Active stability observer using artificial neural network for intuitive physical human–robot interaction

Physical human-robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this paper aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: 1) a statistical analysis of a sensor signal (force and velocity) and 2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations

Methods for Multiloop Identification of Visual and Neuromuscular Pilot Responses

In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails

Integral admittance shaping: A unified framework for active exoskeleton control

© 2015 Elsevier B.V. Current strategies for lower-limb exoskeleton control include motion intent estimation, which is subject to inaccuracies in muscle torque estimation as well as modeling error. Approaches that rely on the phases of a uniform gait cycle have proven effective, but lack flexibility to aid other kinds of movement. This research aims at developing a more versatile control that can assist the lower limbs independently of the movement attempted. Our control strategy is based on modifying the dynamic response of the human limbs, specifically their mechanical admittance. Increasing the admittance makes the lower limbs more responsive to any muscle torque generated by the human user. We present Integral Admittance Shaping, a unified mathematical framework for: (a) determining the desired dynamic response of the coupled system formed by the human limb and the exoskeleton, and (b) synthesizing an exoskeleton controller capable of achieving said response. The present control formulation focuses on single degree-of-freedom exoskeleton devices providing performance augmentation. The algorithm generates a desired shape for the frequency response magnitude of the integral admittance (torque-to-angle relationship) of the coupled system. Simultaneously, it generates an optimal feedback controller capable of achieving the desired response while guaranteeing coupled stability and passivity. The potential effects of the exoskeleton's assistance are motion amplification for the same joint torque, and torque reduction for the same joint motion. The robustness of the derived exoskeleton controllers to parameter uncertainties is analyzed and discussed. Results from initial trials using the controller on an experimental exoskeleton are presented as well

End-point Impedance Measurements at Human Hand during Interactive Manual Welding with Robot

This paper presents a study of end-point impedance measurement at human hand, with professional and novice manual welders when they are performing Tungsten Inert Gas (TIG) welding interactively with the KUKA Light Weight Robot Arm (LWR). The welding torch is attached to the KUKA LWR, which is admittance controlled via a force sensor to give the feeling of a free floating mass at its end-effector. The subjects perform TIG welding on 1.5 mm thick stainless steel plates by manipulating the torch attached to the robot. The end-point impedance values are measured by introducing external force disturbances and by fitting a mass-damper-spring model to human hand reactions. Results show that, for professionals and novices, the mass, damping and stiffness values in the direction perpendicular to the welding line are the largest compared to the other two directions. The novices demonstrate less resistance to disturbances in this direction. Two of the professionals present larger stiffness and one of them presents larger damping. This study supports the hypothesis that impedance measurements could be used as a partial indicator, if not direct, of skill level to differentiate across different levels of manual welding performances. This work contributes towards identifying tacit knowledge of manual welding skills by means of impedance measurements

A MECHANISTIC APPROACH TO POSTURAL DEVELOPMENT IN CHILDREN

Upright standing is intrinsically unstable and requires active control. The central nervous system's feedback process is the active control that integrates multi-sensory information to generate appropriate motor commands to control the plant (the body with its musculotendon actuators). Maintaining standing balance is not trivial for a developing child because the feedback and the plant are both developing and the sensory inputs used for feedback are continually changing. Knowledge gaps exist in characterizing the critical ability of adaptive multi-sensory reweighting for standing balance control in children. Furthermore, the separate contributions of the plant and feedback and their relationship are poorly understood in children, especially when considering that the body is multi-jointed and feedback is multi-sensory. The purposes of this dissertation are to use a mechanistic approach to study multi-sensory abilities of typically developing (TD) children and children with Developmental Coordination Disorder (DCD). The specific aims are: 1) to characterize postural control under different multi-sensory conditions in TD children and children with DCD; 2) to characterize the development of adaptive multi-sensory reweighting in TD children and children with DCD; and, 3) to identify the plant and feedback for postural control in TD children and how they change in response to visual reweighting. In the first experiment (Aim 1), TD children, adults, and 7-year-old children with DCD are tested under four sensory conditions (no touch/no vision, with touch/no vision, no touch/with vision, and with touch/with vision). We found that touch robustly attenuated standing sway in all age groups. Children with DCD used touch less effectively than their TD peers and they also benefited from using vision to reduce sway. In the second experiment (Aim 2), TD children (4- to 10-year-old) and children with DCD (6- to 11-year-old) were presented with simultaneous small-amplitude touch bar and visual scene movement at 0.28 and 0.2 Hz, respectively, within five conditions that independently varied the amplitude of the stimuli. We found that TD children can reweight to both touch and vision from 4 years on and the amount of reweighting increased with age. However, multi-sensory fusion (i.e., inter-modal reweighting) was only observed in the older children. Children with DCD reweight to both touch and vision at a later age (10.8 years) than their TD peers. Even older children with DCD do not show advanced multisensory fusion. Two signature deficits of multisensory reweighting are a weak vision reweighting and a general phase lag to both sensory modalities. The final aim involves closed-loop system identification of the plant and feedback using electromyography (EMG) and kinematic responses to a high- or low-amplitude visual perturbation and two mechanical perturbations in children ages six and ten years and adults. We found that the plant is different between children and adults. Children demonstrate a smaller phase difference between trunk and leg than adults at higher frequencies. Feedback in children is qualitatively similar to adults. Quantitatively, children show less phase advance at the peak of the feedback curve which may be due to a longer time delay. Under the high and low visual amplitude conditions, children show less gain change (interpreted as reweighting) than adults in the kinematic and EMG responses. The observed kinematic and EMG reweighting are mainly due to the different use of visual information by the central nervous system as measured by the open-loop mapping from visual scene angle to EMG activity. The plant and the feedback do not contribute to reweighting

Measuring pilot control behavior in control tasks with haptic feedback

The research goal of this thesis was to increase the understanding of effects of haptic feedback on human’s performance and control behavior. Firstly, we investigated the effectiveness of haptic aids on improving human’s performance in different control scenarios. Beneficial effects of haptic aids were shown in terms of human's performances and control effort. Comparisons with input-mixing systems showed that, although input-mixing systems yielded better performance than haptic aids in nominal conditions, participants recovered better from failures of haptic systems than from failures of input-mixing aids. Secondly, we investigated how humans adapt their dynamic responses to realize benefits of the haptic feedback. To achieve this goal, we developed novel identification methods to estimate human's neuromuscular dynamics in a multi-loop control task. The novel methods assumed a time-invariant behavior of humans responses. The novel methods were validated in simulation and applied to experimental data. Finally, novel methods were developed to account for time-varying behavior of human's responses. Different sets of numerical simulations were used to validate the novel methods. Then, the methods were applied to data obtained in human in-the-loop experiments