Error Feedback Research Articles

The effect of performance pressure and error-feedback on anxiety and performance in an interceptive task.

Whilst the disruptive effects of anxiety on attention and performance have been well documented, the antecedents to anxiety in motivated performance scenarios are less well understood. We therefore sought to understand the cognitive appraisals that mediate the relationship between pressurised performance situations and the onset of anxiety. We tested the effects of performance pressure and error feedback on appraisals of the probability and cost of failure, the experience of anxiety, and subsequent impacts on visual attention, movement kinematics, and task performance during a virtual reality interception task. A series of linear mixed effects models indicated that failure feedback and situational pressure influenced appraisals of the probability and cost of failure, which subsequently predicted the onset of anxious states. We did not, however, observe downstream effects on performance and attention. The findings support the predictions of Attentional Control Theory Sport, that (i) momentary errors lead to negative appraisals of the probability of future failure; and (ii) that appraisals of both the cost and probability of future failure are important predictors of anxiety. The results contribute to a better understanding of the precursors to anxiety and the feedback loops that may maintain anxious states.

Contour error dynamic analysis and predictive control for multi-axis motion system

Contour tracking accuracy is the key performance index of a multi-axis motion control system. In the previous design of the contour error feedback controller, the independent design idea of the tracking controller and contour controller of each logic axis is often adopted, which is not easy to realize the optimal control of tracking accuracy and contour accuracy. The coupling increment of each controller is inevitable in motion control compensation, which impacts the control system’s stability. Here, we propose a predictive control method for contour error. At first, through the state space model of the feed system, the system dynamics are judged to predict the contour accuracy in the future finite time domain. Besides, the linear quadratic performance index function of contour error is established, and the performance of the control objective is continuously optimized during the limited time domain with the form of rolling optimization. A closed-loop solution is obtained, which is most suitable for the current motion situation and could add to the system to realize the optimal control considering the trajectory contour and the tracking stability of each servo axis in the subsequent few sampling cycles. Finally, utilizing comparative experiments on the three-axis motion control platform, it is verified that the proposed contour predictive control strategy not only effectively reduces the tracking error but also significantly improves the contour accuracy, which is suitable for application in a complex industrial environment.

Spontaneous recovery in an untrained arm as an assay of interlimb transfer of motor learning.

Motor skills learned with one effector are known to transfer to an untrained effector. However, which of the many mechanisms that drive learning principally predict interlimb transfer, is less clear. Recent studies of motor adaptation suggest that transfer is tied to the state of an implicit mechanism that evolves gradually during learning. Interestingly, this "slow" process also promotes spontaneous recovery, or adaptation rebound, when error feedback is clamped to zero following adaptation-extinction training. If this mechanism also drives transfer, then recovery must occur in an arm performing zero-error-clamp movements after adaptation-extinction training with the opposite arm. Here we show this to be the case in participants who undergo visuomotor learning with their left arm and perform error-clamp movements with the right, but not vice versa. The performance of control participants reveals that the absence of a rebound in this latter group is not due to an inability to recover past learning when using the left arm. Our findings firstly advance the view that interlimb transfer following visuomotor adaptation is asymmetric. Secondly, since spontaneous recovery is a hallmark of the slow process, they lend strong support to the idea that it is this specific mechanism that provides a gateway for post-learning transfer to occur. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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Three-Dimensional Trajectory Tracking of AUV Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Decoupling Control

This paper presents a nonsingular terminal sliding mode and active disturbance rejection decoupling control (NTSM-ADRDC) scheme for the three-dimensional (3D) trajectory tracking of autonomous underwater vehicles (AUV). Firstly, the AUV model is decoupled into five independent single input–single output (SISO) channels using ADRDC technology. Secondly, the NTSM-ADRDC controller is designed. The linear extended state observer (LESO) is used to observe the AUV state variables, and estimate the total disturbance of the system. In addition, to improve the system error convergence rate, the combination of exponential reaching rate and NTSM constitutes a nonlinear states error feedback control law for the controller. Finally, the stability of the proposed control law is proved using the Lyapunov theory. The simulation results demonstrate the effectiveness and robustness of the designed NTSM-ADRDC trajectory tracking approach.

