With the technological revolution, the Internet of medical things (IoMT) has developed to be of immense benefit. In IoMT, medical images and patients’ data are widely transmitted through private/public network. An ideal transmission should not jeopardize the security, confidentiality, authenticity, authorization, or integrity of medical data/images. To ensure effective transmission and address the aforementioned issues, this paper proposes a blind region based medical image watermarking approach where a medical image is partitioned into region of interest (ROI) and region of non-interest (RONI). To ensure ROI intergrity, localized tamper detection and recovery bits (LTDRB) are generated. For precise diagnosis, patient’s electronic health record (EHR) and LTDRB are embedded in RoNI using hybrid DWT-SVD. No embedding is done in RoI to maintain its integrity and high visual quality. To ensure the security and confidentiality of EHR, a novel encryption scheme using Magic Square technique with low computational cost is proposed. Experimental results demonstrates that the proposed scheme provides high imperceptibility (Avg. PSNR>55 dB, SSIM ≈ 1 and BER ≈0), robustness, security at low computational cost and high accuracy in tamper detection and recovery. A comparative study with some of the latest related research shows that the proposed scheme provides imperceptibility and robustness at par. However, the proposed scheme shows superior performance by providing higher EHR security at low computational cost and higher accuracy in ROI tamper detection and recovery, which other schemes have overlooked.

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter-driven DC motor system. The proposed technique integrates an adaptive polynomial-neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network-based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady-state performance. The stability of the entire closed-loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start-up, step-reference tracking, and external step-load torque disturbances. The real-time experimentation is conducted on a laboratory prototype of power converter-driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to (Formula presented.) and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

This article presents a novel control architecture for an enhanced closed-loop speed tracking of a DC–DC buck power converter fed Permanent Magnet DC motor (PMDC) motor in face of large exogenous load torque uncertainty. The proposed architecture combines a new self learning Zernike radial polynomial neural network (ZRNN) estimator with the backstepping controller. The design involves a computationally simple online learning based ZRNN to rapidly and accurately estimate the unknown large load torque uncertainties. The proposed control solution concurrently guarantees stability and excellent dynamic performance through an effective neural network based estimation and subsequent compensation of unanticipated load torque perturbations over a wide range. The closed loop stability of the DC–DC buck power converter driven PMDC motor and asymptotic speed tracking with the proposed neuro-adaptive controller is proved using the stability theory for non-autonomous systems. The effectiveness of the proposed controller has been investigated through experimentation on an indigenously developed laboratory prototype of 200 W under closed loop operation using digital signal processors. The tests conducted around different operating conditions include the motor start-up response, step variations in the load torque, and step changes in the reference speed. Experimental results demonstrate a significant improvement in the speed tracking performance achieving 48.13% reduction in the settling time and no-change in speed during start-up and load torque perturbations upto 600%, respectively. Experimental validations and extensive tests spanning over a large operating region, substantiate the theoretical claims and real-time suitability of the proposed controller for sensitive applications demanding high performance.

Concerning the power quality issues in the power distribution network due to load uncertainties and improper impedance matching of the inductances, deep reinforced learning (DRL)-based inductively coupled DSTATCOM (IC-DSTATCOM) is proposed. First, by analyzing the impedance matching principle, the expression of source, load and filter current is derived with the help of inductive filtering transformer. And second, an individual DRL subnet structure is accumulated for each phase using mathematical equations to perform the better dynamic response. A 10-kVA, 230-V, 50-Hz prototype direct coupled distributed static compensator (DC-DSTATCOM) and IC-DSTATCOM experimental setup is buit to verify the experimental performance under uncertainties of loading. The IC-DSTATCOM is augmented better dynamic performance in terms of harmonics curtailment, improvement in power factor, load balancing, potential regulation, etc. The benchmark IEEE-519-2017, IEC-61727 and IEC-61000-1 grid code are used to evaluate the effectiveness of the simulation and experimental study.

