Visuomotor feedback tuning in the absence of visual error information

A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system reacts to the change in dynamics by upregulating stiffness and feedback gains to reduce the effect of model errors. However, these feedback gain increases have also been suggested to represent part of the adaptation mechanism. Here, we investigate this by examining changes in visuomotor feedback gains during gradual or abrupt force field adaptation. Participants grasped a robotic manipulandum and reached while a curl force field was introduced gradually or abruptly. Abrupt introduction of dynamics elicited large initial increases in kinematic error, muscle co-contraction and visuomotor feedback gains, while gradual introduction showed little initial change in these measures despite evidence of adaptation. After adaptation had plateaued, there was a change in the co-contraction and visuomotor feedback gains relative to null field movements, but no differences between the abrupt and gradual introduction of dynamics. This suggests that the initial increase in feedback gains is not part of the adaptation process, but instead an automatic reactive response to internal model uncertainty. In contrast, the final level of feedback gains is a predictive tuning of the feedback gains to the external dynamics as part of the internal model adaptation. Together, the reactive and predictive feedback gains explain the wide variety of previous experimental results of feedback changes during adaptation.

Active disturbance rejection control of ship course keeping based on nonlinear feedback and ZOH component

Previous research has questioned whether motor adaptation is shaped by an optimal combination of multisensory error signals. Here, we expanded on this work by investigating how the use of visual and somatosensory error signals during online correction influences single-trial adaptation. To this end, we exposed participants to a random sequence of force-field perturbations and recorded their corrective responses as well as the after-effects exhibited during the subsequent unperturbed movement. In addition to the force perturbation we artificially decreased or increased visual errors by multiplying hand deviations by a gain smaller or larger than one. Corrective responses to the force perturbation clearly scaled with the size of the visual error, but this scaling did not transfer one-to-one to motor adaptation and we observed no consistent interaction between limb and visual errors on adaptation. However, reducing visual errors during perturbation led to a small reduction of after-effects and this residual influence of visual feedback was eliminated when we instructed participants to control their hidden hand instead of the visual hand cursor. Taken together, our results demonstrate that task instructions and the need to correct for errors during perturbation are important factors to consider if we want to understand how the sensorimotor system uses and combines multimodal error signals to adapt movements.Significance StatementWe investigated the factors influencing visual and proprioceptive feedback contributions to movement control and adaptation. While online corrections increased with the size of visual errors, this scaling did not transfer one-to-one to adaptation. Instead, we observed a consistent relationship between limb displacement during perturbation and subsequent after-effects which was independent of the visual error. However, adaptation was slightly reduced when visual errors were artificially decreased and this influence of vision was modulated by the task-instruction given to participants. Our results demonstrate that task-related factors, such as the need to correct movement errors and the instructions, have to be considered to advance our understanding of how the sensorimotor system uses multisensory feedback to adapt motor commands.

Sliding mode adaptive control for ship path following with sideslip angle observer

Robust power control design for a small pressurized water reactor using an H infinity mixed sensitivity method

