Jonathan Casas

Wireless networks are vital for implementing flexible Networked Controlled Systems (NCS) in distributed applications, yet they introduce sampling errors, delays, and packet losses that can compromise control performance. While emerging communication services such as Ultra-Reliable Low Latency Communications (URLLC) can mitigate these issues, they consume more shared network resources and may not be efficient if the NCS does not manage its transmissions. Event Triggered Control (ETC) addresses this challenge by determining when an update is needed, thereby specifying a Minimum Inter-Event Time (MIET) and Maximum Allowable Delay (MAD) to ensure a prescribed L2 norm condition or robust stability criterion. This paper proposes an Encoder-Decoder (E/D) architecture for NCS that requires that a control signal is transmitted over a wireless link. Instead of sending the original control signal whenever a trigger occurs, this method transmits an error signal produced by the comparison between the original control signal and a locally estimated signal. This estimated signal is assumed to be locally available at the transmitter and receiver to be used as the encoder and decoder, respectively. Assuming that the estimated signal is correlated to the original control signal, the transmitted error has a lower magnitude than the original transmitted signal. As a result, the NCS can guarantee its robust stability criterion while increasing the achievable MIET, thus reducing network resource usage. This approach is validated in a Cooperative Adaptive Cruise Control (CACC) setup, demonstrating an at least 20% improvement in MIET compared to conventional ETC, while maintaining L2 (string) stability and robust performance with fewer transmissions.

Lower limb hybrid exoskeletons integrate powered mechanisms and functional electrical stimulation (FES) to provide assistive forces and activate muscles for walking. Technical challenges exist to customize the control of these hybrid devices due to the nonlinear and uncertain walking and muscle dynamics of the combined human–exoskeleton system. Different from gait optimization techniques, this article exploits a learning-based strategy to interface a hybrid exoskeleton for treadmill walking. An adaptive control approach provides torque assistance about the hip and knee joints using a four-degrees-of-freedom (DoFs) cable-driven exoskeleton and activates the quadriceps and hamstrings muscle groups via FES. The human–exoskeleton system is modeled with phase-dependent switched pendular dynamics to capture gait phase transitions (i.e., right leg in stance while left leg swings and vice versa). A concurrent learning adaptive controller is designed to achieve kinematic joint tracking and estimate the uncertain parameters in the lower limb dynamics. The stability of the overall switched system is ensured using a multiple Lyapunov function approach and dwell time analysis to guarantee exponential tracking and parameter estimation convergence across gait phase transitions. The developed learning controller was implemented during walking experiments at a constant speed in two nondisabled individuals. To illustrate the tracking benefit of the learning method, the performance of the concurrent learning controller and a classical gradient-based adaptive controller are compared during 8-min treadmill walking trials. The results highlight the better performance of concurrent learning compared with the classical adaptive controller. On average, concurrent learning significantly reduces 22.6% in the mean root-mean-squared (rms) kinematic tracking error ( $e$ ) for the knee and hip joints. Notably, the concurrent learning system demonstrates average parameter convergence in less than 145 s, whereas the classical adaptive control approach fails to exhibit convergence within the 8-min trial duration.

