Improvement of Control Performance of Sampling Based Model Predictive Control using GPU
Improvement of Control Performance of Sampling Based Model Predictive Control using GPU
复制标题
利用GPU提高基于采样的模型预测控制的控制性能
DOI：
10.1109/ivs.2019.8813807
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Suzuki Tatsuya
中科院分区：
文献类型：
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作者：
Muraleedharan Arun;Okuda Hiroyuki;Suzuki Tatsuya
本文提出了应用图形处理器（GPU）来改善基于采样的预测控制算法的控制性能。作为一个示例问题，将街道上停放汽车的避障情况建模为非线性模型预测控制问题。汽车动力学和非线性约束被认为是实现碰撞避免。考虑到非线性约束，控制输入必须在每个控制步骤中实时优化。基于采样的方法被用来解决这个问题，这种方法的主要限制之一是所涉及的计算成本。在本文中，基于采样的优化算法是适应利用GPU的并行计算能力，使用CUDA。所产生的输入序列和计算速度进行了比较与基于CPU的程序为同一情况下。所提出的方法是在汽车动力学模拟器的仿真实验中实现的，以验证其性能的路径跟踪。最后，还讨论了样本大小与GPU加速计算速度之间的一般关系。
This paper presents the application of Graphics Processing Unit (GPU) to improve the control performance of sampling based predictive control algorithms. As an example problem, obstacle avoidance situation with parked cars in a street is modeled as a non-linear model predictive control problem. Car dynamics and non-linear constraints are considered to achieve collision avoidance. The control input must be optimized in every control step in real-time considering the non-linear constraints. Sampling based approach is used to solve this problem and one of the major limitations to this approach is the computational cost involved. In this paper, the sampling-based optimization algorithm was adapted to utilize the parallel computing capabilities of GPU using CUDA. The generated input sequence and the computational speeds were compared with a CPU based program for the same case. The proposed method is implemented in a simulation experiment with car dynamics simulator to verify its performance in terms of path tracking. Finally, a general relationship between sample size and GPU acceleration of its calculation speed is also discussed.