This paper introduces intelligent central pattern generators (iCPGs) that can plan personalized walking trajectories for lower-limb exoskeletons. This can make walking more comfortable for the users by resolving one of the significant shortcomings of most commercially available exoskeletons, which is the use of pre-defined fixed trajectories for all users. The proposed method combines reinforcement learning (RL) with previously introduced adaptable central pattern generators (ACPGs) to learn a user's physical interaction behaviour and refine the exoskeleton's walking trajectories. The ACPG method embeds physical human-robot interaction (pHRI) in CPGs to make changing gait trajectories in real-time, possible. However, to effectively refine gait trajectories based on pHRIs, the parameters must be precisely identified and updated as a user interacts with the exoskeleton. Our proposed method uses RL to modify (amplify/attenuate) the pHRI energy based on a user's interaction behaviour, and form an effective energy value which can facilitate reaching desired gait pattern for users via iCPG dynamics. The proposed method can resolve the aforementioned challenges with ACPGs and personalized trajectory generation. The simulation and experimental results provide evidence that the proposed method can effectively adapt to the user's behaviour in different walking scenarios with the Indego lower-limb exoskeleton.

In this article, a new hybrid controller is investigated for the immune response and the human cytomegalovirus (HCMV) infection as two substantial factors in renal transplanted recipients’ treatment. To reduce the risk of kidney transplantation rejection, it is necessary to decrease the immune system level in a patient’s body by suppressing immune cells. However, this situation increases the risk of infection with HCMV. The proposed nonlinear control strategy balances the immune response and antiviral treatment to guarantee kidney transplantation success and prevent infection by tracking demanded values. The desired scenarios are designed by evaluating several Bezier curves and exponential functions and investigating the system behavior. Coupled nonlinear dynamics of the immune response and HCMV infection are studied in three modes, including immunosuppressive treatment, dual treatment, and antiviral cut-off modes. Asymptotic stability of the closed-loop system is proven using the Lyapunov theorem and Barbalat’s lemma in all of the three modes in the presence of uncertainties on model parameters. The effectiveness of the developed control scheme is investigated using several simulation studies.

In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.