Functional electrical stimulation (FES) and motorized cycles have the potential to recover lost function and mobility in people with neurological disorders. However, the human-robot system is uncertain, nonlinear, and time-varying, posing technical challenges to customizing the interaction across participants. In this paper, a closed-loop switching adaptive controller is designed to achieve cadence tracking using a powered FES-cycling system. The adaptive design copes with the parametric uncertainty of the cycle-rider dynamics and the unknown switching muscle control effectiveness by computing estimates of the uncertain parameters. A saturated state-feedback controller activates the quadriceps muscle groups, whereas an integral concurrent learning technique activates the electrical motor and leverages input-output data to estimate the parametric uncertainty and achieve cadence tracking. A switching Lyapunov-based stability analysis is developed in two phases. The initial phase ensures bounded tracking and estimation when a learning condition has not been attained; in the second phase, global exponential tracking and estimation convergence is ensured, given an online-verified finite excitation condition is satisfied. The developed controller was tested during three FES-cycling trials with different cadence trajectories and learning conditions in eight able-bodied individuals and three participants with neurological conditions (NCs) during ten-minute and five-minute experiments, respectively. The system achieves an average RMS cadence tracking error of $2.49\pm0.42$ , $2.66\pm0.36$ , and $2.69\pm0.58$ revolutions per minute (RPM) with the able-bodied participants, while an average RMS cadence tracking error of $3.15\pm0.97$ , $2.60\pm0.17$ , $3.47\pm1.43$ RPM for the participants with NCs in three cycling trials. Note to Practitioners —FES-Cycling is a rehabilitation strategy recommended to recover muscle capacity and improve cardiovascular function in people with neurological disorders. Although significant progress has been made on the closed-loop control of FES-cycling systems, a critical need exists to develop adaptive strategies to comply with the nonlinear, time-varying muscle responses to FES, cope with the uncertain parameters of the cycle-rider system, and improve tracking performance. This paper develops a decoupled control design for muscles and motor. The FES controller is tuned using minimal parameters to yield bounded muscle responses with a tunable saturation limit. The electric motor control is designed using an adaptive-based method that estimates the uncertain parameters in the cycle-rider system and strategically exploits the muscle input to improve tracking performance. Results from cycling trials in able-bodied individuals and participants with neurological conditions demonstrate the feasibility of the adaptive control design to tracking different trajectories with the same set of control parameters across all participants despite the inherent variability in human subjects. The adaptive controller requires minimal tuning and copes with the rider’s uncertainty while obtaining predictable, satisfactory performance, potentially paving the way for the widespread implementation of adaptive closed-loop controllers for FES-cycling systems at the clinic and community.

Lower-limb exoskeletons can aid restoring mobility in people with movement disorders. Cable-driven exoskeletons can offload their actuators away from the human body to reduce the weight imposed on the user and enable precise control of joints. However, ensuring limb coordination through bidirectional motion control of joints using cables raise the technical challenge of preventing the occurrence of undesired cable slackness or counteracting forces between cables. Thus, motivation exists to develop a control design framework that integrates both a joint control loop to ensure suitable limb tracking and a cable control loop to maintain cable tension properly. In this paper, a two-layer control structure consisting of high and low-level controllers are developed to ensure a knee-joint exoskeleton system follows the desired joint trajectories and adjusts the cable tension, respectively. A repetitive learning controller is designed for the high-level knee joint tracking objective motivated by the periodic nature of the desired leg swings (i.e., to achieve knee flexion and extension). Low-level robust controllers are developed for a pair of cables, each actuated by an electric motor, to track target motor trajectories composed of motor kinematics and offset angles to mitigate cable slackness. The offset angles are computed using admittance models that exploit measurements of the cable tensions as inputs. Each electric motor switches its role between tracking the knee joint trajectory (i.e., the motor acts as the leader motor to achieve flexion or extension) and implementing the low-level controller (i.e., the motor acts as the follower motor to reduce slackness). Hence, at any time, one motor is the leader and the other is the follower. A Lyapunov-based stability analysis is developed for the high-level joint controller to ensure global asymptotic tracking and the low-level follower controller to guarantee global exponential tracking. The designed controllers are implemented during leg swing experiments in six able-bodied individuals while wearing the knee joint cable-driven exoskeleton. A comparison of the results obtained in two trials with and without using the admittance model (i.e., exploiting cable tension measurements) is presented. The experimental results indicate improved knee joint tracking performance, smaller control input magnitudes, and reduced cable slackness in the trial that leveraged cable tension feedback compared to the trial that did not exploit tension feedback.

