Haptic Transparency and Interaction Force Control for a Lower-Limb Exoskeleton

Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase.Comment: 17 pages, 12 figure

Proof-of-concept of a Pneumatic Ankle Foot Orthosis Powered by a Custom Compressor for Drop Foot Correction

Pneumatic transmission has several advantages in developing powered ankle foot orthosis (AFO) systems, such as the flexibility in placing pneumatic components for mass distribution and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors that restrict them for being used as daily assistive devices. In this study, we improved a previously developed wearable (untethered) custom compressor that can be worn (1.5 kg) at the waist of the body and can generate adequate amount of pressurized air (maximum pressure of 1050 kPa and a flow rate of 15.1 mL/sec at 550 kPa) to power a unilateral active AFO used to assist the dorsiflexion (DF) motion of drop-foot patients. The finalized system can provide a maximum assistive torque of 10 Nm and induces an average 0.03 +/- 0.06 Nm resistive torque when free movement is provided. The system was tested for two unilateral drop-foot patients. The proposed system showed an average improvement of 13.6 degrees of peak dorsiflexion angle during the swing phase of the gait cycle.N

Pogo accumulator optimization based on multiphysics of liquid rockets and neural networks

In this study, a numerical analysis of pogo instability in liquid propulsion rockets was conducted and an optimization of the pogo suppressor was attempted. Pogo analysis was carried out using numerical results obtained via the major models for fuselage structure, feedline, and propulsion systems. In the structural system, the fuselage vibration modes were obtained and the relevant meta-model was constructed using the modal superposition method. To obtain accurate results for the hydraulic transmission line modeling, cavitation effects were also taken into account. Thus, a numerical analysis was performed on a pump inducer to provide the quantitative information of the cavitation volume in the liquid-oxygen feedline. By employing the rocket combustion equations, it was confirmed that the dynamic response was fed back to the longitudinal characteristics of the fuselage structure. In addition, an accumulator was installed to suppress pogo instability. For design optimization, an artificial neural network was suggested by performing Latin hypercube sampling. The sampling verifies the convergence by the learning process. Finally, a multi-objective optimization for the pogo accumulator was achieved with the present meta-model.Y