This paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user’s intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton.

This article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties and external disturbances that are always present. For this reason, the proposed control considers that the non-linear part of the model is unknown and is perturbed by external disturbances, which are estimated by an active disturbance rejection control via Artificial Neural Networks. To validate the proposed approach, a numerical simulation and an experimental implementation of the ANN-Controller are developed.

This article addresses the design of a robust inner and outer loop controller for active gait retraining in individuals with leg motor weakness, using a lower limb exoskeleton with elastic joints. The proposed control algorithm for the inner loop is based on a robust force controller that considers dynamics in the sagittal plane, accounting for human and external perturbations. For the outer loop, a robust controller is proposed to ensure the tracking of the desired trajectories. Lyapunov candidate functions are used to demonstrate the stability of the closed-loop system. To validate the proposed algorithmic approach, numerical simulations and experimental tests are developed. The experimental results show excellent performance, even in the presence of external perturbations and opposing human reactions; the tracking error is minimal (MAE ≤ 0.0661 rad).

This paper presents the development of a robust control algorithm to be applied in a knee and ankle joint exoskeleton designed for rehabilitation of flexion/extension movements. The goal of the control law is to follow the trajectory of a straight leg extension routine in a sitting position. This routine is commonly used to rehabilitate an injury on an Anterior Cruciate Ligament (ACL) and it is applied to the knee and ankle joints. Moreover, the paper presents the development and implementation of the robotic structure of the ankle joint to integrate it into an exoskeleton for gait rehabilitation. The development of the dynamic model and the implementation of the control algorithm in simulation and experimental tests are presented, showing that the proposed control guarantees the convergence of the tracking error.

In this paper, serial elastic actuators (SEAs) in conjunction with an accelerometer are proposed as force sensors to detect the intention of movement, and the SEA is proposed as a gentle actuator of a patient’s upper-limb exoskeleton. A smooth trajectory is proposed to provide comfortable performance. There is an offset trajectory between the link and the motor, which increases safety by preventing sudden movements, and the offset is equivalent to the torsional elastic spring constant. The proposed control law is based on a backstepping approach tested in real-time experiments with robust results in a 2-DoF upper-limb rehabilitation exoskeleton. The experimental results showed a sensitivity of 100% and a positive predictive value of 97.5% for movement intention detection.

This article presents a virtual swashplate mechanism for a mini helicopter in classic configuration. The propeller bases are part of a passive mechanism driven by main rotor torque modulaton, this mechanism generates a synchronous and opposite change in the propellers angle of attack, then the thrust vector tilts. This approach proposes to control the 6 degrees of freedom of the aircraft using two rotors. The main rotor controls vertical displacement and uses torque modulation and swing-hinged propellers to generate pitch and roll moments and the horizontal displacement while the yaw moment is controlled by the tail rotor. The dynamic model is obtained using the Newton-Euler approach and robust control algorithms are proposed. Experimental results are presented to show the performance of the proposed virtual swashplate in real-time outdoor hover flights.