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Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

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One of the prevalent methods in linear bilateral teleoperation systems with communication channel time delays is using position and velocity signals in the control scheme. Utilization of force signals in such controllers improves the performance significantly and reduces the tracking error. Measuring force signals in such cases is one of the major problems. In this paper, a control scheme in the presence of human and environment force signals for the linear bilateral teleoperation is proposed. Due to elimination of measuring forces in the control scheme, a force estimation approach based on disturbance observers has been utilized. The proposed approach guarantees asymptotic estimation of constant forces. The estimation error would only be bounded for time varying external forces. To cope with the variation of the human and environment force, a sliding-mode-controller is used. The stability and transparency condition in the teleoperation system with the designed control scheme is derived from absolute stability concept. The designed control scheme guarantees the stability of the teleoperation system in the presence of time varying human and environment forces. Experimental results show that the proposed control scheme improves position tracking in the free motion and in contact with the environment. In addition, the force estimation approach appropriately estimates human and environment forces.

Self-sensing actuation has been extensively used in vibration control of flexible structures over the last three decades. Positive position feedback controller has been commonly used in this field due to the robustness against spillover phenomena. Piezoelectric clamped capacitance is the most important parameter that affects the performance of this technique. In this study, the effect of capacitance change on performance and stability of a self-sensing system with positive position feedback controller is investigated. Based on this analysis, some modifications are suggested to increase the stability of the closed-loop system against capacitance change. An online Fourier transform–based capacitance measurement method is used, which guarantees good performance and stability of closed-loop system in the presence of capacitance change. Experimental results are also presented to show the effectiveness of this method in vibration control of cantilever beam.