用一阶方法实现与二阶方法相当的机器人控制性能
Accelerated gradient descent for high frequency Model Predictive Control
- 采用加速梯度下降法求解高频率模型预测控制问题
- 在力矩控制机械臂上达到与二阶方法相当的控制精度
- 为实时控制提供轻量高效的新思路，适合嵌入式系统
近年来，模型预测控制在机器人领域的应用前景推动了针对最优控制问题的定制化二阶方法的发展。尽管这些方法具有良好的收敛性，但高效实现仍具挑战。本文研究一阶方法在该场景下的有效性，实验表明在力矩控制机械臂上，一阶方法可达到与二阶方法相当的性能表现。
原文摘要 · Abstract (English)
The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While those methods benefit from strong convergence properties, tailored efficient implementations are challenging to derive. In this work, we study the potential effectiveness of first-order methods and show on a torque controlled manipulator that they can equal the performances of second-order methods.
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