Neural Control Research Articles

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease.


Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.
Approach. This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy (fNIRS) was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.
Results.
The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions. Significance. The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.&#xD.

Implementation of a hybrid neural network control technique to a cascaded MLI based SAPF

This paper presents a naïve back propagation (NBP) based icos∅\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i\\cos \\emptyset$$\\end{document} technique implemented to a cascaded multilevel inverter (MLI) based shunt active power filter (SAPF). The recommended control algorithm is applied to extract the fundamental component of load current and to decide the compensating current reference for harmonic elimination. The performance of the SAPF using the proposed NBP-based icos∅\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i\\cos \\emptyset$$\\end{document} technique is compared with another two classical control techniques, such as, id-iq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i_{d} - i_{q}$$\\end{document} technique and icos∅\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i\\cos \\emptyset$$\\end{document} technique. The accuracy of the proposed control technique depends on the tuned estimation of active and reactive weights. The performance study of the proposed SAPF with the proposed control technique is investigated under non-linear conditions, with balanced and unbalanced loading conditions. The results reveal that the recommended SAPF is efficient enough to reduce the harmonics from the source current with smooth variation in DC link voltage. The effectiveness of the proposed method is validated by simulation using MATLAB Simulink, and the real-time results are also validated by the experimental setup.

Design of Solar Energy Based System Using Artificial Neural Network Controller

A key component of energy generation and the safe operation of the electrical system is solar energy. The solar energy system for producing the required voltage is presented in this paper. It is managed by an artificial neural network (ANN) controller, which performs significantly better than a conventional PI controller. It aids in lowering the system's total harmonic distortion (THD). The sole PI controller, which produces an erratic output response, is utilized in conventional wind turbines. It is swapped out in the suggested system for an ANN controller, which offers smooth voltage and current response. Two inputs and two outputs are used in the system's development. Six pulse-fired IGBTs that reduce excess voltage and current in the circuit operate this system. The MATLAB/Simulink tool was used to create the results, which were shown to be superior to traditional results. To regulate the impact of a variable power factor, an output variable load is connected. Every outcome has been validated and confirmed to be superior than traditional outcomes.

Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control

Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability. To meet these stringent requirements, traditional control techniques such as proportional-integral-derivative (PID) control need to be improved.Methods: In this study, radial basis function neural network is introduced to improve the traditional proportional integral derivative control technique. The improved proportional integral derivative technique is applied to the modeling and optimization of eight kinds of bar stamping mechanisms.Results: Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516 s and 1.038 s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture.Discussion: The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and optimization of eight-bar stamping mechanism. It not only provides enhanced motion accuracy and stability, but also brings significant practicality to industrial manufacturing. This integration opens up new possibilities for improving the performance of complex mechanical systems to meet the evolving needs of modern manufacturing.

A pilot randomized controlled trial of transcranial direct current stimulation adjunct to moderate-intensity aerobic exercise in hypertensive individuals

BackgroundHypertension is a global issue that is projected to worsen with increasingly obese populations. The central nervous system including the parts of the cortex plays a key role in hemodynamic stability and homeostatic control of blood pressure (BP), making them critical components in understanding and investigating the neural control of BP. This study investigated the effects of anodal transcranial direct current stimulation (tDCS) associated with aerobic physical exercise on BP and heart rate variability in hypertensive patients.MethodsTwenty hypertensive patients were randomized into two groups: active tDCS associated with aerobic exercise or sham tDCS associated with aerobic exercise. BP and heart rate variability were analyzed before (baseline) and after twelve non-consecutive sessions. After each tDCS session (2 mA for 20 min), moderate-intensity aerobic exercise was carried out on a treadmill for 40 min.ResultsA total of 20 patients were enrolled (53.9 ± 10.6 years, 30.1 ± 3.7 Kg/m2). There were no significant interactions between time and groups on diastolic BP during wake, sleep, over 24 and 3 h after the last intervention. Heart rate variability variables showed no significant difference for time, groups and interaction analysis, except for HF (ms2) between groups (p < 0.05).ConclusionAnodal tDCS over the temporal cortex associated with aerobic exercise did not induce improvements in BP and heart rate variability.Clinical trial registrationhttps://ensaiosclinicos.gov.br/rg/RBR-56jg3n/1, identifier: RBR-56jg3n.

