Neural Control System Research Articles

INFLUENCE OF DIABETIC NEUROPATHY ON GAIT COMPLEXITY

ABSTRACT Introduction: Human gait is a complex movement dependent on multilevel neural control, which allows a consistent, regular and complex periodic pattern, properties that characterize it as a nonlinear system. Sensory and motor deficits, with diminished proprioceptive responses, may reduce the adaptive capacity of the system, as demonstrated in Parkinson's, Alzheimer's and Huntington's diseases. However, little is known about the effect of peripheral diabetic neuropathy on these responses. Objectives: To analyze the influence of peripheral diabetic neuropathy on entropy in different gait environments. Methods: Ten elderly patients, with and without a diagnosis of peripheral diabetic neuropathy, walked on a treadmill (initial speed of 3 km/h, with 0.5 km/h increments every 5 minutes up to the speed of 5 km/h) to record center of mass acceleration in the vertical, mediolateral and anteroposterior components throughout the test. The sample entropy of the three vectors was calculated for each test speed. Results: The vertical component did not show any statistically significant differences. The mediolateral component showed statistically significant difference for the factors group, speed, and interaction between factors (group and speed). The anteroposterior component showed statistically significant differences for the group factor, but not for speed and interaction between factors (group and speed). Effect sizes classified as large were found in all the comparisons. Conclusions: Peripheral diabetic neuropathy produced changes in the ability to adapt to changes in the environment during gait, probably due to changes in the complexity of the multilevel neural control system, which depends on motor and sensory feedback, known to be affected by peripheral diabetic neuropathy. Level of Evidence II; Diagnostic studies - Investigating a diagnostic test.

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

In this study, the control problem of complex robot system with uncertainties and disturbances is addressed. Fuzzy system-fuzzy neural network-backstepping control (FS-FNN-BSC) system is proposed, which can guarantee the accurate, stable and efficient control. First, the general dynamics model of robot is introduced briefly. Then, the design procedure of backstepping control (BSC) technique is presented, to make the best of the advantages of fuzzy system (FS) and fuzzy neural network (FNN) and compromise the accuracy and efficiency, the FS is adopted to approximate the modeling information, and the FNN is utilized to approximate and predict the non-modeling information, and the FS-FNN-BSC system is constructed. Moreover, based on the Lyapunov stability theorem, the stability of the FS-FNN-BSC is proved. To illustrate the correctness, practicality and generality of the proposed control method, the FS-FNN-BSC system is applied to the series robot (KUKA robot) and the parallel robot (Delta robot). And the superiority of the proposed FS-FNN-BSC strategy is highlighted by quantitative comparison with the existing intelligent control methods.

General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots.

Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is mainly performed via coordination between the legs, commonly known as interlimb coordination. Several components underlying the interlimb coordination process (like distributed neural control circuits, local sensory feedback, and body-environment interactions during movement) have been recently identified and applied to the control systems of walking robots. However, while the sensory pathways of biological systems are plastic and can be continuously readjusted (referred to as sensory adaptation), those implemented on robots are typically static. They first need to be manually adjusted or optimized offline to obtain stable locomotion. In this study, we introduce a fast learning mechanism for online sensory adaptation. It can continuously adjust the strength of sensory pathways, thereby introducing flexible plasticity into the connections between sensory feedback and neural control circuits. We combine the sensory adaptation mechanism with distributed neural control circuits to acquire the adaptive and robust interlimb coordination of walking robots. This novel approach is also general and flexible. It can automatically adapt to different walking robots and allow them to perform stable self-organized locomotion as well as quickly deal with damage within a few walking steps. The adaptation of plasticity after damage or injury is considered here as lesion-induced plasticity. We validated our adaptive interlimb coordination approach with continuous online sensory adaptation on simulated 4-, 6-, 8-, and 20-legged robots. This study not only proposes an adaptive neural control system for artificial walking systems but also offers a possibility of invertebrate nervous systems with flexible plasticity for locomotion and adaptation to injury.

Is the healthy respiratory system built just right, overbuilt, or underbuilt to meet the demands imposed by exercise?

