Data-Driven Optimal Assistance Control of a Lower Limb Exoskeleton for Hemiplegic Patients.

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.