RESEARCH & PAPERS


Centroidal Momentum Shaping Control for Lower-Limb Exoskeletons

Conference Proceedings:


Task-Invariant Control of Lower-Limb Exoskeletons

Traditional control methodologies for rehabilitation orthoses/exoskeletons aim to replicate normative joint kinematics and thus fall into the category of kinematic control. This control paradigm depends on pre-defined reference trajectories, which can be difficult to adjust between different locomotor tasks and human subjects. These strategies tend to compensate for chronic deficits rather than enable training and recovery of normative gait. A paradigm shift from task-specific, kinematic control approaches to task-invariant, energetic control approaches is needed for wearable robots to assist their human users across activities. Therefore, we are investigating a novel control methodology for shaping the potential energy or total energy of the human body with wearable actuators. Because this control method does not depend on pre-defined kinematic patterns, it is ideally suited for task-invariant control of exoskeletons, both for performance augmentation and rehabilitation purposes. In this paradigm, wearable actuators can reduce mass/inertia parameters in body energetics to dynamically offload the weight of a stroke patient who otherwise would be supported by multiple therapists during gait rehabilitation. This innovation in dynamics and control will enable powered orthoses to assist humans in a variety of activities, which cannot be achieved with state-of-art control strategies based on pre-defined, task-specific joint kinematics.

Journal Articles:

Conference Proceedings:


Task-Invariant Learning Framework for Assisting Human Locomotion

Kinematic control approaches for exoskeletons follow specified trajectories, which overly constrain individuals who have partial or full volitional control of their lower-limbs. In our prior work, we proposed a general matching framework for underactuated energy shaping to provide task-invariant, energetic assistance. While our prior shaping strategies have demonstrated gait benefits such as reduced human joint torques, it remains unclear how the parameters of these shaping strategies will affect the overall torque reduction. Meanwhile, research shows that rapid, customized assistance can substantially improve exoskeleton’s performance for each individual. Motivated by this fact, we combine derivative-free, sample efficient optimization algorithms with our energy shaping strategies to propose a task-invariant learning framework for lower-limb exoskeletons. Through online optimization, exoskeletons can learn to adjust energy shaping parameters to minimize human joint torques across users and tasks. Simulation results show that the proposed framework finds the optimal parameters for given strategies that yield human torque and metabolic cost reduction. In addition, the optimal exoskeleton torques calculated using able-bodied subjects’ kinematic data closely match the real human joint torques.

Conference Proceedings:

  • Ge Lv, H. Xing, J. Lin, R. Gregg and C. Atkeson, “A Task-Invariant Learning Framework of Lower-Limb Exoskeletons for Assisting Human Locomotion”,  American Control Conference, Denver, 2020. 


    • Passivity-Based Control for Robust Bipedal Locomotion

    This research projects presents a passivity-based controller (PBC) based on a generalized energy expression in the storage function, which defines a novel passive output that accounts for the energy stored and dissipated by an arbitrary inner-loop controller. It is assumed that this inner-loop controller generates a stable limit cycle for the biped on a given slope. The outer-loop passivity-based PBC will then increase the basin of attraction, improve the robustness to the ground slope, and increase the rate of convergence back to the stable limit cycle. The control method is also shown to perform with an arbitrary degree of underactuation in the system.

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