Torque Estimation Research Articles

Optimized Impedance Control of a Lightweight Gait Rehabilitation Exoskeleton Based on Accurate Knee Joint Torque Estimation

Optimized Impedance Control of a Lightweight Gait Rehabilitation Exoskeleton Based on Accurate Knee Joint Torque Estimation

Joint Angle and Torque Estimation Using Multifrequency Electrical Impedance Myography and Surface Electromyography

Joint Angle and Torque Estimation Using Multifrequency Electrical Impedance Myography and Surface Electromyography

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

Performance improvement of Grid-Connected Wind Energy Conversion System through Definite Time Horizon Control and MPPT based on Adaptive Observers

This research work investigates the implementation of Definite Time Horizon Control (DTHC) through an observer in a grid-connected Wind Energy Conversion System (WECS) equipped with a Permanent Magnet Synchronous Generator (PMSG). The main objective is to achieve sensorless Maximum Power Point Tracking (MPPT). To achieve this goal, mechanical rotational speed and torque estimates are exploited from an adaptive nonlinear observer. In addition, this work explores how the DTHC improves on the MPPT technique, enabling faster and more accurate tracking of the maximum power point. The proposed controller, unlike traditional non-linear controllers, rapidly stabilizes the WECS, tracking reference trajectories in a short, pre-determined period of time. Indeed, it demonstrates robustness in the face of unpredictable WECS parameters, adapting to variations thanks to a robust control design. Mathematical demonstrations of the defined time horizon stability and the resilience of the suggested WECS controllers are detailed in this investigation. This DTHC method improves rotor speed control, enabling more efficient sensorless MPPT. It contributes to improved WECS performance for following dynamic references and enhances adaptability against unpredictable parameters. In addition, it provides sophisticated control capabilities for the grid connected converter, improving the smoothness and efficiency of electrical energy injection.

Biomechanical Hand Model: Modeling and Simulating the Lateral Pinch Movement

Abstract Background Hand movements are crucial in daily activities, sparking extensive interest and research in biomechanical models. While existing models offer valuable insights, their complexity and processing costs may limit their suitability for all applications, sometimes impeding research efficiency. Objectives This study aimed to develop a biomechanical model of the human hand for analyzing the physiology of lateral pinch movement. Unlike conventional methodologies, this approach focuses on delivering a computationally efficient model while incorporating the trapeziometacarpal joint into the analysis. Methods The model, which operates in a multibody environment, simulates lateral pinching movement by applying external time-varying torques to digit joints, emulating musculature, tendons, and ligaments. Torque estimation was achieved through the Euler-Lagrange approach. The model generates animated representations of the movement, aiding pathology identification and outputting dynamic variables. The model’s was validated through data acquired from asymptomatic subjects via an OptiTrack system. Results The average disparity between the expected and obtained joint angular displacements was $$\varvec{6.06~\%}$$ 6.06 % and $$\varvec{1.90~\%}$$ 1.90 % during validation and verification stages, suggesting high fidelity in the model performance. Correlation analysis revealed strong positive linear relationships and robust correlations between the obtained and expected configuration data. Model-generated pinch postures closely resembled expected physiological patterns, with results falling within the range for asymptomatic individuals documented in the scientific literature. Conclusion The system efficiently analyzes dynamic variables at a low computational cost, offering animated representations for pathology identification. The model’s potential for rehabilitation solutions and adaptability, coupled with its accuracy and versatility, make it an asset for advancing hand biomechanics research.

Adaptive fast terminal sliding mode control of robotic manipulators based on joint torque estimation and friction compensation

AbstractIn this work, an adaptive fast terminal sliding mode control (AFSMC) approach based on joint torque estimation and friction compensation is proposed to enhance the trajectory tracking accuracy of robotic manipulators under variable load conditions. The joint torque estimation utilizes an improved harmonic drive compliance model and adaptive low‐pass filtering, and friction compensation employs a hybrid model accounting for velocity and load torque effects. These compensations reduce the upper bound of the uncertainty, while AFSMC further reduces dependency on upper uncertainty bounds and minimizes the chattering. The stability analysis using the Lyapunov method confirms the effectiveness of this approach. Experimental results demonstrate that the proposed controller achieves smaller root mean square and maximum error of trajectory tracking, thus significantly improving trajectory tracking accuracy under variable load conditions.