<p>The quantification of internal variability and model uncertainty sources in Multi-scenario Multi-model Ensembles of climate experiments (MMEs) is a key issue. It is expected to both help decision makers to identify robust adaptation measures and scientists to identify where their efforts are needed to narrow uncertainty. The setup of available MMEs makes however uncertainty analyses difficult. In the popular single-time ANOVA approach for instance, a precise estimate of internal variability requires multiple members for each simulation chain (e.g. each emission scenario/climate model combination) but multiple members are typically available for a few chains only (Hingray et al. 2019). In almost all ensembles also, the matrix of available scenario/models combinations is incomplete making a precise estimate of the main effects of each model difficult (e.g. projections are typically missing for some GCM/RCM combinations) (Evin et al. 2019).</p><p>We present QUALYPSO, a Bayesian approach developed to assess the different sources of uncertainty in incomplete MMEs (Evin et al. submitted). It is based on the quasi-ergodic assumption for transient climate projections and uses data augmentation (Hingray and Said, 2014). The climate response of each available simulation chain is first estimated with a trend model fitted to raw climate projections. Residuals from the climate change response are used to estimate the internal variability of the chain. Scenario uncertainty and the different components of model uncertainty (e.g. GCM uncertainty, RCM uncertainty) are then estimated with a Bayesian ANOVA model applied to the climate change responses of all available chains. The different parameters of the ANOVA model and the missing quantities associated to the missing chains (e.g. missing scenario/GCM/RCM combinations) are jointly estimated using data augmentation techniques.</p><p>QUALYPSO presents many advantages over classical estimation approaches. It first exploits all available experiments, avoiding a dramatic loss of information (the classical case when standard approaches are applied; where the typical solution is to select a complete subset of climate experiments). Along with the estimation of missing data, it also provides an assessment of the estimation uncertainty and adequately propagates the uncertainty due to missing chains. With the explicit treatment of missing experiments, it is then expected to produce unbiased estimates of all parameters, in contrast to direct empirical estimates.</p><p>QUALYPSO can be applied to any kind of climate variable and any kind of MMEs. We present examples of application for different hydroclimatic variables from different ensembles of projections including EUROCORDEX and CORDEX-Africa.</p><p>Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate.</p><p>Hingray, B., Blanchet, J., Evin, G. Vidal, J.P. 2019. Uncertainty components estimates in transient climate projections. Precision of estimators in the single time and time series approaches. Clim.Dyn.</p><p>Evin, G., Hingray, B., Blanchet, J., Eckert, N., Morin, S., Verfaillie, D. 2019. Partitioning uncertainty components of an incomplete ensemble of climate projections using data augmentation. J.Climate.</p><p>Evin, G., Hingray, B. Blanchet, J., Eckert, N., Menegoz, M. Morin, S. (revision). Partitioning uncertainty components of an incomplete ensemble of climate projections using smoothing splines. J.Climate.</p>

A simple and robust framework is proposed for the partitioning of the different components of internal variability and model uncertainty in an unbalanced multimember multimodel ensemble (MM2E) of climate projections obtained for a suite of statistical downscaling models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of the different modeling chains using a two-way analysis of variance (ANOVA) framework. The residuals from the noise-free signals are used to estimate the large- and small-scale internal variability components associated with each considered GCM–SDM configuration. This framework makes it possible to take into account all members available from any climate ensemble of opportunity. Uncertainty is quantified as a function of lead time for projections of changes in temperature and precipitation produced for a mesoscale alpine catchment. Internal variability accounts for more than 80% of total uncertainty in the first decades. This proportion decreases to less than 10% at the end of the century for temperature but remains greater than 50% for precipitation. Small-scale internal variability is negligible for temperature; however, it is similar to the large-scale component for precipitation, whatever the projection lead time. SDM uncertainty is always greater than GCM uncertainty for precipitation. It is also greater for temperature in the middle of the century. The response-to-uncertainty ratio is very high for temperature. For precipitation, it is always less than one, indicating that even the sign of change is uncertain.

Smooth pursuit (SP) eye movements are used to maintain the image of a moving object on or near the fovea. Visual motion signals aid in driving SP and are necessary for its adaptation. The sources of visual error signals that support SP adaptation are incompletely understood but could involve neurons in cortical and brain stem areas with direction selective visual motion responses. Here we focus on the pretectal nucleus of the optic tract (NOT), which encodes retinal error information during SP. The aim of this study was to characterize the role of the NOT in SP adaptation. SP adaptation is typically produced using a double step of velocity ramp (double-step paradigm), where target speed either increases or decreases 100 ms after the beginning of a trial. In our study, we delivered a brief (200 ms) train of microelectrical stimulation (ES) in the left NOT to introduce directional error signals at the point in time where a second target speed would appear in a double-step paradigm. The target was extinguished coincidentally with the onset of the ES train. Initial eye acceleration (1st 100 ms) showed significant increases after 100 trials, which included left NOT stimulation during ongoing pursuit in an ipsiversive (leftward) direction. In contrast, initial eye acceleration showed significant decreases after repeated left NOT stimulation during contraversive (rightward) SP. Control studies performed using the same periodicity of NOT stimulation as in the preceding text but without accompanying SP did not induce changes in eye acceleration. In contrast, ES of the NOT paired with active SP produced gradual changes in eye acceleration similar to that observed in double-step paradigm. Therefore our findings support the suggestion that the NOT is an important source of visual error information for guiding motor learning during horizontal SP.