Feedback control of the mechanical spindle thermal error based on thermal simulation with bearing heat sources analysis

The thermal error stability (STE) of the spindle seriously affects the machining accuracy of a machine tool, however existing empirical heating model-based active cooling strategies mainly focus on suppressing the spindle’s overall thermal deformation and cannot effectively stabilize the thermal error. This study regards the “active cooling-spindle” system as a feedback control system and employs a data-driven thermal error model to provide feedback. Thus, the spindle thermal error can be stabilized for a long time owing to the homeostasis of the feedback control system under disturbance. A mechanical spindle with an external cooling scheme is taken as the study object. Bearings are the primary heat sources of the mechanical spindle; thus, the angular contact ball-bearing heat generation is precisely modeled based on local heat sources analysis of bearing components, elastohydrodynamic lubrication, and micro-contact theory. Thermal simulation of the spindle under varying-coolant temperature cooling is conducted to pre-validate the thermal error suppression and variation trend influencing effect, and obtain the Reference Input of Thermal Error (RITE) for the feedback control under different work conditions. Subsequently, a spindle thermal error feedback control system is developed, including computation, cooling, and real-time monitoring modules with inter-communication. Finally, the thermal error feedback control strategy is applied on the mechanical spindle, and experimental comparisons with constant coolant temperature cooling show that the thermal equilibrium time is advanced by 61.46%, 59.16%, 40.51%, and 58.08%. The thermal error variation range (TEVR) after the preheating stage is reduced to1.92, 1.52, 1.91, and 1.69 μm, respectively. The significant reduction in TEVR validated the effectiveness of the proposed strategy for spindle thermal error stabilization.

Constrained linear output regulation via measurement output feedback model predictive control

Recently, the output regulation problem with input constraints for incrementally stable plants was solved by an error feedback model predictive control (MPC) algorithm. This paper further considers the case with both state and input constraints for linear systems and uses a measurement output feedback MPC algorithm to solve the problem. By relaxing two restrictions, the result of this paper can handle a larger class of linear systems than the existing result. Due to the uncontrollability of the augmented system composed of the plant and the exosystem, a key assumption in the literature fails, we manage to overcome this difficulty by introducing novel tightened constraints and the terminal constraint. As a result, the proposed algorithm solves the linear output regulation problem in the presence of both state and input constraints. The effectiveness of our design is illustrated by a numerical example.

The Effect of Error Correction on Taiwanese EFL Students’ Writing Accuracy

This paper aims to investigate the effect of teacher’s error correction feedback on EFL students’ ability to correct their own writing errors for better accuracy. It intends to find out whether the instructor’s explicit error feedback followed by students’ extensive revisions, has any effect on students’ ability in revising their errors with their own writing pieces. A group of nineteen EFL Taiwanese college students, with low to intermediate English level proficiency, participated in this study. The results indicated that the effect of teachers’ error feedback on students’ ability to self-correct was not significant. Most of the errors, after given extensive feedback and revision, were not significantly revised after a three-month interval. The result of this case study tends to support the claim that teachers’ error feedback does not help much with EFL students improving their writing accuracy. Other findings regarding students’ interpretation towards their writing errors and unsatisfactory self-correction results and teacher’s perspective and observation are discussed.

Design and Stability Analysis of Sliding Mode Controller for Non-Holonomic Differential Drive Mobile Robots

This paper presents a novel extended state observer (ESO) approach for a class of plants with nonlinear dynamics. The proposed observer estimates both the state variables and the total disturbance, which includes both exogenous and endogenous disturbance. The study’s changes can be summarized by developing a sliding mode higher-order extended state observer with a higher-order augmented state and a nonlinear function for the estimation error correction terms (SMHOESO). By including multiple enhanced states, the proposed observer can monitor total disturbances asymptotically, with the second derivative of the total disturbance serving as an upper constraint on the estimation error. This feature improves the observer’s ability to estimate higher-order disturbances and uncertainty. To extend the concept of the linear extended state observer (LESO), a nonlinear function can modify the estimation error in such a way that the proposed observer can provide faster and more accurate estimations of the state and total disturbance. The proposed nonlinearity also reduces the chattering issue with LESOs. This research thoroughly examines and analyzes the proposed SMHOESO’s convergence using the Lyapunov technique. According to this analysis, the SMHOESO is asymptotically stable, and the estimation error can be significantly reduced under real-world conditions. In addition to the SMHOESO, a modified Active Disturbance Rejection Control (ADRC) scheme is built, which includes a nonlinear state error feedback (NLSEF) controller and a nonlinear tracking differentiator (TD). Several nonlinear models, including the Differential Drive Mobile Robot (DDMR), are numerically simulated, and the proposed SMHOESO is compared to several alternative types, demonstrating a significant reduction in controller energy, increased control signal smoothness, and accurate tracking of the reference signal.

