This paper presents the design and implementation of a novel multi-activity control strategy for a backdrivable knee-ankle exoskeleton. Traditionally, exoskeletons have used trajectory-based control of highly geared actuators for complete motion assistance. In contrast, we develop a potential energy shaping controller with ground reaction force (GRF) feedback that facilitates multi-activity assistance from a backdrivable exoskeleton without prescribing pre-defined kinematics. Although potential energy shaping was previously implemented in an exoskeleton to reduce the user’s perceived gravity, this model-based approach assumes the stance leg is fully loaded with the weight of the user, resulting in excessive control torques as weight transfers to the contralateral leg during double support. The presented approach uses GRF feedback to taper the torque control output for any activity involving multiple supports, leading to a closer match with normative joint moments in simulations based on pre-recorded human data during level walking. To implement this strategy, we present a custom foot force sensor that provides GRF feedback to the previously designed exoskeleton. Finally, results from an able-bodied human subject experiment demonstrate that the exoskeleton is able to reduce muscular activation of the primary muscles related to the knee and ankle joints during sit-to-stand, stand-to-sit, level walking, and stair climbing.
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An Energy Shaping Exoskeleton Controller for Human Strength Amplification
In this work, we introduce a novel approach to assistive exoskeleton (or powered orthosis) control which avoids needing task and gait phase information. Our approach is based on directly designing the Hamiltonian dynamics of the target closed-loop behavior, shaping the energy of the human and the robot. Relative to previous energy shaping controllers for assistive exoskeletons, we introduce ground reaction force and torque information into the target behavior definition, reformulate the kinematics so as to avoid explicit matching conditions due to under-actuation, and avoid the need to switch between swing and stance energy shapes. Our controller introduces new states into the target Hamiltonian energy that represent a virtual second leg that is connected to the physical leg using virtual springs. The impulse the human imparts to the physical leg is amplified and applied to the virtual leg, but the ground reaction force acts only on the physical leg. A state transformation allows the proposed control to be available using only encoders, an IMU, and ground reaction force sensors. We prove that this controller is stable and passive when acted on by the ground reaction force and demonstrate the controller's strength amplifying behavior in a simulation. A linear analysis based on small signal assumptions allows us to explain the relationship between our tuning parameters and the frequency domain amplification bandwidth.
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- PAR ID:
- 10297689
- Date Published:
- Journal Name:
- Proceedings of the IEEE Conference on Decision Control
- ISSN:
- 0743-1546
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks.more » « less
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Task-invariant feedback control laws for powered exoskeletons are preferred to assist human users across varying locomotor activities. This goal can be achieved with energy shaping methods, where certain nonlinear partial differential equations, i.e., matching conditions, must be satisfied to find the achievable dynamics. Based on the energy shaping methods, open-loop systems can be mapped to closed-loop systems with a desired analytical expression of energy. In this paper, the desired energy consists of modified potential energy that is well-defined and unified across different contact conditions along with the energy of virtual springs and dampers that improve energy recycling during walking. The human-exoskeleton system achieves the input-output passivity and Lyapunov stability during the whole walking period with the proposed method. The corresponding controller provides assistive torques that closely match the human torques of a simulated biped model and able-bodied human subjects’ data.more » « less
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Abstract Background Maintaining upright posture is an unstable task that requires sophisticated neuro-muscular control. Humans use foot–ground interaction forces, characterized by point of application, magnitude, and direction to manage body accelerations. When analyzing the directions of the ground reaction forces of standing humans in the frequency domain, previous work found a consistent pattern in different frequency bands. To test whether this frequency-dependent behavior provided a distinctive signature of neural control or was a necessary consequence of biomechanics, this study simulated quiet standing and compared the results with human subject data.
Methods Aiming to develop the simplest competent and neuromechanically justifiable dynamic model that could account for the pattern observed across multiple subjects, we first explored the minimum number of degrees of freedom required for the model. Then, we applied a well-established optimal control method that was parameterized to maximize physiologically-relevant insight to stabilize the balancing model.
Results If a standing human was modeled as a single inverted pendulum, no controller could reproduce the experimentally observed pattern. The simplest competent model that approximated a standing human was a double inverted pendulum with torque-actuated ankle and hip joints. A range of controller parameters could stabilize this model and reproduce the general trend observed in experimental data; this result seems to indicate a biomechanical constraint and not a consequence of control. However, details of the frequency-dependent pattern varied substantially across tested control parameter values. The set of parameters that best reproduced the human experimental results suggests that the control strategy employed by human subjects to maintain quiet standing was best described by minimal control effort with an emphasis on ankle torque.
Conclusions The findings suggest that the frequency-dependent pattern of ground reaction forces observed in quiet standing conveys quantitative information about human control strategies. This study’s method might be extended to investigate human neural control strategies in different contexts of balance, such as with an assistive device or in neurologically impaired subjects.
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A biologically-inspired actuation system, including muscles, spinal reflexes, and vestibular feedback, may be capable of achieving more natural gait mechanics in powered prostheses or exoskeletons. In this study, we developed a Virtual Muscle Reflex (VMR) system to control ankle torque and tuned it using data from human responses to anteroposterior mechanical perturbations at three walking speeds. The system consists of three Hill-Type muscles, simulated in real time, and uses feedback from ground reaction force and from stretch sensors on the virtual muscle fibers. Controller gains, muscle properties, and reflex/vestibular time delays were optimized using Covariance Matrix Adaptation (CMA) to minimize the difference between the VMR torque output and the torque measured from the experiment. We repeated the procedure using a conventional finite-state impedance controller. For both controllers, the coefficient of determination ([Formula: see text]) and root-mean-square error (RMSE) was calculated as a function of time within the gait cycle. The VMR had lower RMSE than the impedance controller in 70%, and in 60% of the trials, the [Formula: see text] of the VMR controller was higher than for the impedance controller. We concluded that the VMR system can better reproduce the human responses to perturbations than the impedance controller.
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