scholarly journals Closed-loop Controller Based on Reference Signal Tracking for Absence Seizures

2021 ◽  
Author(s):  
Hudong Zhang ◽  
Yuting Chen ◽  
Yan Xie ◽  
Yuan Chai
Keyword(s):  
Firing Rate ◽  
Closed Loop ◽  
Reference Signal ◽  
Absence Epilepsy ◽  
Open Loop ◽  
Recursive Least Squares ◽  
Biophysical Model ◽  
Reticular Nucleus ◽  
Cortical Region ◽  
Absence Seizures

Abstract Deep brain stimulation (DBS) targeting thalamus reticular nucleus (TRN) brain regions has been proven to play an irreplaceable role in the treatment of absence seizures. Compared with open-loop DBS, closed-loop DBS has been recognized by researchers for its advantages of significantly inhibiting seizures and having fewer side effects. However, due to the complexity of the nervous system, the mechanism of DBS control epilepsy is still unclear, which hinders the study of closed-loop DBS. In our study, based on the biophysical model jointly constituted by cortical, thalamic, and basal ganglia, we selected the 2-4 Hz spike and wave discharges (SWDs) of the cortical region as a biomarker for response to absence epilepsy, and the mean firing rate (MFR) of substantia nigra pars reticulata (SNr) was used as a reference signal for modulation of closed-loop DBS. Moreover, to obtain the linear relationship between the stimulus and the response, we adopted an algorithm that combines controlled auto-regressive (CAR) and recursive least squares (RLS), and we built a proportional integral (PI) controller to make the DBS stimulus parameters self-update to control the seizures. The numerical simulation results show that the closed-loop DBS controllers based on frequency modulation and amplitude modulation respectively not only successfully track the firing rate (FR) of SNr, but also significantly destroy the SWDs of cerebral cortex and restore it to the other two normal discharge modes.

scholarly journals Propagation of cortical activity via open-loop intrathalamic architectures: a computational analysis

2019 ◽  
Author(s):  
Jeffrey W. Brown ◽  
Aynaz Taheri ◽  
Robert V. Kenyon ◽  
Tanya Berger-Wolf ◽  
Daniel A. Llano
Keyword(s):  
Closed Loop ◽  
Neural Model ◽  
Signal Propagation ◽  
Open Loop ◽  
Thalamic Reticular Nucleus ◽  
Oscillatory Activity ◽  
Reticular Nucleus ◽  
Dorsal Thalamus ◽  
In Series ◽  
Intracortical Connectivity

AbstractPropagation of signals across the cerebral cortex is a core component of many cognitive processes and is generally thought to be mediated by direct intracortical connectivity. The thalamus, by contrast, is considered to be devoid of internal connections and organized as a collection of parallel inputs to the cortex. Here, we provide evidence that “open-loop” intrathalamic connections involving the thalamic reticular nucleus (TRN) can support propagation of oscillatory activity across the cortex. Recent studies support the existence of open-loop thalamo-reticulo-thalamic (TC-TRN-TC) synaptic motifs in addition to traditional closed-loop architectures. We hypothesized that open-loop structural modules, when connected in series, might underlie thalamic and, therefore cortical, signal propagation. Using a supercomputing platform to simulate thousands of permutations of a thalamo-reticular-cortical network and allowing select synapses to vary both by class and individually, we evaluated the relative capacities of closed- and open-loop TC-TRN-TC synaptic configurations to support both propagation and oscillation. We observed that 1) signal propagation was best supported in networks possessing strong open-loop TC-TRN-TC connectivity; 2) intrareticular synapses were neither primary substrates of propagation nor oscillation; and 3) heterogeneous synaptic networks supported more robust propagation of oscillation than their homogeneous counterparts. These findings suggest that open-loop heterogeneous intrathalamic architectures complement direct intracortical connectivity to facilitate cortical signal propagation.Significance StatementInteractions between the dorsal thalamus and thalamic reticular nucleus (TRN) are speculated to contribute to phenomena such as arousal, attention, sleep, and seizures. Despite the importance of the TRN, the synaptic microarchitectures forming the basis for dorsal thalamus-TRN interactions are not fully understood. The computational neural model we present incorporates “open-loop” thalamo-reticular-thalamic (TC-TRN-TC) synaptic motifs, which have been experimentally observed. We elucidate how open-loop motifs possess the capacity to shape the propagative properties of signals intrinsic to the thalamus and evaluate the wave dynamics they support relative to closed-loop TC-TRN-TC pathways and intrareticular synaptic connections. Our model also generates predictions regarding how different spatial distributions of reticulothalamic and intrareticular synapses affect these signaling properties.

scholarly journals Open-loop organization of thalamic reticular nucleus and dorsal thalamus: a computational model

2015 ◽  
Vol 114 (4) ◽  
pp. 2353-2367 ◽  
Author(s):  
Adam M. Willis ◽  
Bernard J. Slater ◽  
Ekaterina D. Gribkova ◽  
Daniel A. Llano
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Tunable Filter ◽  
Cortical Activation ◽  
Thalamic Reticular Nucleus ◽  
Reticular Nucleus ◽  
Loop Model ◽  
Dependent Manner ◽  
Dorsal Thalamus ◽  
Low Threshold