The Distributed Static Compensator (DSTATCOM) is being recognized as a shunt compensator in the power distribution networks (PDN). In this research study, the superconducting magnetic energy storage (SMES) is deployed with DSTATCOM to augment the assortment compensation capability with reduced DC link voltage. The proposed SMES is characterized by a DC-DC converter with different circuit elements like one inductor, two diodes and two insulated gate bipolar transistors. The Deep Bayesian Active Learning algorithm is suggested to operate SMES supported DSTATCOM for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor, load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the DSTATCOM. To show the power quality effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC- 61000-1 grid code.

This work presents a full-bridge-based driver circuit for powering two light-emitting diode (LED) lamps of different wattages. In the proposed topology, the LED lamp strings are connected in anti-series across the full bridge, which significantly reduces voltage stress on the switches. Zero-voltage switching (ZVS) is achieved through an LC resonant circuit, minimizing switching losses and improving efficiency. The current through the LC series circuit is inherently determined by the voltage difference between the two LED lamp strings. The system is powered by two equal-magnitude DC sources with series inductors, making it suitable for future DC-grid lighting applications. Additional advantages include low output ripple, reduced component count for cost-effectiveness, and high efficiency through soft-switching operation. A detailed theoretical analysis of the converters operating modes is provided to explain its performance and behavior. The concept is experimentally validated using a 73.4 W laboratory prototype, confirming the effectiveness and practical feasibility of the proposed solution in real-world LED driver applications.

This paper presents a novel droop control strategy for an islanded DC microgrid that integrates renewable energy resources, including photovoltaic (PV) panels, a wind turbine, and fuel cells as generation sources, along with a battery storage system (BSS) and a current-loop-controlled electric vehicle (EV) as flexible storage units. The EV contributes flexibility by charging during low-demand periods and discharging during peak or overload conditions. Unlike conventional proportional–integral (PI) control, which offers accurate voltage regulation but limited dynamic performance, and classical droop methods, which suffer from a trade-off between current sharing accuracy and voltage deviation, the proposed strategy uses adaptive droop coefficients to achieve a balance between bus voltage stability and equitable power sharing without requiring large droop gains. The main contributions are as follows: (i) formulation of an improved droop framework that mitigates the classical trade-off between voltage deviation and sharing accuracy; (ii) coordinated integration of RES, BSS, and EV into a unified control system, enabling dynamic charging and discharging of the EV in response to load fluctuations; and (iii) comparative evaluation with conventional PI control to highlight the practical advantages of the proposed method. Two case studies are investigated: (a) renewable variability and load disturbances, including irradiance fluctuations, wind speed changes, sudden load variations, and load shedding, and (b) different load levels ranging from underload to overload conditions. Simulation studies, carried out in MATLAB/Simulink with a Nissan Leaf battery EV model, confirm that the proposed droop control strategy maintains DC bus voltage stability, reduces current stress in overload conditions, and achieves balanced power distribution more effectively than the conventional PI approach. The results demonstrate that the strategy enhances reliability, dynamic performance, and operational sustainability, establishing it as a robust and scalable solution for future islanded DC microgrids.

The rapid adoption of electric vehicles (EVs) offers ecological and economic benefits but also introduces challenges to power distribution networks, including increased energy losses, voltage fluctuations, reduced reliability, and higher peak demand. Uncoordinated deployment of charging stations (EVCSs) may further deteriorate grid performance. While existing studies have examined EVCS siting or renewable energy integration separately, few provide a holistic framework that simultaneously considers EVCS planning, renewable generation, storage-based energy management, and user behavior under uncertainty. The objective of this study is to develop an integrated planning model that determines the optimal locations and sizes of EVCSs, aiming to minimize energy losses, investment costs, and driver travel costs, while reducing peak demand and maximizing renewable energy utilization. To achieve this, a hybrid Gray Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm is applied for multi-objective optimization, chosen for its effective balance of global exploration and local exploitation. Photovoltaic (PV) systems are incorporated at selected distribution nodes, and energy management strategies (EMSs) are designed to coordinate energy storage system (ESS) operations. Uncertainties in PV generation and EV charging demand are addressed using Monte Carlo Simulation (MCS). The methodology is validated on the IEEE 33-bus distribution system under a 24-hour simulation. Results show that integrating EMS with optimally located EVCSs reduces average energy losses by up to 15 % and lowers peak power demand by 20 %. These findings demonstrate that the proposed approach provides a robust, cost-effective, and sustainable pathway for EVCS infrastructure planning.