Performance assessment of tripping and drilling operations controllers on an experimental drilling rig prototype

Oil well drilling towers have different operating modes during a real operation, each mode involves certain external disturbances and uncertainties. Performance evaluation of robust or adaptive Cascade PID, Active Disturbance Rejection, Loop Shaping, Feedback Error Learning, and Sliding Mode torque controllers, during the tripping and drilling operations, and their practical comparison are studied and evaluated by constructing a drilling rig prototype. The modeling of the experimental setup is extracted by mathematical modeling, and system identification. The practical performance of the controllers and their stabilities against the uncertain forces including the parametric uncertainties and the external disturbances are studied during the operations, by loading and unloading a disturbance weight. It has been shown that the effects of uncertain forces are successfully eliminated by the controllers. The Loop Shaping controller has the best performance among all the designed controllers, and all of them roughly consume the same control energy. A desired speed profile is designed to shape the vertical speed reference during the tripping operation, then its effect on the system behavior is analyzed to prevent the slackening problem in the drilling cable. Also, the behavior of control architectures in two modes of autonomous drilling is studied and analyzed. By analyzing and optimizing the performance efficiency in a controlled environment, along with enhancing the performances of the controllers, what we learn in this research could presumably be applicable in the field to have an accurate and safe operation.

Transient Response Characteristics Improvement of Permanent Magnet Synchronous Motor Based on Enhanced Linear Active Disturbance Rejection Sensorless Control

The compactness, cost decrease, and reliability of the auxiliary drive permanent magnet synchronous motors (PMSMs) for battery electric commercial vehicles can be further improved by adopting the position sensorless control technique. The conventional extended back electromotive force (EEMF) based sensorless control algorithm usually requires a phase-locked loop (PLL) and low pass filter to postprocess the estimated rotor position, which deteriorates the transient response of the PMSM significantly. To solve this problem, this article proposes an active disturbance rejection control based EEMF sensorless control method. By designing an extended state observer (ESO) considering the differential term of the position estimation error, timely and accurate estimation of the load disturbance and rotor position can be achieved simultaneously. The adaptive mechanism of the ESO exhibits sound system antidisturbance performance. Moreover, a linear state error feedback controller and feedforward compensation of load disturbance can replace the speed loop PI controller accordingly. Through stability and tracking performance analysis, the proposed method is superior in both steady-state performance and transient-state performance. The proposed scheme is experimentally validated by a 3 kW automotive power steering oil pump motor drive system. The results show that the transient response characteristics are obviously enhanced compared with the conventional methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Age-related changes to electroencephalographic markers of visuomotor error processing and learning in prism adaptation

Aging is associated with changes in cognitive function, including declines in learning, memory, and executive function. Prism adaptation (PA) is a useful paradigm to measure changes in explicit and implicit mechanisms of visuo-motor learning with age, but the neural correlates are not well understood. In the present study, we used PA to investigate visuo-motor learning and error processing in older adults. Twenty older adults (56–85 yrs) and 20 younger adults (18–33 yrs) underwent a goal-oriented reaching task while wearing prism goggles as continuous EEG was recorded to examine neural correlates of error detection. We examined behavioural measures of PA, as well as ERP components previously found associated with the early and late phases of adaptation to visual distortion caused by the prism goggles. Our results indicate important age-related behavioural and neurophysiological differences. Older adults reached more slowly than younger adults but showed the same accuracy throughout the prism exposure. Older adults also displayed larger aftereffects, indicating preserved visuomotor adaptation. EEG results indicated similar initial error processing in older and younger adults, as measured by the feedback error related negativity (FRN). As seen previously in young adults, the P3a and P3b declined over the prism exposure phase in both groups. Older adults displayed reduced P3a amplitude compared to the younger group in the early phase of adaptation, however, suggesting reduced attentional orienting. Finally, the older group exhibited a greater P3b amplitude compared to the younger group in the later phases of adaptation, potentially a marker of enhanced context updating underlying spatial realignment, leading to their larger aftereffect. Implications for age-related learning differences and clinical applications are discussed.

A Practical Deceleration Control Method, Prototype Implementation and Test Verification for Rail Vehicles

Currently, the theoretical braking force control mode, characterized by actual deceleration as an unstable open-loop output, is the most widely used brake control mode in trains. To overcome the shortcomings of non-deceleration control modes, a deceleration control mode is proposed to realize the closed-loop control of train deceleration. First, a deceleration control algorithm based on parameter estimation was derived. Then, the deceleration control software logic was designed based on the existing braking system to meet the engineering requirements. Finally, the deceleration control algorithm was verified through a ground combination test bench with real brake control equipment and pneumatic brakes. The test results show that the deceleration control can make the actual braking deceleration of the train accurately track the target deceleration in the presence of disturbances, such as uncertain brake pad friction coefficients, line ramps, vehicle loads and braking force feedback errors, as well as their combined effects, and does not affect the original performance of the braking system. The average deceleration in the deceleration control mode is relatively stable, and the control error of instantaneous deceleration is smaller.

