More recently, lower limb exoskeletons (LLE) have gained considerable interests in strength augmentation, rehabilitation, and walking assistance scenarios. For walking assistance, the LLE is expected to control the affected leg to track the unaffected leg's motion naturally. A critical issue in this scenario is that the exoskeleton system needs to deal with unpredictable disturbance from the patient, and the controller has the ability to adapt to different wearers. To this end, a novel data-driven optimal control (DDOC) strategy is proposed to adapt different hemiplegic patients with unpredictable disturbances. The interaction relation between two lower limbs of LLE and the leg of patient's unaffected side are modeled in the context of leader-follower framework. Then, the walking assistance control problem is transformed into an optimal control problem. A policy iteration (PI) algorithm is utilized to obtain the optimal controller. To improve the online adaptation to different patients, an actor-critic neural network (AC/NN) structure of the reinforcement learning (RL) is employed to learn the optimal controller on the basis of PI algorithm. Finally, experiments both on a simulation environment and a real LLE system are conducted to verify the effectiveness of the proposed walking assistance control method.

中文翻译：

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偏瘫患者下肢外骨骼的数据驱动的最佳辅助控制。

最近，下肢外骨骼（LLE）在力量增强，康复和步行辅助场景中引起了相当大的兴趣。对于步行辅助，LLE有望控制受影响的腿以自然跟踪未受影响的腿的运动。在这种情况下，一个关键问题是外骨骼系统需要处理来自患者的不可预测的干扰，并且控制器具有适应不同佩戴者的能力。为此，提出了一种新颖的数据驱动的最佳控制（DDOC）策略，以适应具有无法预测的疾病的不同偏瘫患者。在领导者跟随者框架的背景下，模拟了LLE的两个下肢与患者未患侧腿之间的相互作用关系。然后，将步行辅助控制问题转化为最优控制问题。使用策略迭代（PI）算法来获得最佳控制器。为了提高在线适应不同患者的能力，在PI算法的基础上，采用强化学习（RL）的行为者-批评神经网络（AC / NN）结构来学习最优控制器。最后，在模拟环境和真实的LLE系统上进行了实验，以验证所提出的步行辅助控制方法的有效性。