Abstract
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.
Highlights
With the increasing requirement of accomplishing complex or difficult tasks in the fields of industry and human daily life, wearable devices/robots have attracted more attentions (Fang et al, 2018, 2019)
For participant 1, from Figures 5A,B, we can see that, after about 5 s training, the weights of actor-critic neural network (AC/neural network (NN)) are bounded convergent, i.e., uniformly bounded because of the disturbances and uncertainties always exist in limb exoskeletons (LLE) system
The tracking performance of the hip joint and knee joint for the LLE system with wearer 1 is depicted in Figures 6A,B, which states that with the help of the learned optimal control policies, the hip joint and knee joint of two limbs of the exoskeleton can achieve synchronization with the desired motion trajectories
Summary
With the increasing requirement of accomplishing complex or difficult tasks in the fields of industry and human daily life, wearable devices/robots have attracted more attentions (Fang et al, 2018, 2019). As one of wearable devices, the lower limb exoskeleton (LLE) integrates artificial intelligence technologies, control and robotic theory, and has become a hot topic own to its practical applications. Exoskeleton Assistance Control for Hemiplegia assistance (Esquenazi et al, 2017; Zhang et al, 2017), and rehabilitation (Sankai, 2010; Huo et al, 2014). The wearers usually have walking ability, and the influence of human-robot interaction force should be considered in the controller designs. One usually uses the LLE to assist patients’ walking/training in which the patients lose their ability to walk. LLE has served as a device for rehabilitation/walking training with paraplegia and hemiplegia. Some researchers have introduced biological signals of human body into the controller designs, such as Electromyography signal (EMG) (Kiguchi et al, 2004) and Electroencephalogram signal (EEG) (Kilicarslan et al, 2013)
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