Neural Control Research Articles

Adaptive Neural Asymptotic Tracking Control for PMSM Systems Under Current Constraints and Unknown Dynamics

Adaptive Neural Asymptotic Tracking Control for PMSM Systems Under Current Constraints and Unknown Dynamics

Adaptive Neural Network-Quantized Tracking Control of Uncertain Unmanned Surface Vehicles With Output Constraints

Adaptive Neural Network-Quantized Tracking Control of Uncertain Unmanned Surface Vehicles With Output Constraints

Multiple Noncooperative Targets Encirclement by Relative Distance-Based Positioning and Neural Antisynchronization Control

From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/B8uTorBNrl4</uri>

A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

Adaptive solution to transfer learning of neural network controllers from earth to space environments

AbstractCompliant manipulation has long been a major constraint for grappling in robotic manipulators. To adopt robotic manipulators in space for the prospect of capturing space junk and transforming them into salvageable assets for re‐use, robust adaptive manipulation would be key. We believe that a bio‐inspired approach could provide human‐like tactility required for robustness and adaptability in robotic manipulation. Given the similarity in form and dynamics between earth‐based and space‐based robotic manipulators, we first explored the transfer learning of neural network controllers as an avenue to address the challenges of limited computation resources onboard the spacecraft (space manipulator). We introduced a pre‐trained and learned feedforward neural network for modelling the control error a priori. While the results were encouraging, there are major limitations of neural networks' capability to ensuring the transfer learning of similar earth‐based dynamics to space‐based dynamics, given that the parameters of contrast are fairly straightforward. We have demonstrated these limitations by presenting a novel approach that is inspired by human motor control. We explored the adaptability through a practical problem of transferring a neuro‐controller from earth to space. With the results not as plausible as expected, an alternative adaptive controller has been learned to demonstrate a viable solution. The controller was trained entirely in simulation via rapid online adaptation of the robot's controller to the object's properties and environmental dynamics using only proprioception history. As a notable step, we have shown that appropriate models can be learned in this manner by training the control policy via reinforcement learning, which provides avenue for transferring the learned model from earth to space environments.

Design of a novel control hysteresis algorithm for photovoltaic systems for harmonic compensation

Solar photovoltaic (PV) system design and integration with an existing AC grid is growing very fast in recent years and used by many of them as they are pollution-free, structure is limited and maintenance free. From the factors considering, the performance of PV system depends upon the inverter output voltage tested for linear, non-linear, with harmonic, and without harmonic loads. Generated due to the nonlinear loads. Better inverter control techniques are developed to maintain grid power quality. This article discusses the analysis and comparison of pulse width modulation (PWM) converters with unique and state of the art nonlinear control schemes and various modulation approaches. The primary objective of this research is controlling an active filtering hysteresis PWM converter with no sensors. A simple structure with hysteresis current control method total harmonic distortion (THD) is lower when compared with the sinusoidal pulse width modulation (SPWM) method. The said claims are supported by employing computer simulations using MATLAB/Simulink and different control approaches, such as proportional plus integral and artificial neural network controllers.

Singularity Avoidance Fixed-Time Adaptive Neural Control for Autonomous Underwater Vehicles Considering Unmodelled Dynamics and Disturbances

Singularity Avoidance Fixed-Time Adaptive Neural Control for Autonomous Underwater Vehicles Considering Unmodelled Dynamics and Disturbances

Human arm redundancy: a new approach for the inverse kinematics problem.

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Neural control of fluid homeostasis is engaged below 10°C in hibernation.

Thirteen-lined ground squirrels (Ictidomys tridecemlineatus) hibernate for several months each winter without access to water,1 but the mechanisms that maintain fluid homeostasis during hibernation are poorly understood. In torpor, when body temperature (TB) reaches 4°C, squirrels decrease metabolism, slow heart rate, and reduce plasma levels of the antidiuretic hormones arginine vasopressin (AVP) and oxytocin (OXT).1 Squirrels spontaneously undergo interbout arousal (IBA) every 2weeks, temporarily recovering an active-like metabolism and a TB of 37°C for up to 48 h.1,2 Despite the low levels of AVP and OXT during torpor, profound increases in blood pressure and heart rate during the torpor-IBA transition are not associated with massive fluid loss, suggesting the existence of a mechanism that protects against diuresis at a low TB. Here, we demonstrate that the antidiuretic hormone release pathway is activated by hypothalamic supraoptic nucleus (SON) neurons early in the torpor-arousal transition. SON neuron activity, dense-core vesicle release from the posterior pituitary, and plasma hormone levels all begin to increase before TB reaches 10°C. Invivo fiber photometry of SON neurons from hibernating squirrels, together with RNA sequencing and c-FOS immunohistochemistry, confirms that SON is electrically, transcriptionally, and translationally active to monitor blood osmolality throughout the dynamic torpor-arousal transition. Our work emphasizes the importance of the antidiuretic pathway during the torpor-arousal transition and reveals that the neurophysiological mechanism that coordinates the hormonal response to retain fluid is active at an extremely low TB, which is prohibitive for these processes in non-hibernators.