Robust Liquid Level Control of Quadruple Tank System: A Nonlinear Model-Free Approach

In this paper, two new versions of modified active disturbance rejection control (MADRC) are proposed to stabilize a nonlinear quadruple tank system and control the water levels of the lower two tanks in the presence of exogenous disturbances, parameter uncertainties, and parallel varying input set-points. The first proposed scheme is configured from the combination of a modified tracking differentiator (TD), modified super twisting sliding mode (STC-SM), and modified nonlinear extended state observer (NLESO), while the second proposed scheme is obtained by aggregating another modified TD, a modified nonlinear state error feedback (MNLSEF), and a fal-function-based ESO. The MADRC schemes with a nonlinear quadruple tank system are investigated by running simulations in the MATLAB/SIMULINK environment and several comparison experiments are conducted to validate the effectiveness of the proposed control schemes. Furthermore, the genetic algorithm (GA) is used as a tuning algorithm to parametrize the proposed MADRC schemes with the integral time absolute error (ITAE), integral square of the control signal (ISU), and integral absolute of the control signal (IAU) as an output performance index (OPI). Finally, the simulation results show the robustness of the proposed schemes with a noticeable reduction in the OPI.

Aberrant modulation of broadband neural oscillations reflects vocal sensorimotor deficits in post-stroke aphasia

ObjectiveThe present study investigated the neural oscillatory correlates of impaired vocal sensorimotor control in left-hemisphere stroke. MethodsElectroencephalography (EEG) signals were recorded from 34 stroke and 46 control subjects during speech vowel vocalization and listening tasks under normal and pitch-shifted auditory feedback. ResultsTime-frequency analyses revealed aberrantly decreased theta (4–8 Hz) and increased gamma band (30–80 Hz) power in frontal and posterior parieto-occipital regions as well as reduced alpha (8–13 Hz) and beta (13–30 Hz) desynchronization over sensorimotor areas before speech vowel vocalization in left-hemisphere stroke compared with controls. Subjects with the stroke also presented with aberrant modulation of broadband (4–80 Hz) neural oscillations over sensorimotor regions after speech vowel onset during vocalization and listening under normal and altered auditory feedback. We found that the atypical pattern of broadband neural oscillatory modulation was correlated with diminished vocal feedback error compensation behavior and the severity of co-existing language-related aphasia symptoms associated with left-hemisphere stroke. ConclusionsThese findings indicate complex interplays between the underlying mechanisms of speech and language and their deficits in post-stroke aphasia. SignificanceOur data motivate the notion of studying neural oscillatory dynamics as a critical component for the examination of speech and language disorders in post-stroke aphasia.

Improved Position Sensorless Control for PMLSM via an Active Disturbance Rejection Controller and an Adaptive Full-Order Observer

Position and speed information are very important to realize the efficient control for permanent magnet linear synchronous motor (PMLSM). In order to obtain more accurate position estimation signals, this article investigates an improved position sensorless control method for PMLSM by combining an active disturbance rejection control (ADRC)-based speed controller and an adaptive full-order observer (AFO)-based position estimator. The AFO is constructed based on model reference adaptive system (MRAS), which can obtain more accurate position estimation signal by introducing a current error feedback gain matrix. However, there are lots of thrust fluctuations existing in PMLSM caused by end effect, which will affect the estimation accuracy. Since it is difficult to reduce the influence of thrust fluctuation by only improving the observer itself, an ADRC-based speed controller is designed to improve the anti-interference ability of control system, thereby improving the position estimation accuracy. For the ADRC controller, an second-order linear extended state observer (LESO) is designed to observe the thrust fluctuation and an nonlinear state error feedback (NLSEF) control law is introduced to compensate the observed disturbance. The effectiveness and superiority of the proposed method have been verified by simulation and experimental results.

