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1. Cadence Tracking for Switched FES-Cycling With Unknown Time-Varying Input Delay

   https://doi.org/10.1115/DSCC2019-9078  

   Allen, B. ; Cousin, C. ; Rouse, C. ; Dixon, W. ( October 2019 , ASME 2019 Dynamic Systems and Control Conference)

   Functional electrical stimulation (FES) induced exercise, such as motorized FES-cycling, is commonly used in rehabilitation for lower limb movement disorders. A challenge in closed-loop FES control is the presence of an input delay between the application (and removal) of the electrical stimulus and the production of muscle force. Moreover, switching between motor control and FES control of various muscle groups can be destabilizing. This paper examines the development of a control method and state-dependent trigger condition to account for the time-varying input delayed response. Uniformly ultimately bounded tracking for a switched uncertain nonlinear dynamic system with input delays is achieved. 

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2. Cadence Tracking for Switched FES Cycling with Unknown Input Delay

   Allen, B. ; Cousin, C. ; Rouse, C. ; Dixon, W. ( January 2019 , 2019 IEEE 58th Conference on Decision and Control (CDC))

   Functional electrical stimulation (FES) induced cycling provides a means of therapeutic exercise and functional restoration for people affected by neuromuscular disorders. A challenge in closed-loop FES control of coordinated motion is the presence of a potentially destabilizing input delay between the application of the electrical stimulation and the resulting muscle contraction. Moreover, switching amongst multiple actuators (e.g., between FES control of various muscle groups and a controlled electric motor) presents additional challenges for overall system stability. In this paper, a closed-loop controller is developed to yield exponential cadence tracking, despite an unknown input delay, switching between FES and motor only control, uncertain nonlinear dynamics, and additive disturbances. Lyapunov-Krasovskii functionals are used in a Lyapunov-based stability analysis to ensure exponential convergence for all time. 

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3. 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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4. 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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5. Adaptive Trajectory Tracking During Motorized and FES-Induced Biceps Curls via Integral Concurrent Learning

   https://doi.org/10.1115/DSCC2020-3125  

   Allen, Brendon C. ; Stubbs, Kimberly J. ; Dixon, Warren E. ( October 2020 , ASME Dynamic Systems and Control Conference)

   null (Ed.)

   A common rehabilitative technique for those with neuro-muscular disorders is functional electrical stimulation (FES) induced exercise such as FES-induced biceps curls. FES has been shown to have numerous health benefits, such as increased muscle mass and retraining of the nervous system. Closed-loop control of a motorized FES system presents numerous challenges since the system has nonlinear and uncertain dynamics and switching is required between motor and FES control, which is further complicated by the muscle having an uncertain control effectiveness. An additional complication of FES systems is that high gain feedback from traditional robust controllers can be uncomfortable to the participant. In this paper, data-based, opportunistic learning is achieved by implementing an integral concurrent learning (ICL) controller during a motorized and FES-induced biceps curl exercise. The ICL controller uses adaptive feedforward terms to augment the FES controller to reduce the required control input. A Lyapunov-based analysis is performed to ensure exponential trajectory tracking and opportunistic, exponential learning of the uncertain human and machine parameters. In addition to improved tracking performance and robustness, the potential of learning the specific dynamics of a person during a rehabilitative exercise could be clinically significant. Preliminary simulation results are provided and demonstrate an average position error of 0.14 ± 1.17 deg and an average velocity error of 0.004 ± 1.18 deg/s. 

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