Time-varying formation of uncertain nonlinear multi-agent systems via adaptive feedback control approach with event-triggered impulsive estimator

The time-varying formation problem under directed topology is investigated for the uncertain nonlinear multi-agent systems (MASs) via the neural networks-based adaptive feedback control (NNAFC). Compared with the simple adaptive control, the designed control protocol consists of two parts: NNAFC and the event-triggered impulsive estimator. To save communication costs, communications among followers are only carried out at discrete instants determined by the event-triggered impulsive rule. Two kinds of estimators and controllers are designed for two cases with or without communication delay, respectively. Further, NNAFC can be further improved to eliminate errors due to uncertain function approximation for some special cases. Utilizing the Lyapunov stability theory and impulsive control theory, we obtain some sufficient conditions to implement time-varying formation task. Finally, three examples are given to show the feasibility of theoretical analysis.

Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices

The emergence of Internet of Things (IoT) offers numerous functions, such as intelligent sensor integration, remote sensing, and high-speed data transmission, which have found widespread applications in the smart industry and commercial applications. The associative nonlinear effects of a variety of undesirable power quality concerns were resolved by using harmonic mitigation in nonlinear loads and high-performance converters were built on power electronics in conventional systems. Among other performance objectives, total harmonic (THD) distortion analysis and higher order harmonics mitigation is given main concern in smart electronics equipment. This paper proposes an approach to minimize higher-order harmonics due to nonlinear load disturbances in smart IoT devices using the Takagi–Sugeno (TS) Neuro Fuzzy (TS-ANFIS) supervised shunt APF. The proposed work elicits the novel adaptive harmonic mitigation technique in hybrid IoT embedded systems to protect from malfunctioning and to deal with the uncertainty of harmonic signal stimuli in sensitive sensors-based IoT systems. Hysteresis current control is used for the ignition of reference signal under uncertainty of harmonic stimuli. The single phase shunt APF is used to mitigate higher-order harmonics from supply mains, while the implementation of TS-ANFIS supervises the controller action to generate a trigger signal for adequate single phase APF gate excitation. The higher order harmonic current data set is used for error deviation for training neural networks and adaptive control. The estimated adoption of the neuron's empirical weight reduces the total THD to a significant reduction rate of 72.8 % to 0.78 %. The control mechanism is feasible for a range of smart IoT systems to adhere to the standard of 519 (IEEE).

Event‐triggered adaptive estimated inverse neural network control of uncertain nonlinear systems with unknown hysteresis effects

AbstractThis article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller.

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

The paper presents an approach for controlling a line-following robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following Robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following Robot on a running track with different configurations is then evaluated. The results show that the line-following Robot responded more efficiently with an artificial neural network control algorithm than a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the Robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following Robot to adapt to environmental conditions and overcome obstacles on the track more effectively.

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis. Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model. These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

Fixed-time adaptive neural control of electro-hydraulic system with model uncertainties: Theory and experiments

Nonlinearities, uncertainties and unknown external loads decline tracking dynamic performance and steady-state accuracy of electro-hydraulic systems. Most existing electro-hydraulic controllers have well guaranteed both the cylinder position and the load pressure asymptotic stable or uniformly ultimately bounded. However, the corresponding output response is relatively slow and achieves the desired performance in an infinite time, which cannot meet the requirements of fast convergence in some engineering applications. In this study, a fixed-time adaptive neural control (FTANC) is proposed for an electro-hydraulic system to improve the fast convergence performance and steady-state accuracy of the cylinder position in case of hydraulic model uncertainties A rigorous theoretical derivation proves that the electro-hydraulic system obtains practically fixed-time stability, such that all system state errors converge to the zero neighborhood in a fixed time without relation to the initial conditions of electro-hydraulic systems. Finally, both the simulation and experimental results verify the effectiveness of the proposed control method.

