PD based Robust Quadratic Programs for Robotic Systems
PD based Robust Quadratic Programs for Robotic Systems
复制标题
基于 PD 的机器人系统鲁棒二次规划
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Sushant Veer
中科院分区：
文献类型：
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作者：
Shishir N Y Kolathaya;Sushant Veer
在本文中，受比例微分（PD）控制律的启发，我们提出了一类基于控制李雅普诺夫函数（CLF）的机器人系统的二次规划（QP）。比例-微分（PD）控制律是独立的机器人模型，但是，他们没有纳入物理约束，如转矩饱和。另一方面，大多数基于优化的控制设计方法确保满足物理约束，但它们对机器人模型中的误差很敏感。基于PD的二次规划（PD-QPs），本文提出的，是第一步缩小PD和基于优化的控制器之间的差距，把两者的最佳结合在一起。我们得到两个版本的PD-QP：基于模型和无模型。此外，对于跟踪时变轨迹，我们建立了基于模型的PD-QP的渐近稳定性，以及无模型PD-QP的最终有界性。两个机器人模型：一个完全驱动的推车杆和欠驱动的5自由度的机器人的性能进行评估。
In this paper, inspired by Proportional-Derivative (PD) control laws, we present a class of Control Lyapunov Function (CLF) based Quadratic Programs (QPs) for robotic systems. Proportional-Derivative (PD) control laws are independent of the robot model, however, they fail to incorporate physical constraints, such as torque saturation. On the other hand, most optimization based control design approaches ensure satisfaction of the physical constraints, but they are sensitive to errors in the robot model. The PD based Quadratic Programs (PD-QPs), presented in this paper, are a first step towards bridging this gap between the PD and the optimization based controllers to bring the best of both together. We derive two versions of PD-QPs: model-based and model-free. Furthermore, for tracking time-varying trajectories, we establish asymptotic stability for the model-based PD-QP, and ultimate boundedness for the model-free PD-QP. The performance of the PD-QPs is evaluated on two robot models: a fully actuated cart-pole and an underactuated 5-DOF biped.