Reinhart René Felix, Shareef Zeeshan, Steil Jochen Jakob
Fraunhofer Research Institute for Mechatronic Systems Design (IEM), Zukunftsmeile 1, 33102 Paderborn, Germany.
Institute for Robotics and Process Control (IRP), Technische Universität Braunschweig, Mühlenpfordstraße 23, 38106 Braunschweig, Germany.
Sensors (Basel). 2017 Feb 8;17(2):311. doi: 10.3390/s17020311.
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.
基于前馈模型的控制依赖于被控对象的模型，例如在机器人技术中，依赖于对机械手运动学或动力学的精确了解。然而，机械模型和分析模型并不能涵盖对象固有特性的所有方面，并且由于参数变化、未建模的摩擦或软材料等原因，仍存在未建模的动力学。在这种情况下，机器学习是一种从数据中提取非线性对象模型的合适替代技术。然而，完全基于数据的模型也存在不准确的问题，并且如果它们包含对已知分析模型的学习，则效率低下。因此，本文认为基于包含分析模型和学习到的误差模型的混合模型的前馈控制可以显著提高建模精度。这里的混合建模旨在结合两种建模方式的优点。描述了混合建模方法，并针对机器人技术中的两个典型问题，即逆运动学控制和计算转矩控制，展示了该方法。前者针对一个冗余软机器人进行，后者针对一个具有冗余自由度的刚性工业机器人进行，其中两个平台都没有完整的分析模型。