Research on space attitude recognition and control method for soft robotics based on neural networks

In the modern field of biomedical robotics, there is increasing attention on the use of soft robots as carriers for surgical devices. Compared to rigid robots, soft robots exhibit more complex postures and work environments, requiring new methods for recognizing and controlling their postures within biological organisms. This paper investigates the recognition and control of the posture of soft robots using a neural network learning approach, utilizing a soft robot equipped with resistive sensors. By establishing the relationship between changes in resistance values and posture variations, the study successfully achieves the identification and control of the soft robot's posture. The obtained posture data based on resistance values are validated for reliability. Thus, by combining resistive sensors with soft robots and employing neural network analysis, the recognition and control of soft robots postures within biological organisms can be achieved.

Online Corrections to Neural Policy Guidance for Pinpoint Powered Descent

This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables. The proposed approach is to linearize the dynamics around the baseline values of its arguments and then to solve for the corrective input required to transfer the perturbed trajectory to precisely known or desired values at specific time points, in other words, the interim points. Depending on the type of decision variables to adjust, parameter correction and control function correction methods are developed. These incremental correction methods can be used as a means to compensate for the prediction errors of pretrained neural networks in real-time applications where high accuracy of the prediction of dynamical systems at prescribed time points is imperative. In this regard, the online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control subject to point constraints using a neural policy. A numerical example demonstrates the effectiveness of the proposed approach in an application to a powered descent problem on Mars.

Output-Feedback Consensus Maneuvering of Uncertain MIMO Strict-Feedback Multiagent Systems Based on a High-Order Neural Observer.

In this article, a distributed output-feedback consensus maneuvering problem is investigated for a class of uncertain multiagent systems with multi-input and multi-output (MIMO) strict-feedback dynamics. The followers are subject to immeasurable states and external disturbances. A distributed neural observer-based adaptive control method is designed for consensus maneuvering of uncertain MIMO multiagent systems. The method is based on a modular structure, resulting in the separation of three modules: 1) a variable update law for the parameterized path; 2) a high-order neural observer; and 3) an output-feedback consensus maneuvering control law. The proposed distributed neural observer-based adaptive control method ensures that all followers agree on a common motion guided by a desired parameterized path, and the proposed method evades adopting the adaptive backstepping or dynamic surface control design by reformulating the dynamics of agents, thereby reducing the complexity of the control structure. Combined with the cascade system analysis and interconnection system analysis, the input-to-state stability of the consensus maneuvering closed loop is established in the Lyapunov sense. A simulation example is presented to demonstrate the performance of the proposed distributed neural observer-based adaptive control method for output-feedback consensus maneuvering.

Decentralized event‐triggered output feedback adaptive neural network control for a class of MIMO uncertain strict‐feedback nonlinear systems with input saturation

SummaryFor a class of multiple‐input multiple‐output large‐scale nonlinear systems in strict‐feedback form with input saturation, external disturbances and immeasurable states, an adaptive decentralized neural network (NN) control strategy on the basis of event triggered mechanism is investigated in this article. In contrast to the literature, the proposed method is centered on the control‐error as a replacement to the tracking‐error that leads to a simplified derivation approach of adaptive laws. Furthermore, the control gains for this class of systems are considered unknown nonlinear functions and not assumed as simple unity or known gains as always done in the literature. Moreover, the challenge of losing controllability that typically arises in state transformation‐based methods, as reported in the literature, is entirely resolved in our approach. Last and not least, all restrictions imposed on unmatched interconnections are eliminated along with avoiding the complexity explosion caused by recursive back‐stepping designs. For this end, the unknown ideal control laws are approximated using NNs, while additional control terms are added to handle saturation effects, unknown interactions, and approximation errors. Additionally, fuzzy inference systems are employed to estimate unknown control errors. Due to the strictly positive real property, the tracking errors are proved to belong to a small compact set using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach.

Neuromuscular organization of the benthic ctenophore, Vallicula multiformis

Ctenophora is the earliest metazoan taxon with neurons and muscles. Recent studies have described genetic, physiological, and cellular characteristics of the neural and muscular systems of this phylogenically important lineage. However, despite the ecological diversity of ctenophore niches, including both pelagic and benthic forms, studies have focused predominantly on pelagic species. In the present study, we describe the neural and muscular architectures of the benthic ctenophore, Vallicula multiformis (Order Platyctenida), employing immunohistochemical analysis using antibodies against amidated neuropeptides with the C-terminal sequences VWYa, NPWa, FGLa, or WTGa to compare it to pelagic species. In V. multiformis, which lacks the characteristic comb rows seen in pelagic ctenophores, neural structures that develop beneath the comb were not detected, whereas the subepithelial and tentacle neural networks showed considerable similarity to those of pelagic species. Despite significant differences in morphology and lifestyle, muscle organization in V. multiformis closely resembles that of pelagic species. Detailed analysis of neurons that express these peptides unveiled a neural architecture composed of various neural subtypes. This included widely distributed subepithelial neural networks (SNNs) and neurosecretory cells located primarily in the peripheral region. The consistent distribution patterns of the VWYa-positive SNN and tentacle nerves between V. multiformis and the pelagic species, Bolinopsis mikado, suggest evolutionarily conserved function of these neurons in the Ctenophora. In contrast, NPWa-positive neurons, which extend neurites connecting the apical organ and comb rows in B. mikado, showed a neurite-less neurosecretory cell morphology in this flattened, sessile species. Evaluation of characteristics and variations in neural and muscular architectures shared by benthic and pelagic ctenophore species may yield valuable insights for unraveling the biology of this rapidly evolving yet enigmatic metazoan lineage. These findings also provide important insight into neural control modalities in early metazoan evolution.