Evolving Error Feedback Fuzzy Model for Improved Robustness Under Measurement Noise

In this article, we propose a new variant of evolving fuzzy model for regression problems, which is based on error feedback integration in order to compensate measurement noise and to achieve more robust predictions; thus, it is termed as evolving error feedback fuzzy model. Thereby, we define an autoregressive-based noise model, localized per rule for being able to model different possible noise behaviors in different parts of the input space. Its predictions are added to the real fuzzy model predictions for noise correction purposes. This additive aspect leads to an extended version of the recursive fuzzily weighted least squares estimator (RFWLS-N = RFWLS with noise compensation), inheriting all its favorable convergence properties. For rule antecedent learning, we perform an evolving clustering process with the integration of a new forgetting strategy to be able to achieve more flexible updates in order to account for possible drifts. Therefore, a specific sample weighting concept is designed, with which different forgetting types can be achieved, ranging from (slow) logarithmic forgetting to (uniformly) linear forgetting and to (fast) exponential forgetting. The integration of the weights into the updates of rule centers and inverse covariance matrices is underlined with a theoretical analysis of their sample-wise contributions in the updates. Results on several noise-affected real-world datasets and system identification problems showed significantly improved model performances, compared to related evolving fuzzy methods, to the conventional RFWLS estimator and to the recursive correntropy approach. Antecedent forgetting helped us to improve the error trends in the case of abrupt drifts, while it never worsened the trends in the case of no drift.

Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning

Deep neural networks (DNNs) have achieved remarkable success in many fields. However, large-scale DNNs also bring storage costs when storing snapshots for preventing clusters’ frequent failures or incur significant communication overheads when transmitting DNNs in the Federated Learning (FL). Recently, several approaches, such as Delta-DNN and LC-Checkpoint, aim to reduce the size of DNNs’ snapshot storage by compressing the difference between two neighboring versions of the DNNs (a.k.a., delta). However, we observe that existing approaches, applying traditional global lossy quantization techniques in DNN's delta compression, can not fully exploit the data similarity since the parameters’ value ranges vary among layers. To fully explore the similarity of the delta model and improve the compression ratio, we propose a quantization-based local-sensitive delta compression approach, named QD-Compressor, by developing a layer-based local-sensitive quantization scheme and error feedback mechanism. Specifically, the quantizers and number of quantization bits are adaptive among layers based on the value distribution and weighted entropy of the delta's parameters. To avoid quantization error degrading the performance of the restored model, an alternative error feedback mechanism is designed to dynamically correct the quantization error during the training process. Experiments on multiple popular DNNs and datasets show that QD-Compressor obtains a higher 7×-40× compression ratio in the model snapshot compression scenario than the state-of-the-art approaches. Additionally, QD-Compressor achieves an 11×-15× compression ratio to the residual model of the Federated Learning compression scenario.

Fault-Tolerant Attitude Control Incorporating Reconfiguration Control Allocation for Supersonic Tailless Aircraft

This paper presents a fault-tolerant attitude control scheme, incorporating reconfiguration control allocation for supersonic tailless aircraft subject to nonlinear characteristics, actuator constraint, uncertainty, and actuator faults. The main idea is to propose an incremental reconfiguration closed-loop control allocation scheme, coupled with a basic backstepping attitude controller, to achieve attitude control. Based on the virtual control input generated by the basic backstepping attitude controller, firstly, the incremental nonlinear control allocation method is adopted to deal with the nonlinear characteristics and actuator constraint. Secondly, a distribution error feedback loop is constructed in the incremental nonlinear control allocation method to enhance the robustness against the uncertainty of the control effectiveness matrix. Thirdly, the control effectiveness matrix is reconstructed by different kinds of fault information to deal with actuator faults, and the proper combination of actuator deflections is generated to achieve accurate command tracking. The stability of the proposed scheme is guaranteed by the Jury stability criterion and the Lyapunov stability analysis. Finally, in comparison with the three existing approaches, the simulation results of two cases are provided to show the effectiveness of the proposed scheme.

Does E-Feedback Impact Minimizing ESL Writing Errors? An Experimental Study

Researches around the world have examined how electronic feedback (e-feedback) assists English (ESL) language learners improve their writing by minimizing errors. This current study examines the use of online written peer feedback (OWPF) and automated written corrective feedback (AWCF), analyzing morphological and syntactical errors in writing. The study used a quantitative method to analyze the data. The study participants included one hundred forty-one first-year senior students. They were divided into three groups: two experimental groups received treatment through OWPF and AWCF, and one control group received through the communicative language teaching approach. Regarding writing improvement and minimizing morphological and syntactic errors, the data show that OWPF is significantly more helpful than AWCF and CLT in maximizing writing effectiveness. The study explores and highlights the implications of the study’s results for pedagogical training and future research.