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

Naji A Alibeji, Vahidreza Molazadeh, Brad E Dicianno, Nitin Sharma, Naji A Alibeji, Vahidreza Molazadeh, Brad E Dicianno, Nitin Sharma

Abstract

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.

Keywords: Lyapunov; hybrid neuroprosthesis; neuromuscular stimulation; nonlinear control; synergy.

Figures

Figure 1
Figure 1
A 4-link gait model is used to represent a subject taking a step in a hybrid neuroprosthesis while using a walker.
Figure 2
Figure 2
The dynamic postural synergies computed through the optimizations and the dynamic postures they result in when activated.
Figure 3
Figure 3
(A) The dynamic postural synergies (a) and their activation to produce a half step (b), (B) the joint trajectories they produce, (C) the gait sequence for the half step.
Figure 4
Figure 4
(A) The dynamic postural synergies (a) and their activation to produce a full step (b), (B) the joint trajectories they produce, (C) the gait sequence for the full step.
Figure 5
Figure 5
The control schematic for the implementation of the overall controller.
Figure 6
Figure 6
The Finite State Machine determines the desired trajectories and synergy activations based on what state is activated; either half right step, full left step, or full right step. Then two controllers are used, one for each leg, which work in tandem to produce gait.
Figure 7
Figure 7
The walking hybrid neuroprosthesis and the gait support device used in the experimental demonstration of the synergy-based control system. This system uses an electric motor at the hip and knee joints of each leg and FES of the hamstrings and quadriceps muscle group of each leg.
Figure 8
Figure 8
(A) The desired and actual joint angles of the right and left hip and knee joints resulting from using the developed synergy-based DSC/DC control system in conjunction with the FSM on a subject with an incomplete SCI. The shaded regions indicate which state of the FSM is active at that time. (B) A sequence of photos illustrating the gait produce during the experiments. The depicted individual provided written and informed consent for the publication of this image.
Figure 9
Figure 9
The desired feedforward component of μ¯ for all of the system inputs. This component is generated from the dynamic postural synergies and their activation after adaptation and with the scaling up from the fatigue estimate and the scaling factor control gain.
Figure 10
Figure 10
The desired feedback component of μ¯ which is only applied to the four motors at the hip and knee joints of each leg. It can be observed that they majority of the effort is occurring during the swing phase of each leg.
Figure 11
Figure 11
The fatigue estimates for the knee flexors and extensors of the right leg. The fatigue estimate ranges from 1 to ϕmin, which corresponds to no fatigue to fully fatigued, respectively. It can be observed that the fatigue occurs during the swing phase, and the muscles recover during the stance phase since there is no stimulation.
Figure 12
Figure 12
The inputs to all of the system inputs, including feedback and feedforward, for this experimental trial. Note that there is no stimulation occurring during the stance phase of each leg.

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