高自由度机器人参数化GPU化实时模型预测控制
Parameterized and GPU-Parallelized Real-Time Model Predictive Control for High Degree of Freedom Robots
摘要：
提出了一种新的输入参数化方法，提高了高自由度（DoF）机器人的模型预测控制（MPC）的易处理性。实验结果表明，通过参数化的输入轨迹的四分之三以上的优化变量，在传统的MPC中使用的可以被删除，几乎没有对系统性能的影响。这种参数化还导致轨迹更保守，在具有建模误差的欠阻尼系统中产生更少的超调。在本文中，我们提出了两个MPC的解决方案，利用这种参数化的方法。第一个使用凸解算器，第二个使用图形处理单元（GPU）上的并行计算。我们表明，这两种方法大大减少了解决时间大自由度，长时间的MPC问题，允许解决方案的实时速率。通过仿真和硬件实验，我们表明，参数化的凸求解器MPC具有更快的解决时间比传统的MPC高自由度的情况下，同时仍然达到类似的性能。对于基于GPU的MPC求解方法，我们使用了一种进化算法，我们称之为进化MPC（EMPC）。EMPC被证明有更快的解决时间为高自由度系统。通过使用更强大的GPU，EMPC的求解时间进一步减少。这表明并行MPC方法将随着GPU技术的改进和普及而变得更加有利。
英文摘要：
This work presents and evaluates a novel input parameterization method which improves the tractability of model predictive control (MPC) for high degree of freedom (DoF) robots. Experimental results demonstrate that by parameterizing the input trajectory more than three quarters of the optimization variables used in traditional MPC can be eliminated with practically no effect on system performance. This parameterization also leads to trajectories which are more conservative, producing less overshoot in underdamped systems with modeling error. In this paper we present two MPC solution methods that make use of this parameterization. The first uses a convex solver, and the second makes use of parallel computing on a graphics processing unit (GPU). We show that both approaches drastically reduce solve times for large DoF, long horizon MPC problems allowing solutions at real-time rates. Through simulation and hardware experiments, we show that the parameterized convex solver MPC has faster solve times than traditional MPC for high DoF cases while still achieving similar performance. For the GPU-based MPC solution method, we use an evolutionary algorithm and that we call Evolutionary MPC (EMPC). EMPC is shown to have even faster solve times for high DoF systems. Solve times for EMPC are shown to decrease even further through the use of a more powerful GPU. This suggests that parallelized MPC methods will become even more advantageous with the improvement and prevalence of GPU technology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