The thalamic reticular nucleus (TRN) is a shell of GABAergic neurons that surrounds the dorsal thalamus. Previous work has shown that TRN neurons send GABAergic projections to thalamocortical (TC) cells to form reciprocal, closed-loop circuits. This has led to the hypothesis that the TRN is responsible for oscillatory phenomena, such as sleep spindles and absence seizures. However, there is emerging evidence that open-loop circuits are also found between TRN and TC cells. The implications of open-loop configurations are not yet known, particularly when they include time-dependent nonlinearities in TC cells such as low-threshold bursting. We hypothesized that low-threshold bursting in an open-loop circuit could be a mechanism by which the TRN could paradoxically enhance TC activation, and that enhancement would depend on the relative timing of TRN vs. TC cell stimulation. To test this, we modeled small circuits containing TC neurons, TRN neurons, and layer 4 thalamorecipient cells in both open- and closed-loop configurations. We found that open-loop TRN stimulation, rather than universally depressing TC activation, increased cortical output across a broad parameter space, modified the filter properties of TC neurons, and altered the mutual information between input and output in a frequency-dependent and T-type calcium channel-dependent manner. Therefore, an open-loop model of TRN-TC interactions, rather than suppressing transmission through the thalamus, creates a tunable filter whose properties may be modified by outside influences onto the TRN. These simulations make experimentally testable predictions about the potential role for the TRN for flexible enhancement of cortical activation.

scholarly journals A dual-loop tracking control approach to precise nanopositioning

2018 ◽  
Vol 25 (3) ◽  
pp. 666-674 ◽  
Author(s):  
Mohammed Altaher ◽  
Douglas Russell ◽  
Sumeet S. Aphale
Keyword(s):  
Closed Loop ◽  
Reference Signal ◽  
Open Loop ◽  
Performance Criteria ◽  
Control Technique ◽  
Loop Control ◽  
Control Approach ◽  
First Order ◽  
Integral Controller ◽  
Double Integrator

Nanopositioners are mechanical devices that can accurately move with a resolution in the nanometer scale. Due to their mechanical construction and the piezoelectric actuators popularly employed in nanopositioners, these devices have severe performance limitations due to resonance, hysteresis and creep. A number of techniques to control nanopositioners, both in open-loop and closed-loop, have been reported in the literature. Closed-loop techniques clearly outperform open-loop techniques due to several desirable characteristics, such as robustness, high-bandwidth, absence of the need for tuning and high stability, along with others. The most popular closed-loop control technique reported is one where a damping controller is first employed in an inner loop to damp the mechanical resonance of the nanopositioner, thereby increasing achievable bandwidth. Consequently, a tracking controller, typically an Integral controller or a proportional–integral controller, is implemented in the outer loop to enforce tracking of the reference signal, thereby reducing the positioning errors due to hysteresis and creep dynamics of the employed actuator. The most popular trajectory a nanopositioner is forced to track is a raster scan, which is generated by making one axis of the nanopositioner follow a triangular trajectory and the other follow a slow ramp or staircase. It is quite clear that a triangle wave (a finite velocity, zero acceleration signal) cannot be perfectly tracked by a first-order integrator and a double integrator is necessary to deliver error-free tracking. However, due to the phase profile of the damped closed-loop system, implementing a double integrator is difficult. This paper proposes a method by which to implement two integrators focused on the tracking performance. Criteria for gain selection, stability analysis, error analysis, simulations, and experimental results are provided. These demonstrate a reduction in positioning error by 50%, when compared to the traditional damping plus first-order integral tracking approach.

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

Comparison of Dc-Dc SEPIC, CUK and Flyback Converters Based LED Drivers

2020 ◽  
pp. 99-107
Author(s):  
Erdal Sehirli
Keyword(s):  
Integrated Circuit ◽  
Closed Loop ◽  
Input Voltage ◽  
Open Loop ◽  
Current Sensor ◽  
Switching Frequency ◽  
Led Driver ◽  
Advantages And Disadvantages ◽  
Led Drivers ◽  
Conduction Mode

This paper presents the comparison of LED driver topologies that include SEPIC, CUK and FLYBACK DC-DC converters. Both topologies are designed for 8W power and operated in discontinuous conduction mode (DCM) with 88 kHz switching frequency. Furthermore, inductors of SEPIC and CUK converters are wounded as coupled. Applications are realized by using SG3524 integrated circuit for open loop and PIC16F877 microcontroller for closed loop. Besides, ACS712 current sensor used to limit maximum LED current for closed loop applications. Finally, SEPIC, CUK and FLYBACK DC-DC LED drivers are compared with respect to LED current, LED voltage, input voltage and current. Also, advantages and disadvantages of all topologies are concluded.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

scholarly journals Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3653
Author(s):  
Lilia Sidhom ◽  
Ines Chihi ◽  
Ernest Nlandu Kamavuako
Keyword(s):  
Degrees Of Freedom ◽  
Closed Loop ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Parametric Identification ◽  
Recursive Least Squares ◽  
Unknown Parameters ◽  
Closed Loop Identification ◽  
Input Variables ◽  
Robotic Applications

This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

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