Torque Estimation Research Articles

Flow modeling and in situ viscosity monitoring for a combined rotational and pressure-driven flow of a non-Newtonian fluid through a concentric annulus

Based on the energy dissipation rate, we proposed a systematic method for quantifying the effective viscosity, effective shear rate, and flow characteristics in a drilling flow of non-Newtonian fluids in a concentric annulus subjected to a combined pressure-driven and rotational flow. Two flow parameters, i.e., the energy dissipation rate coefficient and the effective shear rate coefficient, were introduced to quantify flow characteristics, such as the relationship between pressure drop, flow rate, torque, and rotational speed, which are nearly independent of rheological behaviors. In this work, we began with flow quantification of the individual flow problem in a concentric annulus, i.e., pressure-driven flow and rotational flow, and derived expressions of two flow parameters analytically in each case. Then, we established the flow quantification method for the combined pressure-driven and rotational flow. The proposed flow modeling scheme was derived analytically with a power-law fluid and was validated for various non-Newtonian fluids, such as Carreau and Herschel–Bulkley fluids, through extensive numerical simulations. The method can be employed for the in situ viscosity measurement of drilling muds in terms of shear rate, as well as for the estimation of torque, pressure drop, and power consumption. Maximum errors between theoretical prediction and numerical simulation results in estimating torque, pressure drop, and shear-dependent viscosity were found to be 5.5%, 7.3%, and 6%, respectively.

Synthesis of an adaptive observer of the resistance torque at a shaft of a traction electric motor

BACKGROUND: Operating companies and consumers have demands to vehicles for efficiency improvement and cost cut. A part of substantial expenses is fuel cost, therefore use of vehicles with electric drive such as hybrid or pure electric (battery-electric) has potential for agriculture. It is mainly important for them to have the drivetrain characterized by high efficiency that increases autonomous mileage for one charge. To achieve this, it is necessary to control electric motors in the way to ensure maximal surface grip coefficient without wheel slipping. In this case, real-time defining of wheel resistance torque in necessary for efficient control. AIMS: Development of the theoretical basis and the law of optimal estimation of the resistance torque at a shaft of traction electric motor of transport vehicles for the sake of efficient control of traction electric drive. METHODS: Simulation of the law of optimal estimation of the resistance torque at a shaft of traction electric motor was carried out in the MATLAB/Simulink software package. RESULTS: The article provides with theoretical basis of formatting the law of optimal estimation of the resistance torque at a shaft of traction electric motor of transport vehicles for the sake of efficient control of traction electric drive, the law of optimal estimation of the resistance torque at a shaft of traction electric motor and the results of simulation of the law of optimal estimation of the resistance torque at a shaft of traction electric motor obtained in the MATLAB/Simulink. CONCLUSIONS: Practical value of the study lies in ability of using the proposed control laws for development of transport vehicles drivetrain control system.

Estimation of satellite attitude dynamics and external torques via mixed Kalman/H-infinity filter under inertia uncertainties

Estimation of satellite attitude dynamics and external torques via mixed Kalman/H-infinity filter under inertia uncertainties

NARX Technique to Predict Torque in Internal Combustion Engines

To carry out increasingly sophisticated checks, which comply with international regulations and stringent constraints, on-board computational systems are called upon to manipulate a growing number of variables, provided by an ever-increasing number of real and virtual sensors. The optimization phase of an ICE passes through the control of these numerous variables, which often exhibit rapidly changing trends over time. On the one hand, the amount of data to be processed, with narrow cyclical frequencies, entails ever more powerful computational equipment. On the other hand, computational strategies and techniques are required which allow actuation times that are useful for timely and optimized control. In the automotive industry, the ‘machine learning’ approach is becoming one the most used approaches to perform forecasting activities with reduced computational effort, due to both its cost-effectiveness and its simple and compact structure. In the present work, the nonlinear dynamic system we address is related to the torque estimation of an ICE through a nonlinear autoregressive with exogenous inputs (NARX) approach. Preliminary activities were performed to optimize the neural network in terms of neurons, hidden layers, and the number of input parameters to be assessed. A Shapley sensitivity analysis allowed quantification of the impact of each variable on the target prediction, and therefore, a reduction in the amount of data to be processed by the architecture. In all cases analyzed, the optimized structure was able to achieve average percentage errors on the target prediction that were always lower than a critical threshold of 10%. In particular, when the dataset was augmented or the analyzed cases merged, the architecture achieved average prediction errors of about 1%, highlighting its remarkable ability to reproduce the target with fidelity.

Modeling and estimation of thrust force, torque, and surface roughness in indexable drilling of AA6061-T651 with Taguchi, ANN, and ANFIS

Modeling and estimation of thrust force, torque, and surface roughness in indexable drilling of AA6061-T651 with Taguchi, ANN, and ANFIS

A New Closed Loop Constant v/f Control of Induction Motor with Torque Control Based on DPWM

An easy sensor-less scalar control algorithm is described in this article as a method for controlling the speed of an induction motor. For developing a closed-loop v/f control of the induction motor drive, a torque controller with PI is implemented. The torque command was estimated by utilizing the voltage command, the feedback current, and a torque estimator. Additionally, a torque reference was provided for the Torque PI controller. Considering this, the purpose of this work is to investigate the closed-loop PI-based torque control of an induction motor drive that applies DPWM. In addition to this, it provides a description of a PI-based closed-loop torque control that works in conjunction with v/f control and tries to discover the most effective method for driving an induction motor based on an examination of Total Harmonic Distortion (THD) and torque ripple. To evaluate the performance studies of open- and closed-loop strategies with DPWM techniques for induction motor driving, the MATLAB/Simulink environment has been deployed. Experimental results have been validated.