Load torque estimation for cable failure detection in cable-driven parallel robots: a machine learning approach

AbstractThis paper proposes a method for cable failure detection in cable-driven parallel robots (CDPRs) with arbitrary architecture, which is based on the estimates of the motor load torques, together with machine learning algorithms. By just exploiting the dynamic model of each actuator in the conditions of no load, an open-loop load torque observer is designed for each motor to estimate the presence of a load coupled through a cable. Since such a load instantaneously goes to zero for the motor with a broken cable, a simple but effective and robust signature of failure can be inferred to provide reliable detection even in the case of various model mismatches. Additionally, the load torque observer is not computationally demanding since just motor measurements are required, thus avoiding any direct measurement (and a dynamic model as well) on the end-effector. The detection of a failure is made through supervised classification algorithms based on artificial intelligence. The training of the machine learning algorithm is based on a “hybrid” approach: the dataset includes several failure cases, which are numerically generated through a system digital twin developed through the multibody system theory, together with measurements of the real system in nonfailing conditions. Different classification algorithms are considered, together with different sets of input variables to be fed to the classifier. Four numerical examples are proposed by showing the method capability in handling both fully actuated and redundantly actuated CDPRs under cable failure, both rigid and flexible cables, and also evaluating the response in the presence of cable slackness.

Horseshoes and spiral waves: capturing the 3D flow induced by a low-mass planet analytically

ABSTRACT The key difficulty faced by 2D models for planet–disc interaction is in appropriately accounting for the impact of the disc’s vertical structure on the dynamics. 3D effects are often mimicked via softening of the planet’s potential; however, the planet-induced flow and torques often depend strongly on the choice of softening length. We show that for a linear adiabatic flow perturbing a vertically isothermal disc, there is a particular vertical average of the 3D equations of motion that exactly reproduces 2D fluid equations for arbitrary adiabatic index. There is a strong connection here with the Lubow–Pringle 2D mode of the disc. Correspondingly, we find a simple, general prescription for the consistent treatment of planetary potentials embedded within ‘2D’ discs. The flow induced by a low-mass planet involves large-scale excited spiral density waves that transport angular momentum radially away from the planet and ‘horseshoe streamlines’ within the coorbital region. We derive simple linear equations governing the flow that locally capture both effects faithfully simultaneously. We present an accurate coorbital flow solution allowing for inexpensive future study of corotation torques, and predict the vertical structure of the coorbital flow and horseshoe region width for different values of adiabatic index, as well as the vertical dependence of the initial shock location. We find strong agreement with the flow computed in 3D numerical simulations, and with 3D one-sided Lindblad torque estimates, which are a factor of 2–3 lower than values from previous 2D simulations.

Multiobjective Control Strategy for Switched Reluctance Machine With Online Efficiency Estimation and Improved Online Instantaneous Torque Estimation

Multiobjective Control Strategy for Switched Reluctance Machine With Online Efficiency Estimation and Improved Online Instantaneous Torque Estimation

Transformer based Collision Detection Approach by Torque Estimation using Joint Information

Transformer based Collision Detection Approach by Torque Estimation using Joint Information

Real-Time Implementable Reduced-Order Energy Model for an Electric Vehicle

<div>Accurate estimation of vehicle energy consumption plays an important role in developing advanced energy-saving connected automated vehicle technologies such as Eco Approach and Departure, PHEV mode blending, and Eco-route planning. The present study developed a reduced-order energy model with second-order response surfaces and torque estimation to estimate the energy consumption while just relying on the drive cycle information. The model is developed for fully electric Chevrolet Bolt using chassis dynamometer data. The dyno test data encompasses the various EPA test cycles, real-world, and aggressive maneuvers to capture most powertrain operating conditions. The developed model predicts energy consumption using vehicle speed and road-grade inputs for a drive cycle. The accuracy of the model is validated by comparing the prediction results against track and road test data. The developed model was able to accurately predict the energy consumption for track drive cycles within the error of ±4.0% of that measured from the experimental data. Finally, the model has been tested and verified for real-time implementation using the dSPACE MicroAutoBox II HIL test bench.</div>

Ankle torque estimation based on disturbance observers for robotic rehabilitation

Ankle torque estimation based on disturbance observers for robotic rehabilitation

State Reconstruction of Remote Robotic System Using Delayed Output and Torque Estimation

State Reconstruction of Remote Robotic System Using Delayed Output and Torque Estimation

Experimental analysis of enhanced finite set model predictive control and direct torque control in SRM drives for torque ripple reduction

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

MOB-Net: Limb-modularized uncertainty torque learning of humanoids for sensorless external torque estimation

Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB, substantially reducing the estimation errors of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Furthermore, MOB-Net shows more robust performance for the unseen data compared to the end-to-end learning method, and the robustness of MOB-Net is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented to show the versatility of MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.