Design of ABS sliding mode control system for in-wheel motor vehicle based on road identification

Aiming at the problem that the current Anti-lock Braking System (ABS) control algorithm can not make full use of the ground friction to complete the braking when emergency braking on complex roads, an ABS sliding mode control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor regenerative braking and mechanical friction braking is designed. Based on the neural network control method, the road friction coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip ratio of different roads. The ABS sliding mode controler is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the symbol function in the traditional sliding mode control to weaken the chattering problem, and then the ABS controller is designed. The research results show that compared with the traditional ABS sliding mode controller, the designed ABS neural network sliding mode controller has the following advantages: 1. Through the identification of tire-road friction coefficient by neural network, the real-time performance and road adaptability of ABS controller are improved; 2. When the braking simulation is carried out at a given speed, the braking time is reduced by about 1.03 % and the braking distance is shortened by about 1.01%; 3. The identification of tire-road friction coefficient is realized, which weakens the chattering problem of traditional sliding mode control and improves the stability and robustness of ABS controller.

Integrated Modular Neural Control for Versatile Locomotion and Object Transportation of a Dung Beetle-Like Robot.

Dung beetles can effectively transport dung pallets of various sizes in any direction across uneven terrain. While this impressive ability can inspire new locomotion and object transportation solutions in multilegged (insect-like) robots, to date, most existing robots use their legs primarily to perform locomotion. Only a few robots can use their legs to achieve both locomotion and object transportation, although they are limited to specific object types/sizes (10%-65% of leg length) on flat terrain. Accordingly, we proposed a novel integrated neural control approach that, like dung beetles, pushes state-of-the-art insect-like robots beyond their current limits toward versatile locomotion and object transportation with different object types/sizes and terrains (flat and uneven). The control method is synthesized based on modular neural mechanisms, integrating central pattern generator (CPG)-based control, adaptive local leg control, descending modulation control, and object manipulation control. We also introduced an object transportation strategy combining walking and periodic hind leg lifting for soft object transportation. We validated our method on a dung beetle-like robot. Our results show that the robot can perform versatile locomotion and use its legs to transport hard and soft objects of various sizes (60%-70% of leg length) and weights (approximately 3%-115% of robot weight) on flat and uneven terrains. The study also suggests possible neural control mechanisms underlying the dung beetle Scarabaeus galenus' versatile locomotion and small dung pallet transportation.

Event-Triggered-Based Adaptive NN Cooperative Control of Six-Rotor UAVs With Finite-Time Prescribed Performance

This paper studies the disturbance-observer-based adaptive neural network cooperative event-triggered control problem for six-rotor unmanned aerial vehicles with finite-time prescribed performance. Six-rotor unmanned aerial vehicle systems are divided into the position subsystem and the attitude subsystem. The switching threshold event-triggered mechanism that considers the influence of event triggering on the consensus control performance is designed of each six-rotor unmanned aerial vehicle to save the transmission resources, which excludes the Zeno behavior. Neural networks are introduced for estimating the uncertainties and solving the algebraic loop problem. In addition, for the position subsystem, by combining the prescribed performance with the velocity function, a finite-time control scheme is proposed, which can guarantee that the consensus errors converge to a prespecified neighborhood of the origin, and all closed-loop signals are bounded. Based on the designed control input signal of position subsystem, an adaptive neural network event-triggered control mechanism is designed to stabilize the attitude subsystem. Finally, some verification results are given to test the rationality of the proposed control strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The purpose of this paper is to design an event-triggered cooperative control scheme to save the transmission resources for six-rotor unmanned aerial vehicle systems with performance limitations and lumped disturbances. In practical applications, if the impact of the event-triggered mechanism on the system performance is not taken into account, the system performance may be significantly degraded while the information transmission resource is saved. Thus, this paper designs an event-triggered mechanism that considers the system performance, which can effectively weigh the relationship between system performance and transmission resource consumption and better meet the practical requirements. Moreover, a finite-time specified performance control strategy is introduced to avoid the problem of difficult determination of residual set and improve the transient-state and steady-state performances of unmanned aerial vehicle systems, which is more in line with the actual demand.