Validation of Estimators for Weight-Bearing and Shoulder Joint Loads Using Instrumented Crutches.

This research paper aimed to validate two methods for measuring loads during walking with instrumented crutches: one method to estimate partial weight-bearing on the lower limbs and another to estimate shoulder joint reactions. Currently, gait laboratories, instrumented with high-end measurement systems, are used to extract kinematic and kinetic data, but such facilities are expensive and not accessible to all patients. The proposed method uses instrumented crutches to measure ground reaction forces and does not require any motion capture devices or force platforms. The load on the lower limbs is estimated by subtracting the forces measured by the crutches from the subject's total weight. Since the model does not consider inertia contribution in dynamic conditions, the estimation improves with low walking cadence when walking with the two-point contralateral and the three-point partial weight-bearing patterns considered for the validation tests. The shoulder joint reactions are estimated using linear regression, providing accurate values for the forces but less accurate torque estimates. The crutches data are acquired and processed in real-time, allowing for immediate feedback, and the system can be used outdoors in real-world walking conditions. The validation of this method could lead to better monitoring of partial weight-bearing and shoulder joint reactions, which could improve patient outcomes and reduce complications.

Estimation of generator electrical power output and turbine torque in modelling and field testing of OWC wave energy converters

Estimation of generator electrical power output and turbine torque in modelling and field testing of OWC wave energy converters

A New Closed Loop Constant v/f Control of Induction Motor with Torque Control Based on DPWM

An easy sensor-less scalar control algorithm is described in this article as a method for controlling the speed of an induction motor. For developing a closed-loop v/f control of the induction motor drive, a torque controller with PI is implemented. The torque command was estimated by utilizing the voltage command, the feedback current, and a torque estimator. Additionally, a torque reference was provided for the Torque PI controller. Considering this, the purpose of this work is to investigate the closed-loop PI-based torque control of an induction motor drive that applies DPWM. In addition to this, it provides a description of a PI-based closed-loop torque control that works in conjunction with v/f control and tries to discover the most effective method for driving an induction motor based on an examination of Total Harmonic Distortion (THD) and torque ripple. To evaluate the performance studies of open- and closed-loop strategies with DPWM techniques for induction motor driving, the MATLAB/Simulink environment has been deployed. Experimental results have been validated.

Accurate estimation of spin-orbit torque in heavy-metal multilayers with account of thermoelectric effects

Accurate estimation of spin-orbit torque in heavy-metal multilayers with account of thermoelectric effects

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance Rs and combined inertia of rotor and load J) and changes in load torque TL can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.

Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation.

Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human-robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.

A nonlinear robot joint friction compensation method including stick and sliding characteristics

PurposeDue to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.Design/methodology/approachThis paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.FindingsExperiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.Originality/valueThe proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

Impact of Rotor Flux-Barriers on Coupling Coefficient in a Brushless Doubly-Fed Reluctance Machine

A brushless doubly-fed reluctance machine offers several advantages over a single-port machine since it requires a partially-rated power converter, provides reduced maintenance, and operates without permanent magnets. The complexity of the air-gap magnetic fields due to differing winding-pole numbers interacting with a reluctance rotor have precluded the use of an analytical framework to optimize the rotor. Instead, researchers have relied upon time-consuming finite element analysis (FEA). This paper presents an analytical method to compute closed-form solutions for the mean torque, captured by the coupling coefficient, using a practical circular ducted rotor. The method also accounts for the potential drop across the flux-barriers, leading to a better estimation of mean torque. The model is validated using both FEA and experimental tests.

Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Compliance control is crucial for physical human-robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants' motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.

Direct average torque control of switched reluctance generator

AbstractThere are few pieces of research on switched reluctance generator (SRG) that accomplish optimal performance torque control, most prefer to use voltage, speed, or power control strategies. This paper proposes a direct average torque control (DATC) for SRG applied to wind energy conversion systems (WECS). The SRG is driven by an asymmetric half‐bridge converter (AHB), and an external chopper regulates the DC‐bus voltage. In the DATC strategy, the switching logic is modified, so that the machine operates in the generator mode. The dynamic behavior of an artificial neural network (ANN)‐based and the conventional co‐energy‐based torque estimation methods are discussed. Thereafter, the proposed DATC is compared to the direct power control (DPC) strategy. For both strategies, lookup tables are created to store the optimal switching angles. These lookup tables are optimized to achieve a trade‐off between torque ripple minimization and efficiency maximization. Experimental results show that the DATC strategy presents a lower torque ripple than the DPC method over the entire speed range. In contrast, DPC delivers superior efficiency for low and medium speeds.

