Explaining Parkinsonian postural sway variabilities using intermittent control theory

Output information-based intermittent optimal control for continuous-time nonlinear systems with unmatched uncertainties via adaptive dynamic programming

Intermittent control stands as a valuable strategy for resource conservation and cost reduction across diverse systems. Nonetheless, prevailing research is intractable to address the challenges posed by robust optimal intermittent control of nonlinear input-affine systems with unmatched uncertainties. This paper aims to fill this gap. Initially, we introduce an enhanced finite-time intermittent control approach to ensure stability within nonlinear dynamic systems harboring bounded errors. A neural networks (NNs) state observer is constructed to estimate system information. Subsequently, an optimal intermittent controller that operates within a finite time span, guaranteeing system stability by employing the Hamilton-Jacobi-Bellman (HJB) methodology. Furthermore, we devise an output information-based event-triggered intermittent (ETI) approach rooted in the robust adaptive dynamic programming (ADP) algorithm, furnishing an optimal intermittent control law. In this process, a critic NNs is introduced to estimate the cost function and optimal intermittent controller. Simulation results show that our proposed method is superior to existing intermittent control strategies.

An improved intermittent control model of postural sway during quiet standing implemented by a data driven approach

This paper proposes a new intermittent control model during human quiet standing, which is consisted of postulated "regular intervention" and "imminent intervention". The regular intervention is within the main control loop, and its trigger condition is equivalent to the switching frequency of center of pressure (COP) data calculated by wavelet transform. The imminent intervention will only be triggered after the postural sway angle exceeds a certain threshold. In order to prove the effectiveness of the new model, the simulation results of the new model and the model proposed by Asai et al. (2009) are compared with the experimental data. The setting parameters of both models are retrieved by Bayesian regression from the experimental data. The results show that the new model not only could exhibit two power law scaling regimes of power spectral density (PSD) of COP, but also show that indices of the probability density function distance, root mean square (RMS), Total Sway Path, displacement Range, 50% power frequency of center of mass (COP) between the simulation results and the experimental data are closer compared to the existing model. Moreover, the limit cycle oscillations (LCOs) obtained from the simulation results of the new model have a higher degree of matching with those retrieved from the experimental data.

Optimal guaranteed cost intermittent control to the efficient movement of freight trains

Freight train system is under constant pressure to optimize operational efficiency, a factor that has led the sector to spearhead many new technologies. To address this, the optimal guaranteed cost intermittent cruise control of freight train system with time-varying uncertain parameters is investigated. In particular, the guaranteed cost intermittent control approach is proposed, which can achieve both advantages of guaranteed cost control and intermittent control methods. In addition, the acceptable conditions for the guaranteed cost control laws are weakened to insure that the proposed strategy is suitable for the application in freight train systems. Moreover, to promote the optimal control design in the freight train system, the proposed optimal guaranteed cost intermittent control for the nonlinear system subjected to norm bounded parametric uncertainties is addressed. The results of numerical experiments are presented to ascertain the effectiveness of the proposed freight train control methodology and to compare it to prior methods.

Change in task conditions leads to changes in intermittency in intermittent feedback control employed by CNS in control of human stance

Event-driven intermittent feedback control is a form of feedback control in which the corrective control action is only initiated intermittently when the variables of interest exceed certain threshold criteria. It has been reported in the literature that the CNS uses an event-driven intermittent control strategy to stabilize the human upright posture. However, whether the threshold criteria may change under different postural task conditions is not yet well understood. We employ a numerical study with inverted pendulum models and an experimental study with 51 young healthy individuals (13 females and 38 males; age: 27.8 ± 6.5 years) with stabilogram-diffusion, temporal and spectral analysis applied to COP (Center of Pressure) trajectories measured from these experiments to examine this aspect. The present study provides compelling evidence that inducing a natural arm swing during quiet stance appears to lead to higher sensory dead zone in neuronal control reflecting higher intermittency thresholds in active feedback control and a corresponding lower sensory dependence. Beyond the obvious scientific interest in understanding this aspect of how CNS controls the standing posture, an investigation of the said control strategy may subsequently help uncover insights about how control of quiet stance degrades with age and in diseased conditions. Additionally, such an understanding will also be of interest to the humanoid robotics community as it may lead to insights leading to improving control strategies for posture control in robots.

Postural Control Adaptations in Yoga Single-Leg Support Postures: Comparison Between Practitioners and Nonpractitioners

This paper investigates whether a group of regular Yoga practitioners shows postural control differences compared with healthy controls while performing single-leg Yoga postures. Ten Yoga practitioners were compared with a control group of 10 nonpractitioners performing two single-leg support Yoga postures: Vrksasana (tree posture) and Natarajasana (dancer posture). Rambling and trembling decomposition of the center of pressure trajectories was implemented using a genetic algorithm spectral optimization that avoids using horizontal forces and was validated with bipedal posture data. Additionally, the center of mass was estimated from body kinematics using OpenSim and compared with the rambling outputs. During Natarajasana, no postural control adaptations were observed. For Vrksasana, the Yoga practitioners showed a lower center of pressure ellipse confidence interval area, center of pressure anteroposterior SD, and smaller rambling SD in the mediolateral direction, suggesting possible supraspinal feed-forward motor adaptations associated with Yoga training.

Postural Adjustments during Interactions with an Active Partner

Ensuring stability of the human vertical posture is a complex task requiring both anticipatory and compensatory postural strategies when a standing person performs fast actions and interacts with the environment, which can include other persons. How people adjust their preparatory and compensatory postural adjustments in situations when they interact with an active partner is still poorly understood. In this study we investigated the postural adjustments while two healthy persons played a traditional childhood game. While standing facing each other, they were asked to push with their hands against the hands of the opponent only, and to make the opponent to take a step. We explored strategies when pushing the opponent's hands generated perturbations to the posture of both players and when one of the players withdrew the arms to neutralize the opponent's pushing action. Electromyograms were recorded from the leg and trunk muscles and used to quantify early (EPAs), anticipatory (APAs) and compensatory (CPAs) postural adjustments, as well as the co-activation and reciprocal changes in the activity of agonist-antagonist pairs. Results showed higher indices of muscle co-activation during EPAs during the game compared to the control conditions. We found that postural preparation strategies defined whether a participant kept or lost balance during the game. Our results highlight the importance of muscle co-activation, the role of anticipation, and the difference in strategies while interacting with an active partner as compared to interactions with passive objects.

Postural instability via a loss of intermittent control in elderly and patients with Parkinson's disease: A model-based and data-driven approach

Postural instability is one of the major symptoms of Parkinson's disease. Here, we assimilated a model of intermittent delay feedback control during quiet standing into postural sway data from healthy young and elderly individuals as well as patients with Parkinson's disease to elucidate the possible mechanisms of instability. Specifically, we estimated the joint probability distribution of a set of parameters in the model using the Bayesian parameter inference such that the model with the inferred parameters can best-fit sway data for each individual. It was expected that the parameter values for three populations would distribute differently in the parameter space depending on their balance capability. Because the intermittent control model is parameterized by a parameter associated with the degree of intermittency in the control, it can represent not only the intermittent model but also the traditional continuous control model with no intermittency. We showed that the inferred parameter values for the three groups of individuals are classified into two major groups in the parameter space: one represents the intermittent control mostly for healthy people and patients with mild postural symptoms and the other the continuous control mostly for some elderly and patients with severe postural symptoms. The results of this study may be interpreted by postulating that increased postural instability in most Parkinson's patients and some elderly persons might be characterized as a dynamical disease.