In the healthy, untrained young adult, a case is made for a respiratory system (airways, pulmonary vasculature, lung parenchyma, respiratory muscles, and neural ventilatory control system) that is near ideally designed to ensure a highly efficient, homeostatic response to exercise of varying intensities and durations. Our aim was then to consider circumstances in which the intra/extrathoracic airways, pulmonary vasculature, respiratory muscles, and/or blood-gas distribution are underbuilt or inadequately regulated relative to the demands imposed by the cardiovascular system. In these instances, the respiratory system presents a significant limitation to O2 transport and contributes to the occurrence of locomotor muscle fatigue, inhibition of central locomotor output, and exercise performance. Most prominent in these examples of an "underbuilt" respiratory system are highly trained endurance athletes, with additional influences of sex, aging, hypoxic environments, and the highly inbred equine. We summarize by evaluating the relative influences of these respiratory system limitations on exercise performance and their impact on pathophysiology and provide recommendations for future investigation.

Research on H∞ Controller of Cyber Attacks and Eventriggering Mechanism

Aiming at the limited network bandwidth and various cyber attacks, the H∞ neural network controller triggered by discrete events is studied. In order to further improve the utilization of network bandwidth, the event triggering mechanism is improved by taking advantage of the advantages of event triggering in network transmission. At the same time, due to various uncertain and deliberate network attacks, data tampering and packet loss problems are caused. A new neural network control system model is established based on the attack mode obeying Bernoulli probability and dynamic integration. By using Lyapunov function, sufficient conditions are obtained to ensure the asymptotic stability of the closed-loop system, which meets the requirements of H∞. Finally, a numerical example is given to verify the validity of the results.

Sex- and Region-Specific Differences in the Transcriptomes of Rat Microglia from the Brainstem and Cervical Spinal Cord.

The neural control system underlying breathing is sexually dimorphic with males being more vulnerable to dysfunction. Microglia also display sex differences, and their role in the architecture of brainstem respiratory rhythm circuitry and modulation of cervical spinal cord respiratory plasticity is becoming better appreciated. To further understand the molecular underpinnings of these sex differences, we performed RNA sequencing of immunomagnetically isolated microglia from brainstem and cervical spinal cord of adult male and female rats. We used various bioinformatics tools (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Reactome, STRING, MAGICTRICKS) to functionally categorize identified gene sets, as well as to pinpoint common transcriptional gene drivers that may be responsible for the observed transcriptomic differences. We found few sex differences in the microglial transcriptomes derived from the brainstem, but several hundred genes were differentially expressed by sex in cervical spinal microglia. Comparing brainstem and spinal microglia within and between sexes, we found that the major factor guiding transcriptomic differences was central nervous system (CNS) location rather than sex. We further identified key transcriptional drivers that may be responsible for the transcriptomic differences observed between sexes and CNS regions; enhancer of zeste homolog 2 emerged as the predominant driver of the differentially downregulated genes. We suggest that functional gene alterations identified in metabolism, transcription, and intercellular communication underlie critical microglial heterogeneity and sex differences in CNS regions that contribute to respiratory disorders categorized by dysfunction in neural control. These data will also serve as an important resource data base to advance our understanding of innate immune cell contributions to sex differences and the field of respiratory neural control. SIGNIFICANCE STATEMENT: The contributions of central nervous system (CNS) innate immune cells to sexually dimorphic differences in the neural circuitry controlling breathing are poorly understood. We identify key transcriptomic differences, and their transcriptional drivers, in microglia derived from the brainstem and the C3-C6 cervical spinal cord of healthy adult male and female rats. Gene alterations identified in metabolism, gene transcription, and intercellular communication likely underlie critical microglial heterogeneity and sex differences in these key CNS regions that contribute to the neural control of breathing.