Balancing-Prioritized Anti-Slip Control of a Two-Wheeled Inverted Pendulum Robot Vehicle on Low-Frictional Surfaces with an Acceleration Slip Indicator

When a two-wheeled inverted pendulum (TWIP) robot vehicle travels on slippery roads, the occurrence of wheel slip extremely threatens its postural stability owing to the loss of wheel traction. If a severe wheel slip happens between the driving wheels and contact surfaces, no control techniques can guarantee the driving performance and stability of the TWIP robots in the absence of an extra wheel slip control strategy. In this paper, a TWIP-compatible countermeasure against the wheel slip phenomena is investigated for enhancing the reliability of the vehicle and the robustness of the motion control performance on low-frictional surfaces. To this end, we propose a balancing-prioritized anti-slip control method based on the maximum transmissible torque estimation, which is activated only when a wheel slip is detected by the acceleration slip indicator utilizing accessible data from the IMU and wheel encoders. It is proved that the TWIP vehicles applying the proposed method can successfully cope with low frictional surfaces while maintaining postural stability. Finally, comparative simulations and experiments demonstrate the effectiveness and feasibility of the proposed scheme.

Active Compliance Control Based on EKF Torque Fusion for Robot Manipulators

To improve the accuracy of torque estimation and compliance control of the force sensorless, we propose a torque fusion method based on extended Kalman filter (EKF), both the data of motor current and the harmonic reducer torsional deformation are involved. First, a nonlinear EKF is designed based on the motor side dynamic model and joint deformation nonlinear model. The experiment shows that the method overcomes the interference caused by the environment and nonlinear system factors. Then, an active compliance controller is designed with a nested loop framework based on the fused torque. The inner loop computes the dynamic torque as feedforward to compensate for the system's dynamic uncertainty. The outer loop is admittance control to realize the manipulator's active compliance with the fused torque. Experiments on the robotic manipulator show that the proposed torque estimation scheme can reduce the root mean square error (RMSE) to 0.1278N.m and the max error of the joint estimated torque to 0.34N.m. In addition, the force tracking accuracy of the proposed compliant controller can reach 3.6 N, and can be extended to more redundant joints.

Development of a hierarchical control strategy for a soft knee exoskeleton based on wearable multi-sensor system

Soft exoskeleton is a newly emerging approach for providing assistive forces to the movement of disabled individuals, which has aroused the interest of many researchers due to its promising prospects in rehabilitation training and motion assistance. In this article, a wearable wireless multi-sensor system and a hierarchical control architecture are developed for a soft knee exoskeleton. First, the major components of the soft knee exoskeleton are introduced, including the flexible Bowden-cable actuators, the soft wearable components, and the sensing modules and the control system. The wearable wireless multi-sensor system consists of an inertial measurement unit–based knee flexion–extension angle sensing module, a surface electromyogram–based joint torque estimation module, a miniature pulling force sensing module, and a central communication unit. And then, a three-level hierarchical control strategy—which fuses a high-level motion recognition and torque estimation algorithm, a mid-level knee trajectory generation strategy, and a low-level fuzzy impedance controller—is proposed to realize human motion assistance. Finally, experiments are carried out to verify the effectiveness of the proposed control strategy. The experimental setup is established with the soft knee exoskeleton, a treadmill, a wearable robotic rotary joint with an encoder, and an inelastic rope. The experimental result indicates that the root mean square errors of the joint angle estimation strategy and joint torque estimation strategy are less than 4.3° and 4.8 Nm, while the surface electromyogram–based assistance efficiency and torque-based assistance efficiency under different loading conditions are higher than 10.2% and 18.2%, respectively.

Adaptive slip vectoring for speed and yaw-rate control in electric vehicles with four in-wheel motors

A non model-based control architecture, called adaptive slip vectoring, is presented to regulate speed and yaw-rate in a fully electric vehicle equipped with four in-wheel induction motors. The actuators redundancy is dealt with by a Slip Vectoring strategy which, given a driving force and a driving yaw-moment as control constraints, allocates optimal slip references to each tire, so that four speed references are provided to the four in-wheel motors on the basis of vehicle accelerations, speed and yaw-rate regulation errors. In order to drive independently each in-wheel motor speed to its reference, a decentralized feedback control loop is designed which leads to exponentially convergent load torque estimates so that the torque–slip operating conditions can be monitored online and the load torque requirements are matched for each tire. As a result four decentralized current-pair reference inputs are provided to each motor. Structural sufficient conditions for local exponential stability of the speed and yaw-rate regulation are obtained for uniform rectilinear references. A new overall stability index is introduced and monitored online, and an automatic longitudinal speed reduction can be performed with the goal of keeping such a stability index within a predetermined range so that the structural sufficient conditions are met. Realistic CarSim simulations are presented: (i) a snowy uphill path illustrates the non model-based speed reduction driven by the stability index monitoring; (ii) standard moose-test maneuvers on dry and wet asphalt illustrate uniform yaw-rate tracking performance due to the continuous differential actions of the four in-wheel torques.