Neural network predictive control for fault detection and identification in DFIG with SMES for low voltage ride-through requirements

The increasing reliance on renewable energy sources brings to light the operational challenges of doubly fed induction generators (DFIGs), particularly under grid disturbances. This study introduces an innovative approach employing Neural Network Predictive Control (NNPC) for fault detection and identification (FDI) in DFIG systems, integrated with Superconducting Magnetic Energy Storage (SMES) to meet Low Voltage Ride-Through (LVRT) requirements. Our method uniquely combines NNPC for dynamic wind speed prediction and precise control of rotor-side and grid-side converters with the stability enhancement offered by SMES. Simulation results demonstrate a notable improvement in DFIG performance under variable wind conditions and grid failures. The system-maintained voltage stability at the Point of Common Coupling (PCC) with less than 5% deviation during transients and ensured consistent power output, marking a significant advancement in LVRT compliance. However, the model assumes steady-state grid conditions and does not account for extreme weather scenarios, indicating areas for future research. This study's findings contribute valuable insights into the robust operation of DFIGs within modern renewable energy grids.

Observer-Based Neural Control of N-Link Flexible-Joint Robots.

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Adaptive Online Neural Predictive Control of Hydraulic Actuator Using FPGA Acceleration

Hydraulic actuators have recently been widely used in control systems as efficient output devices. However, high-precision control of hydraulic actuators in industrial applications remains challenging due to the conflict between controllers' complexity, especially in theoretical modeling, and their generalization abilities, including adaptability to parametric uncertainties and versatility for various working conditions. To address this issue, we propose an adaptive online neural predictive control (AONPC) scheme, which is capable of handling unmodeled nonlinearities and uncertainties with a simple structure and low demand for system modeling. Specifically, an artificial neural network (ANN) is responsible for data-driven system identification and predictive control, while upper confidence bound (UCB) algorithm is introduced for hyperparameter self-adaptation. The ANN structure and the iteration number for solving the optimization problem are determined through a simulation study. Then, to complete the whole control loop within a short and fixed tracking interval, the proposed controller is implemented on a field programmable gate array (FPGA), which can effectively improve the tracking precision. Finally, extensive experimental results on a realistic hydraulic actuator device demonstrate the superiority of the proposed controller.

Neuro-Fuzzy-SVPWM Switched-Inductor-Capacitor-based Boost Inverter for Grid-Tied Fuel Cell Power Generators, Design and Implementation

Hydrogen energy shows promise as a renewable energy source for various applications like battery and electric vehicle charging stations, as well as grid connections. However, high current ripple from fuel cells (FCs) and inadequate voltages for grid use pose challenges. This study presents a novel solution using neural fuzzy network control in a high-gain DC-DC boost converter to address these issues. The suggested converter charges in parallel and discharges in series, minimizing the current ripple range in the fuel cell network. Additionally, the switch-capacitor cell efficiently increases the output voltage.In this study, a Neuro-fuzzy system with 9 rules is trained meticulously over 50 epochs using hybrid optimization and grid partition methods, achieving a low training error of 0.045 with 522,064 samples. The neural fuzzy network, employing the weighted average method for Defuzzification, produces duty cycle values from 0.02 to 0.5 in response to input signals. Additionally, an innovative Space Vector Pulse Width Modulation (SVPWM) approach within the inverter circuit enhances voltage generation precision and power quality for grid delivery, notably reducing current ripple and ensuring stable power supply. This combined with the neural fuzzy network in the converter efficiently converts hydrogen energy into AC voltage for seamless grid integration, revolutionizing boost converter efficiency and advancing hydrogen energy utilization across various energy sectors.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

A Continuous Neural Network Adaptive Controller for Consensus of Uncertain Multi-Agent Systems

A Continuous Neural Network Adaptive Controller for Consensus of Uncertain Multi-Agent Systems