Haeufle D F B, Günther M, Wunner G, Schmitt S
Universität Stuttgart, Institut für Sport- und Bewegungswissenschaft, Allmandring 28, D-70569 Stuttgart, Germany and Universität Stuttgart, Institut für Theoretische Physik 1, Pfaffenwaldring 57, D-70550 Stuttgart, Germany.
Universität Stuttgart, Institut für Sport- und Bewegungswissenschaft, Allmandring 28, D-70569 Stuttgart, Germany and Friedrich Schiller Universität, Institut für Sportwissenschaft, Seidelstrasse 20, D-07743 Jena, Germany.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jan;89(1):012716. doi: 10.1103/PhysRevE.89.012716. Epub 2014 Jan 22.
In biomechanics and biorobotics, muscles are often associated with reduced movement control effort and simplified control compared to technical actuators. This is based on evidence that the nonlinear muscle properties positively influence movement control. It is, however, open how to quantify the simplicity aspect of control effort and compare it between systems. Physical measures, such as energy consumption, stability, or jerk, have already been applied to compare biological and technical systems. Here a physical measure of control effort based on information entropy is presented. The idea is that control is simpler if a specific movement is generated with less processed sensor information, depending on the control scheme and the physical properties of the systems being compared. By calculating the Shannon information entropy of all sensor signals required for control, an information cost function can be formulated allowing the comparison of models of biological and technical control systems. Exemplarily applied to (bio-)mechanical models of hopping, the method reveals that the required information for generating hopping with a muscle driven by a simple reflex control scheme is only I=32 bits versus I=660 bits with a DC motor and a proportional differential controller. This approach to quantifying control effort captures the simplicity of a control scheme and can be used to compare completely different actuators and control approaches.
在生物力学和生物机器人学中，与技术驱动装置相比，肌肉通常与降低的运动控制工作量和简化的控制相关联。这是基于非线性肌肉特性对运动控制产生积极影响的证据。然而，如何量化控制工作量的简单性方面并在不同系统之间进行比较仍不明确。诸如能量消耗、稳定性或急动度等物理量度已被用于比较生物和技术系统。本文提出了一种基于信息熵的控制工作量的物理量度。其理念是，如果根据所比较系统的控制方案和物理特性，用较少的处理后的传感器信息生成特定运动，那么控制就更简单。通过计算控制所需的所有传感器信号的香农信息熵，可以制定一个信息成本函数，从而能够比较生物和技术控制系统的模型。该方法以跳跃的（生物）力学模型为例进行应用，结果表明，采用简单反射控制方案驱动的肌肉产生跳跃所需的信息仅为I = 32比特，而使用直流电机和比例微分控制器时则为I = 660比特。这种量化控制工作量的方法抓住了控制方案的简单性，可用于比较完全不同的驱动装置和控制方法。
