Physiotherapy is the treatment to recover a patient’s mobility and limb function after an injury, illness, or disability. Rehabilitation robots can be used to replace human physiotherapists. To ensure safety during robot physical therapy, the patient’s limb needs to be controlled to track a desired joint trajectory, and the torque due to interaction force/torque needs to be measured and regulated. Therefore, hybrid impedance and admittance with position control (HIPC) is required to track the trajectory and simultaneously regulate the contact torque. The literature describes two structures of HIPC: (1) a switched framework between admittance and impedance control operating in parallel (HIPCSW); and (2) a series connection between admittance and impedance control without switching. In this study, a hybrid adaptive impedance and position-based admittance control (HAIPC) in series is developed, which consists of a proportional derivative-based admittance position controller with gravitational torque compensation and an adaptive impedance controller. An extended state observer is used to estimate the interaction joint torque due to human stiff contact with the exoskeleton without the use of force/torque sensor, which is then used in the adaptive algorithm to update the stiffness and damping gains of the adaptive impedance controller. Simulation results obtained using MATLAB show that the proposed HAIPC significantly reduces the mean absolute values of the actuation torques (control inputs) required for the shoulder and elbow joints in comparison with HIPC and HIPCSW.

Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation exercises by supporting the patient’s body structure to increase the torques at the hip and knee joints. Assistive rehabilitation is, however, challenging, as the human torque is unknown and varies from patient to patient. This poses difficulties in determining the level of assistance required for a particular patient. In this paper, therefore, a modified extended state observer (ESO)-based integral sliding mode (ISM) controller (MESOISMC) for lower-limb exoskeleton assistive gait rehabilitation is proposed. The ESO is used to estimate the unknown human torque without application of a torque sensor while the ISMC is used to achieve robust tracking of preset hip and knee joint angles by considering the estimated human torque as a disturbance. The performance of the proposed MESOISMC was assessed using the mean absolute error (MAE). The obtained results show an 85.02% and 87.38% reduction in the MAE for the hip and joint angles, respectively, when the proposed MESOISMC is compared with ISMC with both controllers tuned via LMI optimization. The results also indicate that the proposed MESOISMC method is effective and efficient for user comfort and safety during gait rehabilitation training.