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1. Cycling With Functional Electrical Stimulation and Adaptive Neural Network Admittance Control

   Cousin, Christian; Deptula, Patryk; Rouse, Courtney; Dixon, Warren E. (July 2019, Proc. American Control Conference)

   For individuals with neurological conditions (NCs) affecting the muscles of their legs, motorized functional electrical stimulation (FES) cycling is a rehabilitation strategy which offers numerous health benefits. Motorized FES cycling is an example of cooperative physical human-robot interaction where both the cycle’s motor and rider’s muscles (through electrical stimulation) must be well controlled to achieve desired performance. Since every NC is unique, adaptive control of motorized FES cycling is motivated over a one-size-fits-all approach. In this paper, a robust sliding-mode controller is employed on the rider’s muscles while an adaptive neural network admittance controller is employed on the cycle’s motor to preserve rider comfort and safety. Through a Lyapunov-like switched systems stability analysis, global asymptotic stability of the cycle controller is guaranteed and the muscle controller is proven to be passive with respect to the cycle. Experiments on one able-bodied participant were conducted to validate the control design. 

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2. Cadence and Admittance Control of a Motorized Functional Electrical Stimulation Cycle

   https://doi.org/10.1109/CDC.2018.8618685  

   Cousin, Christian A.; Duenas, Victor H.; Rouse, Courtney A.; Dixon, Warren E. (December 2018, Proc. IEEE Conference on Decision and Control)

   Functional electrical stimulation (FES) can be combined with a motorized cycle to offer various rehabilitation options for individuals with neurological conditions. Typically, FES cycling controllers use cooperating muscles and an electric motor to track cadence. In this paper, in addition to cooperative cadence tracking, the motorized cycle tracks an admittance trajectory generated using torque feedback. This method allows the cycle to deviate from the desired cadence trajectory and admit to the rider-applied torque, ensuring safe human-machine interaction. Two sets of uncertain, nonlinear dynamics are presented, one for the human rider and one for the robot, linked by a common measurable interaction torque. After developing cadence and admittance controllers, a Lyapunov-like switched system stability analysis is provided to prove global exponential tracking of the cadence error system, and a passivity analysis is conducted to prove passivity of the cycle’s admittance controller with respect to the rider’s interaction torque. *Note this paper does not properly cite the specific project. 

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3. Concurrent Learning Augmented DNN-Based and Admittance Control of a Hybrid Exoskeleton

   https://doi.org/10.1109/ICORR66766.2025.11063179  

   Basyal, Sujata; Mishra, Kislaya; Ting, Jonathan; Allen, Brendon (May 2025, IEEE)

   People suffering from neurological conditions (NCs) can benefit from motorized functional electrical stimulation (FES)-based rehabilitation equipment, called hybrid exoskeletons. These hybrid exoskeletons incorporate muscle-motor interaction that requires both the control of human muscles (i.e., FES) and robot motors to obtain a desirable performance. Two types of controllers (deep neural networks (DNN)-based and Admittance-based) were developed in this paper for a hybrid exoskeleton to control both human muscles and the exoskeleton’s motors. The uncertain dynamics of the hybrid exoskeleton are approximated by DNN to enable efficient FES control. The approximated DNN weights and biases were implemented in a control law where they were updated in multiple timescales. Specifically, the inner-layer DNN weights were updated iteratively offline while the outer-layer weights were updated online in real-time. The update law for the output-layer DNN weights was augmented with a concurrent learning (CL) inspired term to improve the learning performance of the DNN and, consequently, the overall system performance. The admittance-based motor controller uses torque feedback and desired torque contribution from the participant to modify the motor’s desired trajectory without forcing the participant to follow along predetermined trajectories and to promote the overall safety of the system. A Lyapunov-based stability analysis was completed for both control systems to ensure overall system performance. 

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4. Admittance Control of a Teleoperated Motorized Functional Electrical Stimulation Rehabilitation Cycle

   K. J. Stubbs, B. C. (December 2020, IFAC Conference on Cyber-Physical Human-Systems)

   null (Ed.)

   A bilateral teleoperated rehabilitation cycling system is developed for people with movement impairments due to various neurological disorders. A master hand-cycling device is used by the operator to set the desired position and cadence of a lower-body functional electrical stimulation (FES) controlled and motor assisted recumbent cycle. The master device also uses kinematic haptic feedback to reflect the lower-body cycle's dynamic response to the operator. To accommodate for the unknown nonlinear dynamics inherent to physical human machine interaction (pHMI), admittance controllers were developed to indirectly track desired interaction torques for both the haptic feedback device and the lower-body cycle. A robust position and cadence controller, which is only active within the regions of the crank cycle where FES produces sufficient torque values, was used to determine the FES intensity. A Lyapunov analysis is used to prove the robust FES controller yields global exponential tracking to the desired position and cadence set by the master device within FES stimulation regions. Outside of the FES regions, the admittance controllers at the hands and legs work in conjunction to produce desired performance. Both admittance controllers were analyzed for the entire crank cycle, and found to be input/output strictly passive and globally exponentially stable in the absence of human effort, despite the uncertain nonlinear dynamics. 

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5. Stable Cadence Tracking of Admitting Functional Electrical Stimulation Cycle

   https://doi.org/10.1115/DSCC2018-8989  

   Cousin, Christian A.; Duenas, Victor H.; Rouse, Courtney A.; Dixon, Warren E. (September 2018, Proc. ASME Dynamic Systems and Control Conference)

   Rehabilitation robotics and functional electrical stimulation (FES) are two promising methods of rehabilitation for people with neurological disorders. In motorized FES cycling, both the rider and the motorized cycle must be controlled for cooperative human-machine interaction. While rehabilitation goals vary widely, FES cycling traditionally rejects rider disturbances to accomplish cadence and power tracking; however, this paper ensures that the cycle accommodates the rider without rejecting rider disturbances as a means to promote function and strength recovery while ensuring rider safety. A cadence and admittance controller are developed to activate the cycle’s electric motor and the rider’s leg muscles through FES when kinematically efficient. Using a single set of combined cycle-rider dynamics, a Lyapunov-like switched systems analysis is conducted to conclude global exponential cadence tracking. A subsequent passivity analysis is conducted to show the admittance controller is passive with respect to the rider. For a desired cadence of 50 RPM, preliminary experiments on one able-bodied participant and one participant with spina bifida demonstrate tracking errors of −0.07±2.59 RPM and −0.20±3.86 RPM, respectively. *Note this paper does not properly cite the specific NSF project number. 

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