Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Adaptive compliance control is critical for rehabilitation robots to cope with the varying rehabilitation needs and enhance training safety. This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot (WLLRR), which includes a high-level trajectory planner and a low-level position controller. Dynamic motion primitives (DMPs) and a trajectory deformation algorithm (TDA) are integrated into the high-level trajectory planner, generating multi-joint synchronized desired trajectories through physical human-robot interaction (pHRI). In particular, the amplitude modulation factor of DMPs and the deformation factor of TDA are adapted by a multi-modal adaptive regulator, achieving smooth switching of human-dominant mode, robot-dominant mode, and soft-stop mode. Besides, a linear active disturbance rejection controller is designed as the low-level position controller. Four healthy participants and two stroke survivors are recruited to conduct robot-assisted walking experiments using the TD-MACCS. The results show that the TD-MACCS can smoothly switch three control modes while guaranteeing trajectory tracking accuracy. Moreover, we find that appropriately increasing the upper bound of the deformation factor can enhance the average walking speed (AWS) and root mean square of trajectory deviation (RMSTD).

Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Compliance control is crucial for physical human-robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants' motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.

A triple-step controller with linear active disturbance rejection control for a lower limb rehabilitation robot.

Lower limb rehabilitation robots (LLRRs) have shown promising potential in assisting hemiplegic patients to recover their motor function. During LLRR-aided rehabilitation, the dynamic uncertainties due to human-robot coupling, model uncertainties, and external disturbances, make it challenging to achieve high accuracy and robustness in trajectory tracking. In this study, we design a triple-step controller with linear active disturbance rejection control (TSC-LADRC) for a LLRR, including the steady-state control, feedforward control, and feedback control. The steady-state control and feedforward control are developed to compensate for the gravity and incorporate the reference dynamics information, respectively. Based on the linear active disturbance rejection control, the feedback control is designed to enhance the control performance under dynamic uncertainties. Numerical simulations and experiments are conducted to validate the effectiveness of TSC-LADRC. The results of simulations illustrate that the tracking errors under TSC-LADRC are obviously smaller than those under the triple-step controller without LADRC (TSC), especially with the change of external loads. Moreover, the experiment results of six healthy subjects reveal that the proposed method achieves higher accuracy and lower energy consumption than TSC. Therefore, TSC-LADRC has the potential to assist hemiplegic patients in rehabilitation training.