Construction of Neural Network Fuzzy Control System of Smart grid based on Genetic Algorithm

The genetic algorithm has a good ability of self-learning, self-adaptation and generalization. At the same time, the choice of the number of grid nodes in the hidden layer affects the effect of smart grid control. In this paper, based on the empirical formula, the number of grid nodes of genetic algorithm is reduced, and the number of grid nodes of optimal genetic algorithm is searched in a small range. According to the characteristics of the global optimization of genetic algorithms, using genetic algorithm to optimize the initial threshold of training can avoid the problem of the genetic algorithm falling into the local minimum. In the neural network fuzzy control of the smart grid, the example analysis shows that this method can accurately and effectively construct the neural network fuzzy control system.

Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial disturbances and attacks significantly restricts their applicability in safety-critical systems including cyber-physical systems (CPS) equipped with neural network components at various stages of sensing and control. This article addresses the reachable set estimation and safety verification problems for dynamical systems embedded with neural network components serving as feedback controllers. The closed-loop system can be abstracted in the form of a continuous-time sampled-data system under the control of a neural network controller. First, a novel reachable set computation method in adaptation to simulations generated out of neural networks is developed. The reachability analysis of a class of feedforward neural networks called multilayer perceptrons (MLPs) with general activation functions is performed in the framework of interval arithmetic. Then, in combination with reachability methods developed for various dynamical system classes modeled by ordinary differential equations, a recursive algorithm is developed for over-approximating the reachable set of the closed-loop system. The safety verification for neural network control systems can be performed by examining the emptiness of the intersection between the over-approximation of reachable sets and unsafe sets. The effectiveness of the proposed approach has been validated with evaluations on a robotic arm model and an adaptive cruise control system.

Stabilisation of event‐triggered‐based neural network control system and its application to wind power generation systems

This study addresses the event-triggered (ET)-based stabilisation problem of neural-network-based control system (NNBCS) and illustrates the direct application to wind power generation system. In this regard, the novel ET-based controller algorithm is designed for NNBCS instead of sampled data controller (sampling will be initiated at a fixed rate regardless whether it is required or not) which reduces the computation complexity by avoiding the unnecessary details over the transmission. The novel stability and stabilisation conditions are expressed in terms of linear matrix inequalities that are derived through constructing the time-dependent Lyapunov functional candidate. For deepening the knowledge in the outcomes of the proposed conditions, the study numerically evaluates the dynamic models such as variable-speed wind turbine drive system, permanent magnet synchronous motors model and traditional inverted pendulum model and validates the effectiveness of the proposed controller scheme. Finally, a comparison result shows the superiority of the derived conditions.

Neural network control system of cooperative robot based on genetic algorithms

Attitude detection of cooperative robots can help robots recognize environment, understand tasks, and improve the safety, accuracy and efficiency of robot work. For robots with unknown configuration parameters, their configuration can be estimated; it is convenient for further kinematics and dynamics analysis. For an economical cooperative robot, its kinematic parameters can be corrected by attitude detection. An independent robot attitude detection system can be used as a secondary auxiliary system, and the fault diagnosis system of the robot is composed of the high-precision encoder system of the robot body. This paper studies the neural network control system of cooperative robot based on genetic algorithm. In this paper, the robot is taken as the research object. Aiming at its strong conjunction, non-linearity and multivariable characteristics, the problem of robot motion control based on neural network is mainly discussed. On this basis, the basic genetic algorithm and an improved genetic algorithm called messy are used to optimize the structure of the neural network, so as to better realize the motion control problem of the robot. The results of this study show that: After optimization with messy genetic algorithm, not only the tracking effect is better, but also the number of hidden layer nodes of the network is reduced from 12 to 7. This greatly simplifies the structure of the network and makes the design and training of the network relatively simple.

The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges

Here, we review the role of control theory in modeling neural control systems through a top-down analysis approach. Specifically, we examine the role of the brain and central nervous system as the controller in the organism, connected to but isolated from the rest of the animal through insulated interfaces. Though biological and engineering control systems operate on similar principles, they differ in several critical features, which makes drawing inspiration from biology for engineering controllers challenging but worthwhile. We also outline a procedure that the control theorist can use to draw inspiration from the biological controller: starting from the intact, behaving animal; designing experiments to deconstruct and model hierarchies of feedback; modifying feedback topologies; perturbing inputs and plant dynamics; using the resultant outputs to perform system identification; and tuning and validating the resultant control-theoretic model using specially engineered robophysical models.