Estimation of joint torque in dynamic activities using wearable A-mode ultrasound

The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R2) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

Development of Thrust, Torque, and Power Estimation Model, and Prediction Performance of Earth Pressure Balance Tunnel Boring Machine in Mixed-Face Strata

Development of Thrust, Torque, and Power Estimation Model, and Prediction Performance of Earth Pressure Balance Tunnel Boring Machine in Mixed-Face Strata

Using high sensitivity DC accelerometers for torque estimation on a wind turbine gearbox

In this paper we propose a novel approach to estimate the torque on a wind turbine gearbox using single-axis high sensitivity DC accelerometers. The torque estimator uses an Augmented Extended Kalman Filter (AEKF), combining physics-based models with measurement data. The accelerometer sensor model is derived by analysing the measurements, leading to the identification of two torque-driven acceleration contributions: acceleration due to force excitation and acceleration due to a change in measurement direction. For the latter, two different sources, with distinct frequency content, are capable of changing the measurement direction. The first source corresponds to rigid body motion (f < 0.15Hz), the second one to ring gear deformation (0.15 < f < 4Hz). Using this information, the measured signal can be filtered to target specific acceleration contributions. The first torque estimation approach targets frequencies from 0.15 to 4Hz and produces a Normalised Mean Absolute Error (NMAE) of 2.12%. The second approach targets frequencies up to 0.15Hz, and will estimate the rigid body deflection angle. The torque is calculated through a linear identified relation between the rigid body angle and applied input torque. This approach yields an NMAE of 2.59%. This leads to the conclusion that high sensitivity DC accelerometers are a valid option for estimating torque on a wind turbine gearbox. These torque monitoring approaches will pave the way for advanced condition monitoring strategies and the calculation of remaining useful lifetime of the gearbox.

Position sensorless extended and unscented Kalman filters with permanent magnet flux linkage and load torque estimation for surface-mounted PMSM

In this paper, novel position sensorless state estimators with improved robustness to permanent magnet (PM) flux linkage variations in permanent magnet synchronous machines (PMSMs) are presented. Unlike state estimators using conventional infinite inertia or electromechanical models, the estimators presented here can also estimate the PM flux linkage, so they are not sensitive to its uncertainty. For each models used for state estimation, a detailed observability study is presented. Due to the nonlinear models, extended and unscented Kalman filter algorithms are used for the implementation. To compare the sensitivity of conventional and proposed state estimators to uncertainty in electrical parameters, numerical simulations are carried out. In addition, the computational burden of the estimators is compared by real-time execution.

Wrench Estimation and Friction Compensation Using Multiple Shooting Method for Tether Units Augmented to Remote Tendon-Driven Continuum Robots

Abstract Remote-actuated mechanisms that employ tendon sheaths or tether units can transmit mechanical force without directly connecting the actuators to the mechanism, but suffer from undesirable side effects like mechanical friction and induced external wrench disturbances on the distal side of the mechanism. In this work, a multiple shooting method is proposed as a superior method to the single shooting method to solve the Cosserat rod boundary value problems, to obtain the states of the system. With that, numerical experiments are provided to demonstrate the difficulty of solving these boundary value problems, simultaneously showing the validity of the proposed approach. 2D reconstruction experiments were conducted to show shape reconstruction capabilities. In addition, friction loss and 6-degree-of-freedom wrench estimations were also experimentally validated with the proposed mathematical model with 0.1679N and 0.0401Nm for root-mean-squared error (RMSE) force estimation and torque estimation error, respectively, while achieving a 3% mean absolute percentage steady-state friction estimation error. Finally, a modified resolved rate controller was applied to steer a remote tendon-driven continuum robot to compensate for friction loss for 3.1-m long tether units.