论文标题
识别基于MPC的PowerCube串行机器人的摩擦模型
Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot
论文作者
论文摘要
对于基于模型的控制，精确且复杂性适当地表示真实系统是高高控制质量的决定性先决条件。在结构化的逐步过程中，得出了Schunk PowerCube机器人的模型预测控制（MPC）方案。 Neweul-M $^2 $以象征性和数值形式提供了必要的非线性模型。为了处理派生的非线性模型涉及的繁重的在线计算负担，基于与所需轨迹的非线性系统和一个先验已知的相应的进料前向控制器的非线性系统，开发了线性时变的MPC方案。为了改善关节非线性摩擦模型的识别，将非线性回归方法和非线性动力学（Sindy）的稀疏识别相互比较，彼此涉及鲁棒性，在线适应性以及对输入数据的必要预处理。一切都在使用标准笔记本电脑PC的苗条，低成本控制系统上实现。
For model-based control, an accurate and in its complexity suitable representation of the real system is a decisive prerequisite for high and robust control quality. In a structured step-by-step procedure, a model predictive control (MPC) scheme for a Schunk PowerCube robot is derived. Neweul-M$^2$ provides the necessary nonlinear model in symbolical and numerical form. To handle the heavy online computational burden involved with the derived nonlinear model, a linear time-varying MPC scheme is developed based on linearizing the nonlinear system concerning the desired trajectory and the a priori known corresponding feed-forward controller. To improve the identification of the nonlinear friction models of the joints, a nonlinear regression method and the Sparse Identification of Nonlinear Dynamics (SINDy) are compared with each other concerning robustness, online adaptivity, and necessary preprocessing of the input data. Everything is implemented on a slim, low-cost control system with a standard laptop PC.