Conventional repetitive controller has a prominent capability for tracking any periodic reference signal or attenuating periodically distributed signal due to its infinite dimensional property and the condition for that is the period of the periodic signal should be equal to the amount of delay in RC loop. But due to this infinite dimensional property, the practical system remains unstable in higher frequency range. Small changes in the frequency and phase of input signals results in instability. This paper presents a multiloop approach to compute High-Order Repetitive Controllers (HORC) that results in delivering a robust performance in terms of stability and model uncertainty along with internal uncertainty and unstructured model uncertainty. Here, the LTI system deals with multiplicative parametric uncertainty. A novel multiple loop structure is proposed to make the system robust enough to reject disturbance signal and stable in the presence of internal uncertainty and model uncertainty. The ability of disturbance rejection of the proposed HORC is performed through MATLAB simulation. A comparative study has been carried out among RC, FDRC and HORC.

This paper is concerned with the coordination tracking control for space manipulator system with internal model uncertainties and external environment disturbances. A novel robust anti -disturbance coordination control scheme is proposed to realize feed-forward control of the base satellite attitude and precise trajectory tracking of the manipulator. The composite controller is composed of disturbance observer based controller (DOBC), <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H_{\infty}$</tex> feedback controller and computed-momentum based reaction compensation (CMRC) controller; where the DOBC estimate and compensate the external disturbances, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H_{\infty}$</tex> feedback controller attenuate the internal model uncertainties, then the computed-momentum based reaction compensation (CMRC) controller is designed as a feed-forward coordination controller to actively anticipate the manipulator reaction and compensate for it in advance. Numerical simulations are performed on a two-link free-flying space manipulator system to demonstrate the effectiveness and validity of the proposed control scheme.

Robotic algorithms that augment movement errors have been proposed as promising training strategies to enhance motor learning and neurorehabilitation. However, most research effort has focused on rehabilitation of upper limbs, probably because large movement errors are especially dangerous during gait training, as they might result in stumbling and falling. Furthermore, systematic large movement errors might limit the participants’ motivation during training. In this study, we investigated the effect of training with novel error modulating strategies, which guarantee a safe training environment, on motivation and learning of a modified asymmetric gait pattern. Thirty healthy young participants walked in the exoskeletal robotic system Lokomat while performing a foot target-tracking task, which required an increased hip and knee flexion in the dominant leg. Learning the asymmetric gait pattern with three different strategies was evaluated: (i) No disturbance: no robot disturbance/guidance was applied, (ii) haptic error amplification: unsafe and discouraging large errors were limited with haptic guidance, while haptic error amplification enhanced awareness of small errors relevant for learning, and (iii) visual error amplification: visually observed errors were amplified in a virtual reality environment. We also evaluated whether increasing the movement variability during training by adding randomly varying haptic disturbances on top of the other training strategies further enhances learning. We analyzed participants’ motor performance and self-reported intrinsic motivation before, during and after training. We found that training with the novel haptic error amplification strategy did not hamper motor adaptation and enhanced transfer of the practiced asymmetric gait pattern to free walking. Training with visual error amplification, on the other hand, increased errors during training and hampered motor learning. Participants who trained with visual error amplification also reported a reduced perceived competence. Adding haptic disturbance increased the movement variability during training, but did not have a significant effect on motor adaptation, probably because training with haptic disturbance on top of visual and haptic error amplification decreased the participants’ feelings of competence. The proposed novel haptic error modulating controller that amplifies small task-relevant errors while limiting large errors outperformed visual error augmentation and might provide a promising framework to improve robotic gait training outcomes in neurological patients.

