Neural Compensation Research Articles

Research on Adaptive Trajectory Tracking Algorithm for a Quadrotor Based on Backstepping and the Sigma-Pi Neural Network

In this paper, adaptive tracking control is applied to improve performances of an underactuated quadrotor helicopter with respect to attitude and position control. Firstly, the dynamic model is presented. Then a new trajectory tracking algorithm is designed by using the sigma-pi neural network and backstepping. The paper designs the sigma-pi neural network compensation control law and gives the Lyapunov-type stability analysis. Then the corresponding numerical simulations are performed by using MATLAB. Simulation results are shown to demonstrate the effectiveness of the proposed control strategy, which could reduce tracking error, decrease tracking time, and improve the anti-interference ability of the system.

Sliding Mode Force Control of an Electrohydraulic Servo System with RBF Neural Network Compensation

In this paper, the dynamics of an electrohydraulic servo system is analyzed. It is difficult to achieve the precise force tracking control due to its high nonlinearities and parameter uncertainties. For the accurate force tracking control, a sliding mode control algorithm with radial basis function (RBF) neural network compensation was proposed. The theory verifies that the algorithm is globally asymptotically stable. The experimental results show that the proposed algorithm is not only effective and better than the PID control in the force tracking control, but also robust to external uncertain disturbances. DOI: http://dx.doi.org/10.5755/j01.mech.25.1.21279

Increased brain entropy of resting-state fMRI mediates the relationship between depression severity and mental health-related quality of life in late-life depressed elderly

BackgroundEntropy analysis is a computational method used to quantify the complexity in a system, and loss of brain complexity is hypothesized to be related to mental disorders. Here, we applied entropy analysis to the resting-state functional magnetic resonance imaging (rs-fMRI) signal in subjects with late-life depression (LLD), an illness combined with emotion dysregulation and aging effect. MethodsA total of 35 unremitted depressed elderly and 22 control subjects were recruited. Multiscale entropy (MSE) analysis was performed in the entire brain, 90 automated anatomical labeling-parcellated ROIs, and five resting networks in each study participant. LimitationsDue to ethical concerns, all the participants were under medication during the study. ResultsRegionally, subjects with LLD showed decreased entropy only in the right posterior cingulate gyrus but had universally increased entropy in affective processing (putamen and thalamus), sensory, motor, and temporal nodes across different time scales. We also found higher entropy in the left frontoparietal network (FPN), which partially mediated the negative correlation between depression severity and mental components of the quality of life, reflecting the possible neural compensation during depression treatment. ConclusionMSE provides a novel and complementary approach in rs-fMRI analysis. The temporal-spatial complexity in the resting brain may provide the adaptive variability beneficial for the elderly with depression.

Modulation of Na+/K+- ATPase activity by triterpene 3β, 6β, 16β-trihidroxilup-20 (29)-ene (TTHL) limits the long-term secondary degeneration after traumatic brain injury in mice

Traumatic brain injury (TBI) is a public health problem characterized by a combination of immediate mechanical dysfunction of the brain tissue, and secondary damage. Based on the hypothesis that selected targets, such as Na+ K+-ATPase are involved in the secondary damage after TBI and modulation of this enzyme activity by triterpene 3β, 6β, 16β-trihidroxilup-20 (29)-ene (TTHL) supports the ethnomedical applications of this plant, we decided to investigate whether previous TTHL treatment interrupts the progression of pathophysiology induced by TBI. Statistical analyses revealed that percussion fluid injury (FPI) increased Na+,K+-ATPase activity in all isoform (α1 and α2/3) 15 min after neuronal injury. The FPI protocol inhibited Na+,K+-ATPase activity total and α1 isoform, increased [3H]MK-801 binding but did not alter Dichloro-dihydro-fluorescein diacetate (DCFH-DA) oxidation, carbonylated proteins and free –SH groups 60 min after injury. The increase of immunoreactivity of protein PKC and state of phosphorylation of at Ser16 of Na+,K+-ATPase 60 min after FPI suggest the involvement of PKC on Na+,K+-ATPase activity oscillations characterized by inhibition of total and α1 isoform. Our experimental data also revealed that natural product rich in compounds such as triterpenes (TTHL; 30 mg/kg) attenuates [3H]MK-801 binding increase, phosphorylation of the PKC and the Na+,K+-ATPase alpha 1 subunit (Ser16) induced by FPI. The previous TTHL treatment had not effect on motor disability but protected against spatial memory deficit, BDNF, TrKB expression decrease, protein carbonylation and hippocampal cell death 7 days after FPI. These data suggest that TTHL-induced reduction on initial damage limits the long-term secondary degeneration and supports neural repair or behavioral compensation after neuronal injury.

