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Robot-assisted rehabilitation therapy has become an important technology applied to recover the motor functions of disabled individuals. In the present paper, an adaptive admittance control strategy combined with a neural-network-based disturbance observer (AACNDO) is developed for a therapeutic robot to provide upper extremity movement assistance. Firstly, a comprehensive overview of the robot hardware and real-time control system is introduced. Then, the dynamics-based adaptive admittance controller is designed to improve human-robot interaction compliance and induce the active participation of the patients during rehabilitation training. A disturbance observer with a radial basis function network is designed to guarantee the control performance with external uncertainties and dynamics error. Besides, an adaption law is integrated into the admittance model to adjust the interaction compliance in different working areas based on the motion intention and recovery phase of the patient. Further experimental investigations, including sinusoidal trajectory tracking experiments, circular trajectory tracking experiments with admittance adjustment, and intention-based resistive training experiments, are conducted by three volunteers. Finally, the experimental results validate the feasibility and effectiveness of the rehabilitation robot and AACNDO scheme in providing patient-passive and patient-cooperative rehabilitation training.

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Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

The applications of robotics and automation technology to the therapies of neuromuscular and orthopedic impairments have received increasing attention due to their promising prospects. In this paper, we present an actuated upper extremity exoskeleton aimed to facilitate the rehabilitation training of the disable patients. A modified sliding mode control strategy incorporating a proportional-integral-derivative sliding surface and a fuzzy hitting control law is developed to ensure robust and optimal position control performance. Dynamic modeling of the exoskeleton as well as the human arm is presented and then applied to the development of the fuzzy sliding mode control algorithm. A theoretical proof of the stability and convergence of the closed-loop system is presented using the Lyapunov theorem. Three typical real-time position control experiments are conducted with the aim of evaluating the effectiveness of the proposed control scheme. The performances of the fuzzy sliding mode control algorithm are compared to those of conventional proportional-integral-derivative controller and conventional sliding mode control algorithm. The experimental results indicate that the position control with fuzzy sliding mode control algorithm has a bandwidth about 4 Hz during operation. Furthermore, this control approach can guarantee the best control performances in term of tracking accuracy, response speed, and robustness against external disturbances.