Artificial neural network controller for grid current quality improvement in solid-state transformers

Artificial neural network controller for grid current quality improvement in solid-state transformers

Visual guidance fine-tunes probing movements of an insect appendage

Visually guided reaching, a regular feature of human life, comprises an intricate neural control task. It includes identifying the target's position in 3D space, passing the representation to the motor system that controls the respective appendages, and adjusting ongoing movements using visual and proprioceptive feedback. Given the complexity of the neural control task, invertebrates, with their numerically constrained central nervous systems, are often considered incapable of this level of visuomotor guidance. Here, we provide mechanistic insights into visual appendage guidance in insects by studying the probing movements of the hummingbird hawkmoth's proboscis as they search for a flower's nectary. We show that visually guided proboscis movements fine-tune the coarse control provided by body movements in flight. By impairing the animals' view of their proboscis, we demonstrate that continuous visual feedback is required and actively sought out to guide this appendage. In doing so, we establish an insect model for the study of neural strategies underlying eye-appendage control in a simple nervous system.

Adaptive self-structuring neural network control for full-state constrained hydraulic systems with disturbance compensation

An adaptive self-structuring neural network for full-state constrained systems with a prescribed performance control scheme is proposed for hydraulic systems to achieve asymptotic tracking under parametric uncertainties and time-varying disturbances. To overcome external disturbances in hydraulic systems, a self-structuring neural network is employed to approximate mismatched disturbances, reducing the computational burden. Additionally, an output feedback filtering control technique is introduced to address the challenges of uncertain parameter sets, which deteriorates the control performance when affected by oscillations triggered by high-frequency noise. The prescribed performance control achieves the desired transient and steady-state performance of the system by constructing a sequence of error transition variables that guarantee that all the state errors of the hydraulic systems are within the appointed-time reachable performance function boundary range. For the proposed control scheme, the stability of the closed-loop system can be demonstrated via Lyapunov theory, and all signals are guaranteed to be bounded. Ultimately, the tracking performance of the proposed controller is verified through abundant comparative experiments under the influence of parameter uncertainty and time-varying disturbances.

Adaptive neural inverse optimal control with predetermined tracking accuracy for nonlinear MIMO systems

Adaptive neural inverse optimal control with predetermined tracking accuracy for nonlinear MIMO systems

Revisiting the Role of Serotonin in Sleep-Disordered Breathing.

Serotonin or 5-hydroxytryptamine (5-HT) is a ubiquitous neuro-modulator-transmitter that acts in the central nervous system, playing a major role in the control of breathing and other physiological functions. The midbrain, pons, and medulla regions contain several serotonergic nuclei with distinct physiological roles, including regulating the hypercapnic ventilatory response, upper airway patency, and sleep-wake states. Obesity is a major risk factor in the development of sleep-disordered breathing (SDB), such as obstructive sleep apnea (OSA), recurrent closure of the upper airway during sleep, and obesity hypoventilation syndrome (OHS), a condition characterized by daytime hypercapnia and hypoventilation during sleep. Approximately 936 million adults have OSA, and 32 million have OHS worldwide. 5-HT acts on 5-HT receptor subtypes that modulate neural control of breathing and upper airway patency. This article reviews the role of 5-HT in SDB and the current advances in 5-HT-targeted treatments for SDB.

Optimizing power consumption and position control in an electro-hydraulic system with cylinder bypass and NN-MPC

This study introduces an innovative approach to enhance the energy efficiency and position control performance of electro-hydraulic systems, employing a comprehensive comparative analysis. It presents and evaluates three control techniques: Proportional-Integral-Derivative (PID) control, Model Predictive Control (MPC), and Neural Network Model Predictive Control (NN-MPC). These methods are systematically assessed across varying load conditions. Notably, our research unequivocally establishes the exceptional performance of the NN-MPC approach, even when confronted with load variations. Furthermore, the study conducts an exhaustive examination of energy consumption by comparing a conventional system, where a flow control valve is not utilized as a hydraulic cylinder bypass, with a proposed system that employs a fully open Flow Control Valve (FCV). The results underscore the remarkable energy savings achieved, reaching up to 9% at high load levels.