Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art

Robot-assisted rehabilitation has become a new mainstream trend for the treatment of stroke patients with movement disability. Pneumatic muscle (PM) is one of the most promising actuators for rehabilitation robots, due to its inherent compliance and safety features. In this paper, we conduct a systematic review on the soft rehabilitation robots driven by pneumatic muscles. This review discusses up to date mechanical structures and control strategies for PMs-actuated rehabilitation robots. A variety of state-of-the-art soft rehabilitation robots are classified and reviewed according to the actuation configurations. Special attentions are paid to control strategies under different mechanical designs, with advanced control approaches to overcome PM’s highly nonlinear and time-varying behaviors and to enhance the adaptability to different patients. Finally, we analyze and highlight the current research gaps and the future directions in this field, which is potential for providing a reliable guidance on the development of advanced soft rehabilitation robots

Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training

Robot-assisted rehabilitation offers benefits, such as repetitive, intensive, and task-specific training, as compared to traditional manual manipulation performed by physiotherapists. In this paper, a robust iterative feedback tuning (IFT) technique for repetitive training control of a compliant parallel ankle rehabilitation robot is presented. The robot employs four parallel intrinsically compliant pneumatic muscle actuators that mimic skeletal muscles for ankle's motion training. A multiple degrees-of-freedom normalized IFT technique is proposed to increase the controller robustness by obtaining an optimal value for the weighting factor and offering a method with learning capacity to achieve an optimum of the controller parameters. Experiments with human participants were conducted to investigate the robustness as well as to validate the performance of the proposed IFT technique. Results show that the normalized IFT scheme will achieve a better and better tracking performance during the robot repetitive control and provides more robustness to the system by adapting to various situations in robotic rehabilitation