Control of closed kinematic chains using a singularly perturbed dynamic model

Zhiyong Wang, F. Ghorbel
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引用次数: 23

Abstract

In this paper, we propose a new method to the control of closed kinematic chains (CKC). This method is based on a recently developed singularly perturbed model for CKC. Conventionally, the dynamics of CKC are described by differential-algebraic equations (DAE). Our approach transfers the control of the original DAE system to the control of an artificially created singularly perturbed system in which the slow dynamics corresponds to the original DAE when the small perturbation parameter tends to zero. Compared to control schemes which rely on the solution of nonlinear algebraic constraint equations, the proposed method uses an ODE solver to obtain the dependent coordinates, hence eliminates the need for Newton type iterations and is amenable to real-time implementation. The composite Lyapunov function method is used to show that the closed loop system, when controlled by typical open kinematic chain schemes, achieves local asymptotic trajectory tracking. Simulations and experimental results on a parallel robot, the rice planar delta robot, are also presented to illustrate the efficacy of our method.
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用奇摄动动力学模型控制闭合运动链
本文提出了一种闭合运动链控制的新方法。该方法基于最近发展的CKC奇异摄动模型。传统上,CKC的动力学用微分代数方程(DAE)来描述。我们的方法将原始DAE系统的控制转移到人工创建的奇异摄动系统的控制上,其中当小摄动参数趋于零时,慢动力学对应于原始DAE。与依赖于非线性代数约束方程求解的控制方案相比,该方法采用ODE求解器获得依赖坐标,从而消除了牛顿型迭代的需要,易于实时实现。利用复合Lyapunov函数方法证明了闭环系统在典型开链方案控制下,能够实现局部渐近轨迹跟踪。通过对一个平行机器人——水稻平面三角机器人的仿真和实验结果,验证了该方法的有效性。
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