Vahidreza Molazadeh’s research while affiliated with National Institutes of Health and other places

Strength and selective motor control are primary determinants of pathological gait in children with cerebral palsy (CP) and other neuromotor disorders. Emerging evidence suggests robotic application of task-specific resistance to functional movements may provide the opportunity to strengthen muscles and improve neuromuscular function during walking in children with CP. Such a strategy could be most beneficial to children who are more severely affected by the pathology but their ability to overcome such resistance and maintain functional ambulation remains unclear. The goal of this study was to design, validate and evaluate initial feasibility and effects of a novel exoskeleton strategy that provides interleaved assistance and resistance to knee extension during overground walking. One participant with CP (GMFCS III) was recruited and completed ten total visits, nine walking with the exoskeleton. Our results validated the controller's ability to parse the gait cycle into five discrete phases (mean accuracy 91%) and provide knee extension assistance during stance and resistance during swing. Following acclimation to the interleaved strategy, peak knee extension was significantly improved in both the left (mean 7.9 deg) and right (15.2 deg) limbs when walking with the exoskeleton. Knee extensor EMG during late swing phase increased to 2.7 (left leg) and 1.7 (right leg) times the activation level during baseline exoskeleton walking without resistance. These results indicate that this interleaved strategy warrants further investigation in a longitudinal intervention study, particularly in individuals who may be more severely affected such that they are unable to ambulate overground using an exoskeleton training strategy that only deploys targeted resistance to limb motion.

A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.

A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions through FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this article, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.

In this paper, a switched control method for a class of wearable robotic systems that prioritizes the use of human skeletal muscles in an assistive rigid powered exoskeleton is derived. A general N-degree-of-freedom (N-DOF) human-robot model is proposed to consider the challenges induced by the wearable system that include uncertainties and nonlinearities, unilateral actuation properties of the skeletal muscles, input delays, as well as a time varying actuator efficiency. Two control modes that alternatively switch and control a wearable robotic system are designed to overcome these challenges. A multiple Lyapunov functional analysis with state-dependent constraints on the switch criteria is performed to prove the stability. Simulations are performed to demonstrate the gain conditions, selected for each subsystem, that stabilize the overall system. Experiments on a human participant wearing a 4-DOF hybrid exoskeleton that combines functional electrical stimulation and a powered exoskeleton demonstrate the effectiveness of the switched control design.

In this paper a lower-limb powered exoskeleton is combined with functional electrical stimulation of the quadriceps muscle to achieve a standing-up motion. As two actuation mechanisms (FES and the motors) act on the knee joints, it is desirable to optimally coordinate them. A feedback controller that stabilizes the desired standing-up motion is derived. The knee torques, computed by the feedback controller, are further distributed to FES and the knee electric motors by using a ratio allocation that is solved via a model predictive control method. The optimization method relies on a fatigue dynamical model. Simulations and the experimental results of the ratio allocation approach are reported for the standing-up motion.

Currently controllers that dynamically modulate functional electrical stimulation (FES) and a powered exoskeleton at the same time during standing-up movements are largely unavailable. In this paper, an optimal shared control of FES and a powered exoskeleton is designed to perform sitting to standing (STS) movements with a hybrid exoskeleton. A hierarchical control design is proposed to overcome the difficulties associated with developing an optimal real-time solution for the highly nonlinear and uncertain STS control model with multiple degrees of freedom. A higher-level robust nonlinear control design is derived to exponentially track a time-invariant desired STS movement profile. Then, a lower-level optimal control allocator is designed to distribute control between FES and the knee electric motors. The allocator uses a person’s muscle fatigue and recovery dynamics to determine an optimal ratio between the FES-elicited knee torque and the exoskeleton assist. Experiments were performed on human participants, two persons without disability and one person with spinal cord injury (SCI), to validate the feedback controller and the optimal torque allocator. The muscles of the participant with SCI did not actively contract to FES, so he was only tested with the powered exoskeleton controller. The experimental results show that the proposed hierarchical control design is a promising method to effect shared control in a hybrid exoskeleton.

In this paper, a novel neural network based iterative learning controller for a hybrid exoskeleton is presented. The control allocation between functional electrical stimulation and knee electric motors uses a model predictive control strategy. Further to address modeling uncertainties, the controller identifies the system dynamics and input gain matrix with neural networks in an iterative fashion. Virtual constraints are employed so that the system can use a time invariant manifold to determine desired joint angles. Simulation results show that the controller stabilizes the hybrid system for sitting to standing and standing to sitting scenarios.

In this paper, a robust iterative learning switching controller that uses optimal virtual constraint is designed for a hybrid walking exoskeleton that uses functional electrical stimulation and a powered exoskeleton. The synthesis of iterative learning control with sliding-mode control improves tracking performance and accuracy. The motivation for designing this switching controller was to obtain joint torques either from functional electrical stimulation or electric motor. A generalized switching controller is utilized to switch based on the stimulated muscle fatigue state. For achieving stability in walking cycle, the controller is used to force the system to follow the designed virtual constraints. The combination of sequential quadratic programming and genetic-particle swarm optimization algorithm is used for deriving the virtual constraints. The effectiveness of the new iterative learning control for output tracking is verified in a simple model of walking (3-link) that has active actuation at the hip joints.

Mobility disorders caused by spinal cord injury (SCI), stroke, or progressive neurological diseases such as multiple sclerosis and amyotrophic lateral sclerosis, lead to a deterioration in quality of life. Resulting sequelae, such as pressure ulcers, depression, and urinary infections, require constant medical care throughout a patient's lifetime. Evidence has shown that, following an injury or a disease, individuals who use rehabilitative interventions to restore walking and standing functions experience fewer secondary medical complications than do wheelchair users [1]. Two such rehabilitative interventions include functional electrical stimulation (FES) and powered exoskeletons. These technologies have the potential to mitigate secondary health complications, lower medical expenses, and achieve independent ambulation in individuals following an SCI.