Information Theoretic Model Predictive Control on Jump Diffusion Processes
Information Theoretic Model Predictive Control on Jump Diffusion Processes
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DOI：
10.23919/acc.2019.8815263
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Ziyi Wang;Grady Williams;Evangelos A. Theodorou
中科院分区：
文献类型：
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作者：
Ziyi Wang;Grady Williams;Evangelos A. Theodorou
本文提出了具有复合泊松噪声系统的随机最优控制问题的信息论方法。我们将信息论路径积分控制的工作推广到具有复合泊松噪声的不连续动力学。我们还利用随机优化方法推导出了相同形式的控制更新律。提出了一种基于采样的迭代模型预测控制算法。该算法是可并行化的，当在图形处理器(GPU)上实现时，可以实时运行。通过对两种控制任务的仿真实验，验证了所提算法的性能。我们的仿真结果表明，新方案的性能得到了改善，并表明了在计算随机最优控制策略时考虑随机扰动的统计特性的重要性。
In this paper we present an information theoretic approach to stochastic optimal control problems for systems with compound Poisson noise. We generalize previous work on information theoretic path integral control to discontinuous dynamics with compound Poisson noise. We also derive a control update law of the same form using a stochastic optimization approach. We develop a sampling-based iterative model predictive control (MPC) algorithm. The proposed algorithm is parallelizable and when implemented on a Graphical Processing Unit (GPU) can run in real time. We test the performance of the proposed algorithm in simulation for two control tasks using a cartpole and a quadrotor system. Our simulations demonstrate improved performance of the new scheme and indicate the importance of incorporating the statistical characteristics of stochastic disturbances in the computation of the stochastic optimal control policies.