论文标题
合作操纵器的自适应机器学习
Adaptive Machine Learning for Cooperative Manipulators
论文作者
论文摘要
使用自适应机器学习解决了在不准确运动学模型的情况下，在存在不准确运动模型的情况下形成封闭运动链的合作操作器的自我调整控制的问题。两个级联估计器在线更新了与相互联系的操纵器的相对位置/方向不确定性有关的运动学参数，以调整一个合作控制器，以通过最小值驱动力来实现准确的运动跟踪。该技术允许对所涉及的操纵器的相对运动学进行准确的校准，而无需高精度的终点传感或力测量，因此在经济上是合理的。研究整个实时估计器/控制器系统的稳定性表明，如果i）i）如果i）角速度矢量的方向不会随着时间的推移而保持恒定，并且ii）ii）初始运动学参数误差在某些已知参数的尺度函数上界定。即使控制定律涉及在估计参数上计算的矩阵的近似，自适应控制器也被证明是无奇异性的。实验结果表明，常规反向动态控制方案跟踪性能对运动学不准确的敏感性，而自我调整合作控制器的跟踪误差显着降低。
The problem of self-tuning control of cooperative manipulators forming a closed kinematic chain in the presence of an inaccurate kinematics model is addressed using adaptive machine learning. The kinematic parameters pertaining to the relative position/orientation uncertainties of the interconnected manipulators are updated online by two cascaded estimators in order to tune a cooperative controller for achieving accurate motion tracking with minimum-norm actuation force. This technique permits accurate calibration of the relative kinematics of the involved manipulators without needing high precision end-point sensing or force measurements, and hence it is economically justified. Investigating the stability of the entire real-time estimator/controller system reveals that the convergence and stability of the adaptive control process can be ensured if i) the direction of the angular velocity vector does not remain constant over time, and ii) the initial kinematic parameter error is upper bounded by a scaler function of some known parameters. The adaptive controller is proved to be singularity-free even though the control law involves inverting the approximation of a matrix computed at the estimated parameters. Experimental results demonstrate the sensitivity of the tracking performance of the conventional inverse dynamic control scheme to kinematic inaccuracies, while the tracking error is significantly reduced by the self-tuning cooperative controller.