Call production induces motor-driven ZENK response in the song control system of black-capped chickadees

Imitative vocal learning has now been described in a variety of taxa, but is most extensively studied for birdsong learning. However, songbirds produce a variety of songs and calls that also are learned. For example, black-capped chickadees, Poecile atricapillus, produce a variety of highly complex learned vocalizations that they use in different contexts. The role of the neural song control system, including the HVC and the robust nucleus of the arcopallium (RA), in the production of chickadee calls is unclear. In this experiment, birds were housed in different conditions to elicit fee-bee songs and gargle, chick-a-dee and tseet calls. The birds were exposed to different stimuli (i.e. a novel live chickadee, a stuffed saw-whet owl, Aegolius acadicus, a mirror, or long-day photoperiods) and their behavioural and vocal responses were recorded. Brains were then processed to label immediate early gene (ZENK) immunoreactivity. In both HVC and RA, the gargle call elicited the most ZENK immunoreactivity. Although the fee-bee song is well established to depend on imitative vocal learning, its production did not induce the most motor-driven ZENK. These findings suggest that activity in the song control system may be more closely correlated with the acoustic complexity of learned vocalizations than with the extent of imitative learning required during development. These findings, and those of earlier studies, indicate that the neural circuit commonly referred to as the ‘song control system’ is better described as a vocal control system for learned vocalizations.

Anatomical structure responsible for direction changing bilateral gaze-evoked nystagmus in patients with unilateral cerebellar infarction.

The direction-changing bilateral gaze-evoked nystagmus (GEN) (BGEN) is a more specific sign for a gaze-holding deficit than unilateral GEN (UGEN) in a central lesion. We sought to clarify which cerebellar structure is responsible for the generation of BGEN compared with UGEN.We studied 47 cases of UGEN or BGEN associated with isolated unilateral cerebellar infarction in the territories of the cerebellar arteries diagnosed by brain magnetic resonance image (MRI) from June 2007 to April 2014. To identify the structures involved in the generation of BGEN, the overlapped lesions of the BGEN group were subtracted from those of UGEN group and vice versa.About half of the patients (25/47, 53%) showed BGEN and others showed UGEN. There was no difference in the interval from symptom onset to examination between 2 groups (1.3 days vs 2.5 days, P = .24). Thirty-five patients (35/47, 75%) with GEN also showed spontaneous nystagmus. Lesion subtraction analyses revealed that both of the patients with BGEN and UGEN had damage around the vermal pyramid, the uvula and the tonsil, parts of the biventer lobule, and the inferior semilunar lobule.Midline and lower cerebellar structures are related to both BGEN and UGEN in patients with unilateral cerebellar infarction. Regardless of unilateral or bilateral, GEN may represent damage of the gaze-holding neural integrator control system in human.

Research on parallel nonlinear control system of PD and RBF neural network based on U model

ABSTRACTThe modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system.

The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics.

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93mA during operation with a biological recording frequency 50Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

Bio-Inspired Adaptive Locomotion Control System for Online Adaptation of a Walking Robot on Complex Terrains

Developing a controller that enables a walking robot to autonomously adapt its locomotion to navigate unknown complex terrains is difficult, and the methods developed to address this problem typically require robot kinematics with arduous parameter tuning or machine learning techniques that require several trials or repetitions. To overcome this limitation, in this paper, we present continuous, online, and self-adaptive locomotion control inspired by biological control systems, including neural control and hormone systems. The control approach integrates our existing modular neural locomotion control (MNLC) and a newly introduced artificial hormone mechanism (AHM). While the MNLC can generate various gaits through its modulatory input, the AHM, which replicates the endocrine system, adapts to rapid changes in online walking frequency and gait in response to different complex terrains. The control approach is evaluated on an insect-like hexapod robot with 18 degrees of freedom. We provide the results in three sections. First, we demonstrate the adaptability of the robot with the proposed artificial hormones. Second, we compare the performance of two robots with and without artificial hormones while walking on different complex terrains using three performance indexes (stability, harmony, and displacement). Third, we evaluate real-time online adaptations in the real world through real robot walking on different unknown terrains. The experimental results demonstrate that the robot with the proposed artificial hormones does not require several learning trials to adapt its locomotion. Instead, it can continuously adapt its locomotion online, thereby providing greater success and higher performance than other techniques when walking on all terrains.

