tool for planning and control problems in various challenging environments. In this paper, we show how the default choice of uncorrelated Gaussian distributions can be improved upon with the use of a colored noise distribution. Our choice of distribution allows for the emphasis on low frequency control signals, which can result in smoother and more exploratory samples. We use this frequency-based sampling distribution with Model Predictive Path Integral (MPPI) in both hardware and simulation experiments to show better or equal performance on systems with various speeds of input response.

翻译：基于采样的模型预测控制器已成为各种复杂环境中规划与控制问题的强大优化工具。本文证明，通过使用有色噪声分布，可以改进默认的不相关高斯分布选择。我们选择的分布能够强调低频控制信号，从而生成更平滑且更具探索性的样本。我们将这种基于频率的采样分布应用于模型预测路径积分（MPPI）方法，在硬件与仿真实验中均表明，对于具有不同输入响应速度的系统，该方法能实现更优或相等的性能。