Interaction Torque Research Articles

Gait self-learning control based on reference trajectory generation online for an asymmetric limb rehabilitation exoskeleton

Lower limb exoskeleton (LEX) are widely used to assist stoke survivors with walking dysfunction, which is lack of a more flexible trajectory and fails to address the control challenge posed by gait variability and asymmetry in rehabilitation training. This paper introduces an asymmetric self-learning lower exoskeleton (AS-LEX) based on reference trajectory generation for the affected side. Motor intent of the unaffected limb based on thresholds was identified to classify the gait phase of stance and swing. A parameterized gait trajectory was generated online, namely a combination of circular trajectory in the stance phase and an elliptical trajectory in the swing phase. Gait self-learning control is presented to make the affected limb adaptively learn the gait parameters generated by the unaffected limb. Feasibility of the AS-LEX is demonstrated experimentally using three healthy subjects. Resuls demonstrate that overground walking in a more natural speed (with a stride length 600 mm and 700 mm) make subjects more actively learn gait of the affected side from the unaffected side. Additionally, experiments of the fatigue level of the affected limb and human-robot interaction torques were carried out, and the results indicate a more natural gait and reduced interaction forces with the AS-LEX.

Human–exoskeleton interaction portrait

Human–robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human–robot interaction and co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We introduce the interaction portrait (IP), which visualizes this variable’s distribution in polar coordinates. We applied IP to compare a recently developed hybrid torque controller (HTC) based on kinematic state feedback and a novel adaptive model-based torque controller (AMTC) with online learning, proposed herein, against a time-based controller (TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and AMTC significantly lower users’ normalized oxygen uptake, suggesting enhanced user-exoskeleton coordination. IP analysis reveals that this improvement stems from two distinct co-adaptation strategies, unidentifiable by traditional muscle activity or interaction torque analyses alone. HTC encourages users to yield control to the exoskeleton, decreasing overall muscular effort but increasing interaction torque, as the exoskeleton compensates for user dynamics. Conversely, AMTC promotes user engagement through increased muscular effort and reduces interaction torques, aligning it more closely with rehabilitation and gait training applications. IP phase evolution provides insight into each user’s interaction strategy formation, showcasing IP analysis’s potential in comparing and designing novel controllers to optimize human–robot interaction in wearable robots.

Adaptive Modular Neural Control for Online Gait Synchronization and Adaptation of an Assistive Lower-Limb Exoskeleton.

Gait synchronization has attracted significant attention in research on assistive lower-limb exoskeletons because it can circumvent conflicting movements and improve the assistance performance. This study proposes an adaptive modular neural control (AMNC) for online gait synchronization and the adaptation of a lower-limb exoskeleton. The AMNC comprises several distributed and interpretable neural modules that interact with each other to effectively exploit neural dynamics and adopt feedback signals to quickly reduce the tracking error, thereby smoothly synchronizing the exoskeleton movement with the user's movement on the fly. Taking state-of-the-art control as the benchmark, the proposed AMNC provides further improvements in the locomotion phase, frequency, and shape adaptation. Accordingly, under the physical interaction between the user and the exoskeleton, the control can reduce the optimized tracking error and unseen interaction torque by up to 80% and 30%, respectively. Accordingly, this study contributes to the advancement of exoskeleton and wearable robotics research in gait assistance for the next generation of personalized healthcare.

Research on exoskeleton compliance control strategy based on dual interaction torque split phase control method.

Human gait pattern recognition and compliance control are key technologies for achieving high coordination and assistance between exoskeleton robots and human movements. In order to improve the adaptability of exoskeleton robots to the human body, this paper proposes an exoskeleton compliance control strategy based on dual interaction torque phase separation control method. A support phase swing phase split control strategy based on dual interaction torque is proposed. Utilize the interaction force of human joints and adopt a model-based method to control the support phase. By utilizing the interaction force of exoskeleton joints and using a torque closed-loop method to control the swing phase, a multi-state control method of motion is achieved. A lower limb exoskeleton knee joint testing platform is built to verify the proposed human gait recognition The effectiveness of human-machine interaction force identification and human-machine coupling system compliance control technology. The proposed control method can effectively adjust joint torque, enabling the exoskeleton robot to maintain balance and stability during the entire walking phase.

Human-in-the-Loop Optimization of Knee Exoskeleton Assistance for Minimizing User's Metabolic and Muscular Effort.