Research on load simulator control strategy based on BP neural network and PID method

In the research of load simulator control method, PID control is the most widely used control strategy, but PID controller’s three parameters is difficult to set. This paper proposes a BP neural network feedforward PID controller system which uses BP neural network for setting these parameters, and in order to make the network learning speed up the convergence speed and not fall into local minimum, the adaptive vector method is adopted to improve the algorithm. The simulation and experimental results show that this method is good at avoiding the primeval shock and the sine tracking performance of the system has also been improved.

Formation of information signs of unmanned aerial vehicles use efficiency

The subject matter of the article is an unmanned aerial vehicle and methods for determining the effectiveness of its use. The goal of the work is formation of information signs of unmanned aerial vehicles use effectiveness. The following tasks were solved in the article: formation of information signs of the effectiveness of unmanned aerial vehicles using the mathematical apparatus of probability theory and mathematical statistics, the analysis of the survivability of unmanned aerial vehicles by developing a two-level unmanned aerial vehicle control system using a neural network control system. The following methods used are: methods of probability theory and mathematical statistics, neural network management methods. The following results were obtained: The generated informational signs of unmanned aerial vehicles use effectiveness with application of the mathematical apparatus of probability theory and mathematical statistics, which made it possible to obtain an analytical expression for determining the time of information transmission by an unmanned aerial vehicle, being the main criterion for the effectiveness of its use in a given situation. An analysis of the survivability of unmanned aerial vehicles by developing a two-level control system for an unmanned aerial vehicle using a neural network control system that allows you to increase the survivability and thereby the likelihood of completing a task by an unmanned aerial vehicle. It was determined that an essential feature of the UAV functioning processes is their randomness, which is caused by the incomplete certainty of the conditions in which these processes occur, as well as various random deviations and errors that occur during the collection of information, the generation of control signals and their execution. Thus, the result of UAV functioning is randomly and quantitatively characterized by the laws of distribution of parameters expressing this result.

Research on RBF neural network model reference adaptive control system based on nonlinear U – model

ABSTRACTThe overall objective of this study is to design the nonlinear U-model-based radial basis function neural network model reference adaptive control system, through research into a class of complex time-varying nonlinear plants. First, the ideal nonlinear plant is adopted as the reference model and transformed into the U-model representation. In the process, the authors establish the corresponding relationship between the degrees of the reference nonlinear model and the controlled nonlinear plants, and carry out research into the corresponding coefficient relationship between the reference nonlinear model and the controlled nonlinear plants. Also, the impact of the adjusting amplitude and tracking speed of the model on the system control accuracy is analyzed. Then, according to the learning error index of the neural network, the paper designs the adaptive algorithm of the radial basis function neural network, and trains the network by the error variety. With the weight coefficients and network parameters automatically updated and the adaptive controller adjusted, the output of controlled nonlinear plants can track the ideal output completely. The simulation results show that the model reference adaptive control system based on RBF neural network has better control effect than the nonlinear U-model adaptive control system based on the gradient descent method.

A Novel Reference Model-Based Neural Network Approach to Temperature Control System

With the continuous development of deep learning, neural network with its excellent self-learning performance obtains a series of a major breakthrough in target detection, image recognition and so on. In this paper, the temperature control system based on a neural network combined with I-PD compensation is proposed. To improve the neural network self-learning efficiency, the reference model is introduced for providing the teaching signal of neural network. The system simulations are carried out in MATLAB/SIMULINK environment to verify the control efficiency of the proposed reference model based neural network control system. The experiments are carried out on the DSP based temperature control system platform, the results are compared to the conventional I-PD control system to verify the control efficiency.