This work investigates the application of Artificial Bee Colony (ABC) optimization for the design of Type compensators utilizing the dual-loop control scheme. The proposed Type compensators integrate the ABC optimization for regulating the closed-loop operation of a DC-DC buck converter. Such an integration of ABC optimization, aids in effectively regulating the output voltage and inductor current, besides ensuring enhanced time domain criteria. The proposed dual-loop control scheme consists of a current loop and a voltage loop. The current loop regulates the inductor current and the voltage loop regulates the output voltage. The efficacy of the proposed method is revealed through extensive simulation and experimental investigation under start-up response, step perturbations in external load. The experimentation is conducted on a laboratory prototype using dspace DS1104 control board.

Since the capacitor current can reflect load variations faster than the peak inductor current, incorporating the capacitor current into control loops helps to improve the transient response of the dc-dc converter. In this paper, a coupled inductor single input dual output (CI-SIDO) buck converter is investigated under capacitor current ripple (CCR) control. A precise small-signal model for a CCR-controlled CI-SIDO buck converter operating in continuous conduction mode (CCM) is developed. The accurate small-signal model is obtained by substituting the derived CCR controller expressions in the CI-SIDO buck converter state-space model. The CCR controller equations specify the duty ratios as functions of the circuit variables namely the capacitor currents and the output voltages. It is observed that the CI-SIDO buck converter with the CCR controller exhibits instability when both or either of the duty ratios are greater than 0.7 or their sum is greater than 1. The results of the PLECS STANDALONE simulation validate the theoretical propositions as regards the switching instability of the controlled converter.

As the capacitor current can respond to load fluctuations more rapidly than the peak inductor current, incorporating the capacitor current into the current control loop enhances the transient response in DC-DC converters as well as ensures over-current protection and noise immunity. This paper presents a comprehensive small-signal model (SSM) for a capacitor current ripple controlled (CCR) coupled inductor single-input dual-output (CI-SIDO) buck converter. The complete SSM is derived by unifying the developed SSM of the CCR controller with the SSM of the considered power converter using state-space averaging technique. In CCR control, the amalgamation of the comparator and SR flip-flop is accountable for producing the duty cycle. The proposed SSM is of immense usefulness in designing the outer loop controller, deriving switching instability conditions, and analyzing the dynamic characteristics of the capacitor current controlled CI-SIDO buck converter. To evaluate the advantages of this current controller to CI-SIDO buck converter, a load regulation analysis using the SSM of CCR controller is provided and thereafter verified through simulations in MATLAB/Simulink. It is observed that the low frequency gain of the cross and self regulation transfer functions is substantially less signifying promising dynamic and load disturbance rejection capability of the CCR control driven CI-SIDO buck converter.

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer.

A novel zero-voltage switching full-bridge (NZVSFB) converter designed for multiple load LED driver applications is introduced in this paper. Four LED lamps are involved in this configuration, with Lamp-2, Lamp-3, and Lamp-4 being powered by a full bridge converter, and Lamp-l being directly connected in series with the battery source. The efficiency of the system is increased since the power provided to lamp-l comes directly from a battery source, eliminating the need for any power processing stage. The major claims of the proposed NZVSFB converter are low component count/lamp, enhanced efficiency, zero voltage switching (ZVS) of all the switching devices, ripple free current and equal current sharing. The interleaved technique utilized in inductor design aims to mitigate the adverse effects of ripple currents on LED performance and circuit reliability by reducing their magnitude and ensuring more stable operation. The steady state operation of the proposed NZVSFB converter is discussed in detail and the effectiveness of the circuit is verified in MATLAB Simulink environment.