Lower limb exoskeletons have the potential to mitigate work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls adapt an exoskeleton's assistance in real time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton using a CMA-ES algorithm to minimize the users' physical effort, a parameter innovatively evaluated using the interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user's metabolic cost within the HITL control through a machine-learning model. The regression model estimated the metabolic cost, in real time, with a root mean squared error of 0.66 W/kg and mean absolute percentage error of 26% (n = 5), making faster (10 s) and less noisy estimations than a respirometer (K5, Cosmed). The HITL reduced the user's metabolic cost by 7.3% and 5.9% compared to the zero-torque and no-device conditions, respectively, and reduced the interaction torque by 32.3% compared to a zero-torque control (n = 1). The developed HITL control surpassed a non-exoskeleton and zero-torque condition regarding the user's physical effort, even for a task such as slow walking. Furthermore, the user-specific control had a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential of HITL controls in assisted walking.

Motor Control in Parkinson’s Disease During the Performance of Multi-Joint Reversal Movements

We tested if the movement slowness of individuals with Parkinson’s disease is related to their decreased ability to generate adequate net torques and linearly coordinate them between joints. This cross-sectional study included ten individuals with Parkinson’s disease and ten healthy individuals. They performed planar movements with a reversal over three target distances. We calculated joint kinematics of the elbow and shoulder using spatial orientation. The muscle, interaction, and net torques were integrated into the acceleration/deceleration phases of the fingertip speed. We calculated the linear correlations of those torques between joints. Both groups modulated the elbow and shoulder net torques with target distances. They linearly coupled the production of torques. Both groups did not modulate the interaction torques. The movement slowness in Parkinson’s disease was related to the difficulty in generating the appropriate muscle and net torques in the task. The interaction torques do not seem to play any role in movement control.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition. However, due to unknown model parameters such as the mass, moment of inertia and mechanical size, the dynamic model of exoskeletons is difficult to construct. Hence, an enhanced whale optimization algorithm (EWOA) is proposed to identify the exoskeleton model parameters. Meanwhile, the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints. Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion. Finally, the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform. The knee joint motion achieves a steady-state response after 0.5 s. Meanwhile, the position error of hip joint response is less than 0.03 rad after 0.9 s. In addition, the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{N}\\cdot\ ext{m}}$$\\end{document}. This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters. Furthermore, an enhanced mutation strategy is adopted to avoid whale evolution's unsatisfactory local optimal value.

Rogue wave excitations and hybrid wave structures of the Heisenberg ferromagnet equation with time-dependent inhomogeneous bilinear interaction and spin-transfer torque.

In this paper, we focus on the localized rational waves of the variable-coefficient Heisenberg spin chain equation, which models the local magnetization in ferromagnet with time-dependent inhomogeneous bilinear interaction and spin-transfer torque. First, we establish the iterative generalized (m,N-m)-fold Darboux transformation of the Heisenberg spin chain equation. Then, the novel localized rational solutions (LRSs), rogue waves (RWs), periodic waves, and hybrid wave structures on the periodic, zero, and nonzero constant backgrounds with the time-dependent coefficients α(t) and β(t) are obtained explicitly. Additionally, we provide the trajectory curves of magnetization and the variation of the magnetization direction for the obtained nonlinear waves at different times. These phenomena imply that the LRSs and RWs play the crucial roles in changing the circular motion of the magnetization. Finally, we also numerically simulate the wave propagations of some localized semi-rational solutions and RWs.

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper.

In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human-machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

Realizing polarization-dependent unidirectional magnon channel in antiferromagnetic domain wall

Achieving unidirectional spin wave (or magnon) transport in domain wall (DW) represents the key step for designing functional magnonic devices. Here, we theoretically investigate the propagation behavior of spin waves (SWs) in antiferromagnetic DW when the Dzyaloshinskii–Moriya interaction (DMI) and/or spin transfer torque (STT) are considered. On the one hand, we find that the DMI lifts the degeneracy of magnon bands, from which one can obtain pure right- or left-handed polarized SWs. On the other hand, the nonreciprocal attenuation of magnons induced by STT is identified. Interestingly, we realize the polarization-dependent unidirectional propagation of SWs when the nonadiabatic coefficient β exceeds a critical value. Moreover, the micromagnetic simulations verify the theoretical predictions with good agreement. Our work provides a simple method for achieving unidirectional magnons with desired polarity in antiferromagnetic DW, which is indispensable for future magnonic computing and communication.

Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation.

For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can lead to the risk of degenerative tissue ulceration. This article presents a novel human-robot collaborative control scheme that achieves control weight self-adjustment for robotic prostheses to minimize interaction torque. To establish the human-robot interaction relationship, we regard the contact pressure between human residual limb and the prosthetic receiving cavity as the interaction force. We aim at reducing the interaction force under the premise of minimally changing the original motion trajectory of the robotic prosthesis. The control scheme mainly includes trajectory optimization based on a dual-agent game control scheme under a cooperative relationship, and a fuzzy logic system for improving the control accuracy of trajectory tracking of robotic prostheses with unknown dynamic parameters. Experiments were carried out on two amputee participants to verify the proposed human-robot interactive control scheme in a robotic prosthesis. The results show that the interaction torque could be reduced while maintaining minimal trajectory tracking error. The proposed control scheme could potentially facilitate the dexterous manipulation of leg prostheses, thus benefiting amputees.

Dynamic characterization and control of a back-support exoskeleton 3D-printed cycloidal actuator

The safety, health, and well-being of human workers are crucial for socially sustainable production systems, especially in Industry 5.0. Occupational exoskeletons, particularly back-support devices, are increasingly being adopted to reduce musculoskeletal disorders and human fatigue. To reduce costs and weight, optimized exoskeleton design is being explored. A 3D-printed cycloidal reduction stage for the actuation unit is proposed, focusing on an interaction torque observer and an impedance-based controller for human-robot interaction. The device's dynamic characterization and control are analyzed to evaluate its applicability to a sensorless back-support occupational exoskeleton.

Repetitive Control of Knee Interaction Torque via a Lower Extremity Exoskeleton for Improved Transparency During Walking

Repetitive Control of Knee Interaction Torque via a Lower Extremity Exoskeleton for Improved Transparency During Walking

Adaptive Human–Robot Interaction Torque Estimation With High Accuracy and Strong Tracking Ability for a Lower Limb Rehabilitation Robot

Adaptive Human–Robot Interaction Torque Estimation With High Accuracy and Strong Tracking Ability for a Lower Limb Rehabilitation Robot

Particle-wall alignment interaction and active Brownian diffusion through narrow channels.

We numerically examine the impacts of particle-wall alignment interactions on active species diffusion through a structureless narrow two-dimensional channel. We consider particle-wall interaction to depend on the self-propulsion velocity direction whereby some specific particle's alignments with respect to the boundary walls are stabilized more. Further, the alignment interaction is meaningful as long as particles are close to the confining boundaries. Unbiased diffusion of active particles for various possible stable velocity alignments against the walls has been examined. We show that for the most stable configuration leading to the self-propulsion velocity direction perpendicular to the wall, diffusivity becomes inversely proportional to the square of the alignment interaction torque. On the other hand, when the self-propulsion velocity direction making an acute angle to the channel walls is the most stable configuration, diffusion exponentially grows with strengthening alignment interaction. Hence, particle-wall interaction plays a pivotal role in the transport control of active particles through narrow channels. Moreover, the impacts of the alignment interactions on diffusion largely depend on the particle's self-propulsion properties and its chirality. Our simulation results can potentially be used to understand unbiased diffusion of artificial or living micro/nano-objects (such as virus, bacteria, Janus particles, etc.) though narrow confined structures.

A cascade fuzzy adaptive based interaction torque control of a pneumatically actuated forearm rehabilitation robot under disturbance effects

In this study, an intelligent adaptive interaction torque control for a pneumatically actuated forearm rehabilitation robot has been proposed. The main objective is to provide a haptic environment that ensures stable interaction torque fields at changing levels. To achieve this goal, a cascade fuzzy adaptive controller, that is specifically tailored to handle varying levels of interaction torque and ensure stability throughout the rehabilitation process, has been designed. To improve the efficiency of the controller, non-linear friction torque identification of the pneumatic actuator based on changing operating conditions has been conducted. Parallel to this, a user motion intention detection algorithm has been designed to provide compliant, safe and suitable human-robot interactions. The disturbance cases have been considered to make the system robust to unknown conditions. Stability analysis has been performed, specifically focusing on the boundary-input boundary-output (BIBO) stability conditions. In order to demonstrate the superior performance of the proposed cascade fuzzy adaptive algorithm, a cascade PID algorithm has also been meticulously designed for comparison. Numerous experimental validation tests involving a healthy user were conducted in a Hardware-in-the-Loop environment, focusing on torque trajectory tracking performance. The proposed control technique exhibited improved convergence dynamics compared to the cascade PID algorithm, yielding mean absolute error levels of 0.0218 Nm and 0.099 Nm for target interaction torque under disturbance-free and disturbed conditions, respectively.

Assist-As-Needed rehabilitation using velocity field for upper limb exoskeleton

Assist-As-Needed (AAN) rehabilitation in related works typically limits subjects to perform rehabilitation tasks with particular time instants. This paper proposes a velocity field based on an active-assistive control system to solve this issue. The active intention of motion of the subjects are extracted via a Kalman filter constructed on the basis of an interactive torque observer. Then, given a task motion pattern devised to provide subjects the time-decoupled assistance, our system can first automatically produce a position-coupled velocity field. Afterwards, a method is proposed to integrate the active and assistive motion based on the subjects’ performance and involvement so that eventually they will actively perform the task in a more accurate manner. The stability of the proposed system is verified by Lyapunov analysis. The experiment results show that the execution time and the subjects’ exertion are reduced when performing given tasks compared with the related work. To sum up, the hereby developed system not only can maintain the subjects’ active intention but also can assist them to run the task accurately.

Gait Prediction and Variable Admittance Control for Lower Limb Exoskeleton With Measurement Delay and Extended-State-Observer.

The measurement delay of the feedback control system is a universal problem in industrial engineering, which will degrade output performance, especially causing undesirable chatter responses. In this study, a deep-Gaussian-process (DGP)-based method for operator's gait prediction is proposed to estimate the real-time motion intention and to compensate for the measurement delay of the inertial measurement unit (IMU). On the basis of these gait prediction uncertainties quantified by the DGP method, a variable admittance controller is designed to reduce real-time human-exoskeleton interaction torque. The reference trajectory is generated by the admittance controller, which is smoothed by the two-order Bessel interpolation. Meanwhile, the admittance parameters are self-regulated based on the defined uncertainty index of gait prediction. The extend-state observer (ESO) with backstepping iteration is adopted to compensate unmeasured system state, model uncertainties, and unmodeled dynamics of lower limb exoskeleton. The effectiveness of the proposed gait prediction and control scheme is verified by both the comparative simulations and experimental results of the human-exoskeleton cooperative motion.

Field-free domain wall spin torque nano-oscillators with multimodal real-time modulation and high-quality factor

We report on a magnetic domain wall (DW)-based spin torque nano-oscillator (STNO) with real-time multimodal frequency modulation characteristics by engineering the synergistic effect of shape anisotropy, Dzyaloshinskii-Moriya interaction (DMI), and spin-transfer torque. The achieved manageable oscillation and precession of magnetic DW motivate us to implement such attributes into the developed STNO under a low current density of ∼107 A/cm2, which demonstrates outstanding functionality of multimodal real-time modulation ranging from few GHz and sub-THz with corresponding few tens and >104 quality factor (fSTNO/Δf) in absence of external magnetic field. Furthermore, the dynamic process of DW motion and magnetic moment precession under different modes has been revealed systematically, and the frequency dependence of various physical parameters including damping constant, uniaxial anisotropy constant, saturation magnetization, exchange stiffness, and DMI constant has also been summarized in detail. Such a DW-based device enriches the STNO family and promises great potential with a broad spectrum of applications in microwave generators and neuromorphic computing.

Effects of accretion on the structure and rotation of forming stars

Context. Rotation period measurements of low-mass stars show that the spin distributions in young clusters do not exhibit the spin-up expected due to contraction in the phase when a large fraction of stars is still surrounded by accretion discs. Many physical models have been developed to explain this feature based on different types of star-disc interactions alone. In this phase, the stars accrete mass and angular momentum and may experience accretion-enhanced magnetised winds. The stellar structure and angular momentum content thus strongly depend on the properties of the accretion mechanism. At the same time, the accretion of mass and energy has a significant impact on the evolution of the stellar structure and the moment of inertia. Our understanding of the spin rates of young stars therefore requires a description of how accretion affects the stellar structure and angular momentum simultaneously. Aims. We aim to understand the role of accretion to explain the observed rotation-rate distributions of forming stars. Methods. We computed evolution models of accreting very young stars and determined in a self-consistent way the effect of accretion on stellar structure and the angular momentum exchanges between the stars and their disc. We then varied the deuterium content, the accretion history, the entropy content of the accreted material, and the magnetic field as well as the efficiency of the accretion-enhanced winds. Results. The models are driven alternatively both by the evolution of the momentum of inertia and by the star-disc interaction torques. Of all the parameters we tested, the magnetic field strength, the accretion history, and the deuterium content have the largest impact. The injection of heat plays a major role only early in the evolution. Conclusions. This work demonstrates the importance of the moment of inertia evolution under the influence of accretion for explaining the constant rotation-rate distributions that are observed during the star-disc interactions. When we account for rotation, the models computed with the recently calculated torque along with a consistent structural evolution of the accreting star are able to explain the almost constant spin evolution for the whole range of parameters we investigated, but it only reproduces a narrow range around the median of the observed spin rate distributions. Further development, including for example more realistic accretion histories based on dedicated disc simulations, are likely needed to reproduce the extremes of the spin rate distributions.