Compensatory Neural Responses to Cognitive Fatigue in Young and Older Adults.

Prolonged performance of a demanding cognitive task induces cognitive fatigue. We examined the behavioral and neural responses to fatigue-induced cognitive impairments in young and older adults. Particular emphasis was placed on whether the brain exhibited compensatory neural activity in response to cognitive fatigue. High-density EEG was recorded from a young (n = 16; 18–33 years of age) and an older (n = 18; 60–87 years of age) cohort who performed a Stroop task continuously for ∼2 h with no breaks. In the young cohort, behavioral performance declined as the experiment progressed, reflecting the deleterious effects of cognitive fatigue. Neurophysiologically, in addition to declining neural activity as cognitive fatigue developed, there is also evidence of region- and time-specific increase in neural activity, suggesting neural compensation. The compensatory activities followed patterns paralleling that of posterior-anterior shift in aging (PASA) and early to late shift in aging (ELSA) observed in cognitive aging and helped to moderate fatigue-induced behavioral deterioration. In the older cohort, behavioral performance did not decline as the experiment progressed, and neural activity either declined or stayed unchanged, showing no evidence of neural compensation, in contrast to the young. These results suggest that young and older adults coped with cognitive fatigue differently by exhibiting differential responses as a function of time-on-task at both the behavioral level and the neural level.

Experimental Evaluation of a Sectorial Fuzzy Controller Plus Adaptive Neural Network Compensation Applied to a 2-DOF Robot Manipulator

In this paper we propose a novel control architecture that employs an adaptive neural network (NN) for feedforward compensation and a sectorial fuzzy controller in the feedback loop applied to the trajectory tracking control of robot manipulators. Both simulation and experimental results are presented in comparison with the original classic Proportional-Derivative (PD) plus feedforward controller, from which this new proposal is based, and two preliminary versions of the application of feedforward sectorial fuzzy control and feedforward adaptive neural nonlinear PD control to the trajectory tracking control of a two-degree of freedom (2-DOF) robot manipulator. The proposed controller has, in general, better performance than its counterparts in terms of transient response and steady-state error while it maintains one of the main characteristics of fuzzy controllers: Its tolerance to parameter deviation.

MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator

It is very important to have maximum power point trackers for photo voltaic systems to improve their efficiency. This paper deals with the converter based maximum power point tracking by robust backstepping controller along with the neural network. The neural network provides the output reference PV voltage to the backstepping controller. Back propagation neural network is used for a standalone photovoltaic system under robust environmental conditions. Unlike Conventional solar-array mathematical model, neural network does not require any physical data for modeling since it has the superior potential to derive non-linear models without requiring the physical data of the models. In this paper the maximum power point of photovoltaic module is predicted with the simulation trained back-propagation neural network using a random set of data collected from a real photovoltaic array. The neural network based PV system with backstepping controller is modeled in MATLAB/Simulink. At different atmospheric conditions the developed model is simulated. The simulation results of PV system depict that with the proposed converter based controller, the maximum power is tracked accurately and successfully.

Dynamic trajectory-tracking control method of robotic transcranial magnetic stimulation with end-effector gravity compensation based on force sensors

PurposeThis paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment.Design/methodology/approachFirst, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems.FindingsThe gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller.Originality/valueA BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.

Activity-Induced Amyloid-β Oligomers Drive Compensatory Synaptic Rearrangements in Brain Circuits Controlling Memory of Presymptomatic Alzheimer's Disease Mice

BackgroundA consistent proportion of individuals at risk for Alzheimer’s disease show intact cognition regardless of the extensive accumulation of amyloid-β (Aβ) peptide in their brain. Several pieces of evidence indicate that overactivation of brain regions negative for Aβ can compensate for the underactivation of Aβ-positive ones to preserve cognition, but the underlying synaptic changes are still unexplored. MethodsUsing Golgi staining, we investigate how dendritic spines rearrange following contextual fear conditioning (CFC) in the hippocampus and amygdala of presymptomatic Tg2576 mice, a genetic model for Aβ accumulation. A molecular biology approach combined with intrahippocampal injection of a γ-secretase inhibitor evaluates the impact of Aβ fluctuations on spine rearrangements. ResultsEncoding of CFC increases Aβ oligomerization in the hippocampus but not in the amygdala of Tg2576 mice. The presence of Aβ oligomers predicts vulnerability to network dysfunctions, as low c-Fos activation and spine maturation are detected in the hippocampus of Tg2576 mice upon recall of CFC memory. Rather, enhanced c-Fos activation and new spines are evident in the amygdala of Tg2576 mice compared with wild-type control mice. Preventing Aβ increase in the hippocampus of Tg2576 mice restores CFC-associated spine changes to wild-type levels in both the hippocampus and amygdala. ConclusionsOur study provides the first evidence of neural compensation consisting of enhanced synaptic activity in brain regions spared by Aβ load. Furthermore, it unravels an activity-mediated feedback loop through which neuronal activation during CFC encoding favors Aβ oligomerization in the hippocampus and prevents synaptic rearrangements in this region.

