Modified transpose Jacobian control of robotic systems
Modified transpose Jacobian control of robotic systems
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DOI：
10.1016/j.automatica.2006.12.029
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Ali A. Moosaviana;Evangelos Papadopoulosb
中科院分区：
文献类型：
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作者：
Ali A. Moosaviana;Evangelos Papadopoulosb
转置雅可比(TJ)控制的简单性是该算法用于机器人控制的一个显著特点。然而，糟糕的性能可能会导致跟踪快速轨迹，因为它不是基于动力学的。在存在反馈测量噪声的情况下，使用高增益会严重降低性能。另一个缺点是，没有规定的方法来选择其控制增益。本文基于反馈线性化方法，提出了一种改进的TJ(MTJ)算法，该算法利用前一时间步中存储的控制命令数据作为学习工具来提高性能。这种新算法的增益可以系统地选择，并且不需要很大的增益，从而改善了算法的噪声抑制特性。基于李亚普诺夫定理，证明了标准算法和MTJ算法都是渐近稳定的。对所需计算工作量的分析表明，与基于模型的算法相比，所提出的MTJ定律的效率更高。给出了仿真结果，比较了MTJ算法与TJ算法和基于模型的算法在不同任务下的跟踪性能。这些仿真结果表明，新的MTJ算法的性能与计算扭矩算法相当，不需要对象动力学的先验知识，并且减少了计算负担。因此，该算法非常适合于大多数工业应用，在这些应用中，简单高效的算法比计算量大的复杂理论算法更合适。
The simplicity of Transpose Jacobian (TJ) control is a significant characteristic of this algorithm for controlling robotic manipulators. Nevertheless, a poor performance may result in tracking of fast trajectories, since it is not dynamics-based. Use of high gains can deteriorate performance seriously in the presence of feedback measurement noise. Another drawback is that there is no prescribed method of selecting its control gains. In this paper, based on feedback linearization approach a Modified TJ (MTJ) algorithm is presented which employs stored data of the control command in the previous time step, as a learning tool to yield improved performance. The gains of this new algorithm can be selected systematically, and do not need to be large, hence the noise rejection characteristics of the algorithm are improved. Based on Lyapunov's theorems, it is shown that both the standard and the MTJ algorithms are asymptotically stable. Analysis of the required computational effort reveals the efficiency of the proposed MTJ law compared to the Model-based algorithms. Simulation results are presented which compare tracking performance of the MTJ algorithm to that of the TJ and Model-Based algorithms in various tasks. Results of these simulations show that performance of the new MTJ algorithm is comparable to that of Computed Torque algorithms, without requiring a priori knowledge of plant dynamics, and with reduced computational burden. Therefore, the proposed algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden.