Functional electrical stimulation (FES) has recently been proposed as a supplementary torque assist in lower-limb powered exoskeletons for persons with paraplegia. In the combined system, also known as a hybrid neuroprosthesis, both FES-assist and the exoskeleton act to generate lower-limb torques to achieve standing and walking functions. Due to this actuator redundancy, we are motivated to optimally allocate FES-assist and exoskeleton torque based on a performance index that penalizes FES overuse to minimize muscle fatigue while also minimizing regulation or tracking errors. Traditional optimal control approaches need a system model to optimize; however, it is often difficult to formulate a musculoskeletal model that accurately predicts muscle responses due to FES. In this paper, we use a novel identification and control structure that contains a recurrent neural network (RNN) and several feedforward neural networks (FNNs). The RNN is trained by supervised learning to identify the system dynamics, while the FNNs are trained by a reinforcement learning method to provide sub-optimal control actions. The output layer of each FNN has its unique activation functions, so that the asymmetric constraint of FES and the symmetric constraint of exoskeleton motor control input can be realized. This new structure is experimentally validated on a seated human participant using a single joint hybrid neuroprosthesis.

中文翻译：

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具有多层神经网络结构的混合神经假体系统中的次优求解致动器冗余

最近，功能性电刺激（FES）已被提议作为下肢动力截肢患者下肢动力外骨骼的辅助扭矩辅助。在组合系统（也称为混合神经假体）中，FES辅助和外骨骼都可产生下肢扭矩，以实现站立和行走功能。由于执行器具有冗余性，因此我们有动机根据性能指标来优化分配FES辅助扭矩和外骨骼扭矩，从而惩罚FES过度使用，从而最大程度地减少肌肉疲劳，同时最大程度地减少调节或跟踪误差。传统的最优控制方法需要一个系统模型来进行优化。然而，通常难以建立能够准确预测由于FES引起的肌肉反应的肌肉骨骼模型。在本文中，我们使用一种新颖的识别和控制结构，其中包含一个递归神经网络（RNN）和几个前馈神经网络（FNN）。通过监督学习来训练RNN以识别系统动力学，而通过加强学习方法来训练FNN以提供次优的控制动作。每个FNN的输出层都有其独特的激活功能，因此可以实现FES的不对称约束和外骨骼电机控制输入的对称约束。使用单个关节混合神经假体在就座的人类参与者上通过实验验证了这种新结构。而FNN通过强化学习方法进行训练，以提供次优的控制动作。每个FNN的输出层都有其独特的激活功能，因此可以实现FES的不对称约束和外骨骼电机控制输入的对称约束。使用单个关节混合神经假体在就座的人类参与者上通过实验验证了这种新结构。而FNN通过强化学习方法进行训练，以提供次优的控制动作。每个FNN的输出层都有其独特的激活功能，因此可以实现FES的不对称约束和外骨骼电机控制输入的对称约束。使用单个关节混合神经假体在就座的人类参与者上通过实验验证了这种新结构。