Association of olfaction dysfunction with brain microstructure in prodromal Parkinson disease

Although olfaction dysfunction is now considered as an established clinical marker of prodromal Parkinson disease (PD), little is known about the neural underpinnings of olfaction dysfunction in the prodromal phase of PD. The aim of this study was to examine the microstructural association of olfaction in prodromal PD compared to early stage drug-naïve PD patients. Diffusion MRI connectometry was conducted on 18 early PD and 17 prodromal PD patients to investigate the differences in group in terms of altered connectivity, i.e., integrity of white matter tracts, and subsequently to study the correlation of University of Pennsylvania Smell Identification Test (UPSIT) score to white matter integrity in each group using a multiple regression model considering age, sex, RBD, and MoCA, as covariates. Individuals with prodromal PD had significantly higher quantitative anisotropy (QA) comparing with PD patients in bilateral middle cerebellar peduncles and right arcuate fasciculus. Multiple regression analysis in prodromal PD demonstrated positive association between UPSIT score and connectivity in left and right subgenual cingulum, right inferior fronto-occipital fasciculus, left corticospinal tract, left parietopontine, left corticothalamic tract, and the body and the splenium of corpus callosum. These results indicate that PD and prodromal PD patients, which were matched for sex, UPSIT, and MoCA scores, have different white matter fiber architecture. Thus, it is postulated that olfaction dysfunction in prodromal and early clinical phases of PD may involve distinct pathogenesis. Increased network connectivity in prodromal and early PD may suggest the neural compensation.

Identifying the Dynamics of Leg Muscle Activation During Human Gait Using Neural Oscillator and Fuzzy Compensator

Background: The goal of this study is to design a model in order to predict the muscle activation pattern because the muscle activation patterns contain valuable information about the muscle dynamics and movement patterns. Therefore, the goal of the presentation of this neural model is to identify the desired muscle activation patterns by Hopf chaotic oscillator during walking. Since the knee muscles activation has a significant effect on the movement pattern during walking, the main concentration of this study is to identify the knee muscles activation dynamics using a modeling technique. Methods: The electromyography (EMG) recording obtained from 5 healthy subjects that electrodes positioned on the tibialis-anterior (TA) and rectus femoris muscles on every 2 feet. In the proposed model, along with the chaotic oscillator, a fuzzy compensator was designed to face the unmolded dynamics. In fact, on the condition, the observed difference between the desired and actual activation patterns violate some specific quantitative ranges, the fuzzy compensator based on predefined rules modify the activity pattern produced by the Hopf oscillator. Results: Some quantitative measures used to evaluate the results. According to the achieved results, the proposed model could generate the trajectories, dynamics of which are similar to the muscle activation dynamics of the studied muscles. In this model, the generated activity pattern by the proposed model cannot follow the desired activity of the TA muscle as well as rectus femoris muscle. Conclusion: The similarity between the generated activity pattern by the model and the activation dynamics of Rectus- Femoris muscle was more in comparison with the similarity observed between activation pattern of Tibialis- Anterior and the pattern generated by the model. In other words, based on the recorded human data, the activation pattern of the Rectus- Femoris is more similar to a rhythmic pattern.

Neural network-based PID compensation for nonlinear systems: ball-on-plate example

In this paper, a neural network (NN)-based feedback controller is proposed in order to compensate for the errors caused by using an approximated dynamic model in controller design. The controller consists of two subcontrollers working in parallel: base linear controller and NN-based PID compensator. The former can be any controller that is easily designed based on the system’s linearized or simplified model. The latter is based on a PID controller with adjustable gains and a neural network is used to update the PID gains during control process, aiming to compensate for the nonlinear effects ignored in the base controller. The performance of the proposed controller is demonstrated with a ball-on-plate system built for this study. Approximate feedback linearization is used to design the base controller in this work and the NN-based PID compensator is used in parallel. Simulation and experimental results that achieve better stabilization and trajectory tracking performance are provided and discussed.

Adaptive Backstepping Fuzzy Neural Network Fractional-Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme.

Reverse Radial Bias: Temporal Orientation Bias Compensation in Early Visual Areas Revealed by MEG

Human visual system is organized sophisticatedly but it is also known that it has some response biases (e.g. color sensitivity, orientation selectivity) depending on locations of the visual field. Nevertheless, we can perceive an isotropic visual world without notifying any selectivity biases of the underlying neural responses. Therefore, a long-standing question is how our visual system compensates the visual feature selectivity anisotropies. Here, using human MEG measurements (Elekta Neuromag 360ch, 1000 Hz, N=12) and a machine learning classification (a linear SVM) technique, we explored a temporal compensation mechanism of orientation selectivity bias (aka, Radial Bias) in early visual areas. Specifically, we measured cortical responses for -45 and 45 deg gratings and visualized the dynamic changes of the orientation selectivity in each of retinotopic cortical positions during and after the stimulus on/offsets. The two orientations could be discriminated (90% accuracy) at 100 ms after the stimulus onset. Furthermore, the orientation selectivity in V1-V3, computed from SVM classifier weights, followed the presumed radial bias preference in each of retinotopic locations. However, interestingly, we found the reversal of the radial orientation bias 120 ms after the stimulus offsets, which may suggest a temporal neural compensation mechanism for the non-preferred tangential orientations. Follow-up analysis showed that the reversal was more likely to come from the temporal alteration of neural selectivity rather than the spatial response pattern shift. To explore a functional meaning of the reverse radial bias, we run additional psychophysics to measure orientation sensitivity thresholds in the reverse period and found that the orientation detection inefficiency corresponding to the radial bias was recovered exactly in the reversal period. Taken together, those findings indicate that our visual system compensates the neural selectivity bias temporally, as well as spatially, to capture transient changes of objects seen in the dynamic visual world. Meeting abstract presented at VSS 2018

Adaptive dynamical tracking control under uncertainty of shunt DC motors

A new adaptive velocity trajectory tracking control scheme for nonlinear shunt DC electric motors subjected to unmeasurable variable load torque and parametric uncertainty is proposed. Artificial neural networks and dynamical tracking error compensators are synergically combined to avoid dependence on detailed mathematical models of uncertain nonlinear systems and significantly improve the controller robustness and efficiency. The introduced robust tracking control approach can efficiently adapt to diverse uncertain operating scenarios. A priori knowledge or real-time estimation of disturbances and system parameters are unnecessary. The proposed output feedback dynamical tracking control can be extended to a wide class of controllable electric power systems operating under uncertainty. Analytical and numerical results prove the robust and efficient performance of the dynamical tracking control.

Control Surface Faults Neural Adaptive Compensation Control for Tailless Flying Wing Aircraft with Uncertainties

A neural adaptive compensation tracking control scheme considering the prescribed tracking performance bound is proposed for a flying wing aircraft with control surface faults, actuator saturation and uncertainties of aerodynamic parameters. Second-order command filters are introduced to avoid the saturation of the actuators, prescribed performance bound strategy is designed to characterize the convergence rate and maximum overshoot of the tracking error, uncertainties of aerodynamic parameters are approximated by online RBF neural networks, and control allocation law is designed to reduce the coupling of the flight dynamics. The closed-loop control law is given based on adaptive backstepping compensation control scheme, and the stability of the closed-loop system is proved by Lyapunov based design. Simulation results are given to illustrate the effectiveness of the proposed neural adaptive compensation control scheme.

Investigating the relationships between hypothalamic volume and measures of circadian rhythm and habitual sleep in premanifest Huntington's disease

ObjectivePathological changes within the hypothalamus have been proposed to mediate circadian rhythm and habitual sleep disturbances in individuals with Huntington’s disease (HD). However, investigations examining the relationships between hypothalamic volume and circadian rhythm and habitual sleep in individuals with HD are sparse. This study aimed to comprehensively evaluate the relationships between hypothalamic pathology and circadian rhythm and habitual sleep disturbances in individuals with premanifest HD. MethodsThirty-two individuals with premanifest HD and twenty-nine healthy age- and gender-matched controls participated in this dual-site, cross-sectional study. Magnetic resonance imaging scans were performed to evaluate hypothalamic volume. Circadian rhythm and habitual sleep were assessed via measurement of morning and evening cortisol and melatonin levels, wrist-worn actigraphy, the Consensus Sleep Diary and sleep questionnaires. Information on mood, physical activity levels and body composition were also collected. ResultsCompared to healthy controls, individuals with premanifest HD displayed significantly reduced grey matter volume in the hypothalamus, decreased habitual sleep efficiency and increased awakenings; however, no alterations in morning cortisol or evening melatonin release were noted in individuals with premanifest HD. While differences in the associations between hypothalamic volume and cortisol and melatonin output existed in individuals with premanifest HD compared to healthy controls, no consistent associations were observed between hypothalamic volume and circadian rhythm or habitual sleep outcomes. ConclusionWhile significant differences in associations between hypothalamic volume and cortisol and melatonin existed between individuals with premanifest HD and healthy controls, no differences in circadian markers were observed between the groups. This suggests that circadian regulation is maintained despite hypothalamic pathology, perhaps via neural compensation. Longitudinal studies are required to further understand the relationships between the hypothalamus and circadian rhythm and habitual sleep disturbances in HD as the disease course lengthens.

