Iterative Learning Control Methods for Hybrid Wearable Robots

In this dissertation, iterative learning control methods for a hybrid exoskeleton to produce sitting-to-standing and walking in people with paraplegia are investigated. The hybrid exoskeleton combines a lower limb powered exoskeleton and functional electrical stimulation (FES). Limited research has been done to design control methods that provide shared modulation of FES and the powered exoskeleton. A major technical challenge to the implementation of control algorithms is their need to identify a user's musculoskeletal dynamics. Further, currently, setting desired regulation points or desired limb trajectories during sitting-to-standing and walking movements is a daunting task as it requires separate and coordinated design for each lower-limb. An inaccurate regulation of set-points or desired trajectories can possibly cause uncoordinated standing-up movements, potentially destabilizing the user. Goal: The goal of this research is to design robust and adaptive control algorithms for hybrid exoskeletons that overcome the difficulty in model identification, can dynamically allocate the shared use of FES and the powered exoskeleton, and produce coordinated joint movements. Objectives: The primary objective of this research is to develop robust control methods that iteratively learn modeling uncertainties in the hybrid exoskeleton (i.e., addressing model identification), while facilitating allocation of FES and motor input (i.e., resolving actuator redundancy) in the hybrid exoskeleton. The proposed control methods are experimentally validated for a sitting to standing task with the hybrid exoskeleton. The experiments are performed on human participants with no disabilities and a participant with spinal cord injury. The tasks that are accomplished to achieve the objectives are listed as: 1- Design and implement time-invariant desired joint trajectories by using virtual constraints for sitting-to-standing and walking motion 2- Derive and experimentally validate a robust control method that uses an arbitrarily switched allocation strategy to coordinate motor and FES. 3- Derive a control method that iteratively learns the system nonlinear dynamics and control gains. 4- Using an optimal and cooperative model predictive control method, instead of switched control, to allocate between motors and FES

A Control Scheme That Uses Dynamic Postural Synergies to Coordinate a Hybrid Walking Neuroprosthesis: Theory and Experiments

A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury

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<p>A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury.</p