Learning combined feedback and feedforward control of a musculoskeletal system
Learning combined feedback and feedforward control of a musculoskeletal system
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DOI：
10.1007/bf00238741
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发表时间：
1996-07-01
影响因子：
1.9
通讯作者：
Stroeve, S
中科院分区：
文献类型：
--
作者：
Stroeve, S
本文的目标是神经肌肉控制的学习，给出以下必要条件：（1）控制回路中的时间延迟，（2）非线性肌肉特性，（3）前馈和反馈控制的学习，（4）任务期间反馈增益调制的可能性。给出了满足这些条件的控制系统和学习方法。控制系统包含一个神经网络，包括前馈和反馈控制。学习方法是通过时间的反向传播与显式的灵敏度模型。将给出具有两块肌肉的单自由度手臂的结果。取得了良好的控制效果，与实验数据进行了比较。控制器的分析表明，显着的差异，控制器的特性被发现，如果回路延迟被忽略。在控制任务期间，系统显示反馈增益调制，类似于在快速自主收缩期间实验发现的反射增益调制。如果只有有限的反馈信息可用于控制器，则系统学习共同收缩拮抗肌对。以这种方式，关节刚度增加，并且更容易保持稳定的控制。
The goal of this paper is the learning of neuromuscular control, given the following necessary conditions: (1) time delays in the control loop, (2) non-linear muscle characteristics, (3) learning of feedforward and feedback control, (4) possibility of feedback gain modulation during a task. A control system and learning methodology that satisfy those conditions is given. The control system contains a neural network, comprising both feedforward and feedback control. The learning method is backpropagation through time with an explicit sensitivity model. Results will be given for a one degree of freedom arm with two muscles. Good control results are achieved which compare well with experimental data. Analysis of the controller shows that significant differences in controller characteristics are found if the loop delays are neglected. During a control task the system shows feedback gain modulation, similar to experimentally found reflex gain modulation during rapid voluntary contraction. If only limited feedback information is available to the controller the system learns to co-contract the antagonistic muscle pair. In this way joint stiffness increases and stable control is more easily maintained.