Lower-limb exoskeletons (LLE) have proven to be effective tools in rehabilitation for diminishing gait impairments. However, modern LLE control systems based on the assistance-as needed concept face challenges in providing appropriate interaction torques to the user. This is especially true when it is necessary to make the exoskeleton transparent, i.e. with zero user robot interaction torques (τUR), even during walking speed variations, when no assistance is required. To address these limitations, in this work we designed a novel transparent control system that utilizes a baseline torque controller, which generates a torque profile based on τUR from previous strides, and drives the actuators, leading to a lower τUR, along with a synchronization layer comprising a pool of adaptive oscillators (AOs) to estimate the user's gait phase and adapt the controller to different walking speeds. Additionally, a zero-torque controller was used in parallel to the main torque controller to enhance users' volitional control and balance. The proposed controller was experimentally tested on a six-DoF LLE with eight healthy participants who walked for 200 gait strides at three walking speeds. The baseline torque controller was shown to adapt to different walking speeds and successfully improve the exoskeleton transparency in terms of user-robot interaction, reducing the average τUR by 40% of the entire exoskeleton and 70% of the hip joint at 0.8 m/s, and gait kinematics compared to our prior controller based on a zero impedance model.

Recent studies underscore the importance of the patient’s active contribution and voluntary effort in enhancing therapy outcomes in physical rehabilitation. This paper presents an adaptive control scheme to implement active robotic rehabilitation. The primary goal is to dynamically regulate robotic assistance based on the patient’s performance and individual conditions, encouraging active participation, and effective therapy. To achieve this, a Lyapunov-based adaptive algorithm is developed that dynamically adjusts the admittance parameters by balancing the error and effort minimization. A novel performance index based on human energy input enables real-time identification of the intended human sharing role. This index is used as an adaptive rate in the proposed algorithm to enhance the control system’s dynamic responsiveness to changes in human performance. The proposed approach achieves two main rehabilitation objectives. First, it encourages active and safe human participation. Second, it enhances the therapy by providing personalized assistance, tailored to individual abilities and conditions, and thus reduces the need for therapist intervention. The performance of the proposed approach is illustrated in experimental studies. The results demonstrate the adaptability of the algorithm, ensuring compliant and safe interaction and effective task completion. Note to Practitioners—In a human-robot cooperation (HRC) framework, the automatic adaptation of the robot’s role as well as safe and stable interaction are crucial. These aspects are amplified in the context of robotic rehabilitation due to the special conditions of the human participants. Classic control methods, in shared control, lack system intelligence and automation in role allocation. However, the shared role of humans in HRC, particularly in rehabilitation applications, introduces real-time and unpredictable variations. This study addresses the shortcomings of classic control methods, by integrating intelligence into the control system through an adaptive Neural Network algorithm in shared autonomy. To emulate human-like adaptability, two crucial aspects are considered. Firstly, it incorporates safety assurance embedded in the adaptive algorithm via Lyapunov-based adaptation. Secondly, it detects the human’s role within the control loop through a novel energy-based performance index, which views the human as an active contributor to the system’s dynamic energy flow. This ensures robust behavior by dynamically adjusting the trade-off between task completion and minimal robot intervention. A standout feature of our algorithm lies in its expendability to exoskeleton systems, making it highly versatile for use in robotic rehabilitation and assistive technologies. The algorithm’s design allows for straightforward integration with exoskeletons, requiring only interaction force measurements in the joint space. It facilitates monitoring of a patient’s performance in each joint using the proposed performance index based on the human energy entry into the system. Beyond rehabilitation, the algorithm’s ability to adjust autonomy levels through adaptation makes it applicable to a wide range of Human-Robot Cooperation scenarios where automatic role allocation is necessary. Preliminary experiments underscore the adaptive algorithm’s robust responsiveness to changes in human performance. Future investigations should involve clinical experiments addressing real-life challenges associated with various movement deficiencies and responding to real-time issues that may arise during rehabilitation sessions.

Robot-assisted rehabilitation is expected to reduce locomotor limitations of children and young adults with Cerebral Palsy (CP). However, to achieve this result, it is essential that the robot is transparent, allowing the user to move freely, and generate joint torques only when the exoskeleton joints significantly deviate from physiological gait patterns. Nevertheless, the development of transparent operation in lower-limb exoskeletons is still an open problem with several implementation challenges. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground exoskeleton designed for children and young adults with CP. The approach employs a feedback zero-torque controller with feedforward compensations for the exoskeleton's dynamics and actuators' impedance. We experimented the proposed system in five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff). The proposed transparent controller reduced the user-robot interaction torque and improved user gait kinematics relative to ExoOff. This is a significant step toward an overground gait training exoskeleton for CP population.