Aging modulates fronto-temporal cortical interactions during lexical production. A dynamic causal modeling study

In this dynamic causal modeling (DCM) study, we evaluated the effect of age on the effective connectivity of a cerebral network involved in lexical production. Younger and older adults performed an object naming task during fMRI. The DCM was used to explore the interactions between four regions of interest: the occipital cortex, OC; the lateral temporal cortex, LTC; the medial temporal cortex, MTC; and the inferior frontal cortex, IFC. We mainly focused on the modulation of the fronto-temporal interaction, according to the hypothesis that aging requires strategies that modulate the access to the semantic knowledge, either through a neural reserve mechanism (increased MTC-LTC connectivity) or through a neural compensation mechanism (supplementary IFC-MTC connectivity). For younger adults, our results indicated a bi-directional interaction between the left IFC and LTC suggesting a typical activation related to lexico-semantic representations. For older adults, our results reveal the existence of bi-directional interaction between the IFC and MTC, but not between the IFC and LTC – which in turn suggests that older adults adapt a new strategy, via supplemental access to conceptual access and semantic retrieval processes. This neural compensation strategy would be facilitated by a top-down mechanism from the IFC to the MTC. We discuss our results in the context of the possible additional strategies used by older compared to younger adults, to retrieve and produce words.

Physical exercise increases involvement of motor networks as a compensatory mechanism during a cognitively challenging task.

Neuroimaging studies suggest that older adults may compensate for declines in cognitive function through neural compensation and reorganization of neural resources. While neural compensation as a key component of cognitive reserve is an important factor that mediates cognitive decline, the field lacks a quantitative measure of neural compensatory ability, and little is known about factors that may modify compensation, such as physical exercise. Twenty-five healthy older adults participated in a 6-week dance training exercise program. Gait speed, cognitive function, and functional magnetic resonance imaging during a challenging memory task were measured before and after the exercise program. In this study, we used a newly proposed data-driven independent component analysis approach to measure neural compensatory ability and tested the effect of physical exercise on neural compensation through a longitudinal study. After the exercise program, participants showed significantly improved memory performance in Logical Memory Test (WMS(LM)) (P<.001) and Rey Auditory Verbal Learning Test (P=.001) and increased gait speed measured by the 6-minute walking test (P=.01). Among all identified neural networks, only the motor cortices and cerebellum showed greater involvement during the memory task after exercise. Importantly, subjects who activated the motor network only after exercise (but not before exercise) showed WMS(LM) increases. We conclude that physical exercise improved gait speed, cognitive function, and compensatory ability through increased involvement of motor-related networks.

MEMS gyros temperature calibration through artificial neural networks

In this paper, the application of Artificial Neural Networks to perform the thermal calibration of bias for Micro Electro-Mechanical gyros that are installed in Inertial Measurement Units is discussed. In recent years, the interest in using these systems to perform integrated inertial navigation has increased. Several new applications, related to the use of autonomous systems and personal navigation systems in GPS-challenging environments, have been developed. Thermal calibration of bias is a key issue to be assessed to achieve the best performance of a Micro Electro-Mechanical gyro. It can reduce sensor bias to one order of magnitude lower than non-calibrated conditions. Usually, thermal calibration is performed by exploiting polynomial fitting, i.e. finding the least-square polynomial that fits experimental data collected during laboratory tests in a climatic chamber. Polynomials have some drawbacks when they are applied to Micro Electro-Mechanical gyro calibration. They are not adequate to model abrupt change of bias trend in small temperature intervals and sensor hysteresis. For this reason, in the present paper, the use of Back Propagation Artificial Neural Networks is suggested as an improvement of polynomial fitting. Indeed, Neural Networks have intrinsic adaptive configurations and standard training and testing techniques, so that they can be adequately adopted for mapping thermal bias variations. In this paper, the polynomial fitting and Neural Network compensation algorithms are compared on selected testing points where the two techniques have the largest difference. Results highlight that the proposed method has better performance on these points. Therefore, the time in which the flight attitude accuracy meets the requirements imposed by the current regulations is improved by 20%.