Currently, Social Assistive Robotics (SAR) is widely explored in different areas and scenarios. In cardiac rehabilitation, SAR has been recently implemented as a tool to improve the quality of the procedures and support patients to boost their performance. As cardiac rehabilitation comprises numerous sessions, such systems must guarantee to be effective in the long term. Therefore, to achieve this goal, it is important to understand how users, namely patients and clinicians who mostly know the needs and the therapy environment, perceive this technology. In this context, this paper presents the assessment of the attitudes towards a social robot in order to evaluate the expectation of potential new users, and perception of users who interacted with the social robot during a period of 18 weeks performing cardiac rehabilitation. A total of 43 participants (28 patients and 15 clinicians) were included in the study, and acceptance and perception factors were evaluated through a modified UTAUT questionnaire model and open discussion sessions. Results show that 75% of patients have positive thoughts regarding the usefulness, utility, safety, and trust perceived of a social robot, and 80% of clinicians consider that the robot is a useful tool for cardiac rehabilitation. Similarly, a more positive perception was noticed after the users interacted with the robot. Furthermore, this perception study allows the enhancement of the social model of interaction in the future, aiming to provide a more natural interaction trough personalized features, increasing social abilities and engagement of the users during the therapy.

This paper presents the development and validation of a polymer optical-fiber strain-gauge sensor based on the light-coupling principle to measure axial deformation of elastic tendons incorporated in soft actuators for wearable assistive robots. An analytical model was proposed and further validated with experiment tests, showing correlation with a coefficient of R = 0.998 between experiment and theoretical data, and reaching a maximum axial displacement range of 15 mm and no significant hysteresis. Furthermore, experiment tests were carried out attaching the validated sensor to the elastic tendon. Results of three experiment tests show the sensor’s capability to measure the tendon’s response under tensile axial stress, finding 20.45% of hysteresis in the material’s response between the stretching and recovery phase. Based on these results, there is evidence of the potential that the fiber-optical strain sensor presents for future applications in the characterization of such tendons and identification of dynamic models that allow the understanding of the material’s response to the development of more efficient interaction-control strategies.

We report the application of fiber Bragg grating (FBG) arrays in polymer optical fibers for the quasi-distributed displacement and torque sensing in a series elastic actuator's spring. The arrays were inscribed in cyclic transparent optical polymer fiber through the direct-write, plane-by-plane inscription method using a femtosecond laser. Two arrays with four FBGs each were positioned on a rotary spring, and a numerical analysis using the finite-element method was performed to evaluate the sensors' behavior under different loading conditions. Experimental tests were performed on the spring, and the results show that the FBGs responses follow the predicted numerical simulations. In addition, a Kalman filter was applied on the response of the FBGs for sensor fusion, leading to a more accurate torque estimation, where the relative error for the torque estimation was improved by a factor of 7. Furthermore, transverse forces were applied on the predefined points of the spring in order to show the ability of the quasi-distributed sensor approach to identify external mechanical disturbances on the spring.

This paper presents the development of temperature sensors based on fiber Bragg gratings (FBGs) embedded in 3D-printed structures made of different materials, namely polylatic acid (PLA) and thermoplastic polyurethane (TPU). A numerical analysis of the material behavior and its interaction with the FBG sensor was performed through the finite element method. A simple, fast and prone to automation process is presented for the FBG embedment in both PLA and TPU structures. The temperature tests were made using both PLA- and TPU-embedded FBGs as well as an unembedded FBG as reference. Results show an outstanding temperature sensitivity of 139 pm/°C for the FBG-embedded PLA structure, which is one of the highest temperature sensitivities reported for FBG-based temperature sensors in silica fibers. The sensor also shows almost negligible hysteresis (highest hysteresis below 0.5%). In addition, both PLA- and TPU-embedded structures present high linearity and response time below 2 s. The results presented in this work not only demonstrate the feasibility of developing fully embedded temperature sensors with high resolution and in compliance with soft robot application requirements, but also show that the FBG embedment in such structures is capable of enhancing the sensor performance.

Social robots are demonstrating to have potential in several healthcare applications, especially in rehabilitation areas. This paper presents an architecture for a socially assistive robot system for cardiac rehabilitation, based on a model-controller structure through a finite-state machine and a behaviour module. The platform has been designed to provide social support and assistance during the therapy, aiming to improve the quality of the provided service, as well as the engagement and performance of the patients. This architecture has been tested under clinical conditions with a patient during a typical therapy session. The results show that the proposed architecture is able to adapt to the various situations present during a session, providing a robust framework to further develop the robot's behaviour towards a more natural and intuitive interaction with the patients.