1. Voluntary saccadic eye movements were made toward flashes of light on the horizontal meridian, whose duration and distance from the point of fixation were varied; eye movements were measured using d.c.-electrooculography.--2. Targets within 10°---15° eccentricity are usually reached by one saccadic eye movement. When the eyes turn toward targets of more than 10°---15° eccentricity, the first saccadic eye movement falls short of the target by an angle usually not exceeding 10°. The presence of the image of the target off the fovea (visual error signal) subsequent to such an undershoot elicits, after a short interval, corrective saccades (usually one) which place the image of the target on the fovea. In the absence of a visual error signal, the probability of occurrence of corrective saccades is low, but it increases with greater target eccentricities. These observations suggest that there are different, eccentricity-dependent modes of programming saccadic eye movements.--3. Saccadic eye movements appear to be programmed in retinal coordinates. This conclusion is based on the observations that, irrespective of the initial position of the eyes in the orbit, a) there are different programming modes for eye movements to targets within and beyond 10°---15° from the fixation point, and b_ the maximum velocity of saccadic eye movements is always reached at 25° to 30° target eccentricity. --4. Distributions of latency and intersaccadic interval (ISI) are frequently multimodal, with a separation between modes of 30 to 40 msec. These observations suggest that saccadic eye movements are produced by mechanisms which, at a frequency of 30 Hz, process visual information. --5. Corrective saccades may occur after extremely short intervals (30 to 60 msec) regardless of whether or not a visual error signal is present; the eyes may not even come to a complete stop during these very short intersaccadic intervals. It is suggested that these corrective saccades are triggered by errors in the programming of the initial saccadic eye movements, and not by a visual error signal. --6. The exitence of different, eccentricity-dependent programming modes of saccadic eye movements, is further supported by anatomical, physiological, psychophysical, and neuropathological observations that suggest a dissociation of visual functions dependent on retinal eccentricity. Saccadic eye movements to targets more eccentric than 10°---15° appear to be executed by a mechanism involving the superior colliculus (perhaps independent of the visual cortex), whereas saccadic eye movements to less eccentric targets appear to depend on a mechanism involving the geniculo-cortical pathway (perhaps in collaboration with the superior colliculus).

Movement accuracy depends crucially on the ability to detect errors while actions are being performed. When inaccuracies occur repeatedly, both an immediate motor correction and a progressive adaptation of the motor command can unfold. Of all the movements in the motor repertoire of humans, saccadic eye movements are the fastest. Due to the high speed of saccades, and to the impairment of visual perception during saccades, a phenomenon called “saccadic suppression”, it is widely believed that the adaptive mechanisms maintaining saccadic performance depend critically on visual error signals acquired after saccade completion. Here, we demonstrate that, contrary to this widespread view, saccadic adaptation can be based entirely on visual information presented during saccades. Our results show that visual error signals introduced during saccade execution–by shifting a visual target at saccade onset and blanking it at saccade offset–induce the same level of adaptation as error signals, presented for the same duration, but after saccade completion. In addition, they reveal that this processing of intra-saccadic visual information for adaptation depends critically on visual information presented during the deceleration phase, but not the acceleration phase, of the saccade. These findings demonstrate that the human central nervous system can use short intra-saccadic glimpses of visual information for motor adaptation, and they call for a reappraisal of current models of saccadic adaptation.

In this paper, a cooperative time-varying formation maneuvering problem with connectivity preservation and collision avoidance is investigated for a fleet of autonomous surface vehicles (ASVs) with position-heading measurements. Each vehicle is subject to unknown kinetics induced by internal model uncertainty and external disturbances. At first, a nonlinear state observer is used to recover the unmeasured linear velocity and yaw rate as well as unknown uncertainty and disturbances. Then, observer-based cooperative time-varying formation maneuvering control laws are designed based on artificial potential functions, nonlinear tracking differentiators, and a backstepping technique. The stability of closed-loop distributed formation control system is analyzed based on input-to-state stability and cascade stability. The salient features of the proposed method are as follows. First, cooperative time-varying formation maneuvering with the capability of connectivity preservation and collision avoidance can be achieved in the absence of velocity measurements. Second, the complexity of the cooperative time-varying formation maneuvering control laws is reduced without resorting to dynamic surface control. Third, the uncertainty and disturbance are actively rejected in the presence of position-heading measurements. Simulation results are given to substantiate the proposed output feedback control method for cooperative time-varying formation maneuvering of ASVs with connectivity preservation and collision avoidance.

The article addresses the event‐triggered obstacle avoidance control problem for autonomous surface vehicles subject to actuator faults. In order to tackle the challenges presented by unknown actuator faults, internal model uncertainties, and external disturbances, we propose a fault‐tolerant control scheme that relies on an extended state observer to estimate the unknown parameters. In addition, considering the occurrence of actuator faults, a novel event‐triggered mechanism is developed to reduce wear of actuator and ensures the performance of system. On this basis, we employ backstepping technique and an improved artificial potential function methods to develop an event‐triggered obstacle avoidance control scheme with the capability of fault tolerance. This proposed control strategy ensures the uniform ultimate boundedness of tracking errors. In contrast to existing results, the presented control strategy simultaneously holds the performance of obstacle avoidance and fault tolerance and reduce the update frequency of actuators. The effectiveness of the provided control scheme has been confirmed through simulation.