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1. Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements

   https://doi.org/10.3389/fnbot.2023.1127783  

   Dunkelberger, Nathan; Berning, Jeffrey; Schearer, Eric M.; O'Malley, Marcia K. (April 2023, Frontiers in Neurorobotics)

   IntroductionIndividuals who have suffered a cervical spinal cord injury prioritize the recovery of upper limb function for completing activities of daily living. Hybrid FES-exoskeleton systems have the potential to assist this population by providing a portable, powered, and wearable device; however, realization of this combination of technologies has been challenging. In particular, it has been difficult to show generalizability across motions, and to define optimal distribution of actuation, given the complex nature of the combined dynamic system. MethodsIn this paper, we present a hybrid controller using a model predictive control (MPC) formulation that combines the actuation of both an exoskeleton and an FES system. The MPC cost function is designed to distribute actuation on a single degree of freedom to favor FES control effort, reducing exoskeleton power consumption, while ensuring smooth movements along different trajectories. Our controller was tested with nine able-bodied participants using FES surface stimulation paired with an upper limb powered exoskeleton. The hybrid controller was compared to an exoskeleton alone controller, and we measured trajectory error and torque while moving the participant through two elbow flexion/extension trajectories, and separately through two wrist flexion/extension trajectories. ResultsThe MPC-based hybrid controller showed a reduction in sum of squared torques by an average of 48.7 and 57.9% on the elbow flexion/extension and wrist flexion/extension joints respectively, with only small differences in tracking accuracy compared to the exoskeleton alone. DiscussionTo realize practical implementation of hybrid FES-exoskeleton systems, the control strategy requires translation to multi-DOF movements, achieving more consistent improvement across participants, and balancing control to more fully leverage the muscles' capabilities. 

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2. Optimal Task-Invariant Energetic Control for a Knee-Ankle Exoskeleton

   https://doi.org/10.1109/LCSYS.2020.3043838  

   Lin, Jianping; Divekar, Nikhil V.; Lv, Ge; Gregg, Robert D. (December 2020, IEEE Control Systems Letters)

   null (Ed.)

   Task-invariant control methods for powered exoskeletons provide flexibility in assisting humans across multiple activities and environments. Energy shaping control serves this purpose by altering the human body’s dynamic characteristics in closed loop. Our previous work on potential energy shaping alters the gravitational vector to reduce the user’s perceived gravity, but this method cannot provide velocity-dependent assistance. The interconnection and damping assignment passivity-based control (IDA-PBC) method provides more freedom to shape a dynamical system’s energy through the interconnection structure of a port-controlled Hamiltonian system model. This paper derives a novel energetic control strategy based on IDA-PBC for a backdrivable knee-ankle exoskeleton. The control law provides torques that depend on various basis functions related to gravitational and gyroscopic terms. We optimize a set of constant weighting parameters for these basis functions to obtain a control law that produces able-bodied joint torques during walking on multiple ground slopes. We perform experiments with an able-bodied human subject wearing a knee-ankle exoskeleton to demonstrate reduced activation in certain lower-limb muscles. 

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3. Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation

   Zhang, Q. (October 2022, Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation)

   null (Ed.)

   Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialis anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDO-based DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time. 

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4. Design of a customizable, modular pediatric exoskeleton for rehabilitation and mobility

   https://doi.org/10.1109/SMC.2019.8914629  

   Eguren, David; Cestari, Manuel; Luu, Trieu Phat; Kilicarslan, Atilla; Steele, Alexander; Contreras-Vidal, Jose L. (October 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC))

   Powered exoskeletons for gait rehabilitation and mobility assistance are currently available for the adult population and hold great promise for children with mobility limiting conditions. Described here is the development and key features of a modular, lightweight and customizable powered exoskeleton for assist-as-needed overground walking and gait rehabilitation. The pediatric lower-extremity gait system (PLEGS) exoskeleton contains bilaterally active hip, knee and ankle joints and assist-as-needed shared control for young children with lower-limb disabilities such as those present in the Cerebral Palsy, Spina Bifida and Spinal Cord Injured populations. The system is comprised of six joint control modules, one at each hip, knee and ankle joint. The joint control module, features an actuator and motor driver, microcontroller, torque sensor to enable assist-as-needed control, inertial measurement unit and system monitoring sensors. Bench-testing results for the proposed joint control module are also presented and discussed. 

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5. Deep Neural Network Based Saturated Adaptive Control of Muscles in a Lower-Limb Hybrid Exoskeleton

   https://doi.org/10.1115/IMECE2023-112415  

   Ting, Jonathan A; Basyal, Sujata; Allen, Brendon C (October 2023, American Society of Mechanical Engineers)

   Hybrid exoskeletons are used to blend the rehabilitative efficacy and mitigate the shortcomings of functional electrical stimulation (FES) and exoskeleton-based rehabilitative solutions. This paper introduces a novel nonlinear controller that may potentially improve the rehabilitative efficiency of a lower limb hybrid exoskeleton by implementing four key features into the FES and exoskeleton controllers. First, the FES input to the user’s muscles is saturated based on user preference to ensure user comfort. Second, rather than discarding the excess control effort from the saturated FES input, it is redirected into the exoskeleton’s motor controller. Third, a safe deep neural network (DNN) is designed to estimate the unknown dynamics of the hybrid exoskeleton and the DNN is implemented in the FES controller to improve the control efficiency and tracking performance. Fourth, an adaptive update law is designed to estimate the unknown muscle control effectiveness to facilitate the implementation of the DNN. Lyapunov stability-based methods are used to generate real-time adaptive update laws that will train the adaptive estimate of the muscle effectiveness and the output layer weights of the DNN in real-time, ensure the effectiveness and safety of the controllers, and prove global asymptotic tracking of the desired trajectory. 

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