In contemporary energy production, there's been a significant transition from coal-centric methods to renewable energy sources (RES) that emit zero pollutants. As RES becomes more integral to expansive power systems, there's a growing need for regulated power electronic systems. When integrated with microgrids, RES often face stability challenges, being represented in DC microgrids as a constant power load (CPL). The DC-DC converters designed to operate these CPL loads are affected by switching irregularities and the destabilizing effects of CPL, leading to broader power system instability. This study introduces a backstepping control (BSC) approach for a DC-DC buck converter operating with CPL. Through extensive experimental investigations, the effectiveness of the proposed controller under various test conditions, contrasting its results with the cascade PI controller have been evaluated. The outcomes reveal that the proposed backstepping control technique enhances both the dynamic and steady-state performance of the DC-DC buck converter-CPL system, especially during extensive fluctuations in the load power.

The introduction of alternative vehicle technologies, such as Electrical Vehicles (EVs) is a practical endeavour to minimize CO2 and NOX emissions. Therefore, EVs raise concerns about vehicle charging and management. The deployment of charging stations (CS) for EVs is explored in this study due to the importance of charging station infrastructure. Furthermore, its objective is to examine the economic and technical aspects of the placement of fast charging stations, the investment cost necessary to install the charging station is considered under the economic aspect, whereas distribution system energy loss and voltage variation at buses are considered under the technical aspect to construct CS at optimal locations. First, a mathematical formulation of the problem is developed to deploy the CS in the distribution system. A novel AI-based hybrid technique of gray wolf optimization and particle swarm optimization (HGWOPSO) is used to determine the optimal location of CSs. The developed method is tested by simulation on a 33-IEEE bus. Furthermore, after integrating renewable energy sources at the CSs, 10.55% energy loss is reduced and also improved voltage profile of the proposed system.

Turbine blades, which carry approximately one-third of a turbine weight, are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. Early detection of blade damage is crucial for preventing catastrophic failures that can lead to downtime, repair costs, and even injury or loss of life. This manuscript aims to explore an image analytics-based deep learning framework for wind turbine blade erosion detection. Turbine blade images are captured via drones/unmanned aerial vehicles during the data collection phase. Upon inspection, it was found that the image dataset was limited; hence, image augmentation was applied to improve the blade image dataset. The approach is modeled as a multiclass supervised learning problem where different turbine blade surface damage scenarios are considered. The potential capability of transfer learning methods such as VGG16-RCNN and AlexNet are tested against a convolutional neural network for detecting the blade's surface damage. Results reveal the VGG16-RCNN model as the best classifier among the tested ones with the highest accuracy and precision score. To validate the effect of image augmentation on the training data, the accuracy of the proposed VGG16-RCNN framework is assessed via sensitivity analysis, and results of the same reveal that horizontal and vertical flip together with zoom and rotation brings out an efficiency of 93.8%. However, a more generic model that works well with turbines located in different topological regions could be of more importance.

This paper presents a novel coronavirus herd immunity optimization (CHIO) algorithm for tuning the proportional-integral-derivative (PID) controller for the DC-DC boost converter. The closed-loop control action using the PID controller is designed to regulate the output voltage of DC-DC boost converter across the load end. CHIO is a nature-inspired meta-heuristic optimization algorithm formulated based on the way humankind handled the coronavirus pandemic (COVID-19) in recent years. This optimization algorithm exploits the herd immunity and social distancing concepts. The optimization algorithm has been developed on MATLAB/Simulink software for obtaining the optimum PID controller gains. Extensive simulations are conducted under (i) start-up response, (ii) reference voltage change (iii) load resistance change, and (iv) input voltage change to find the performance of the proposed controller. The obtained results indicate a successful convergence and satisfactory dynamic response of the output voltage under wide variation in the operating points.

This article proposes a novel Zernike radial neural network based adaptive control architecture for closed-loop control of output DC voltage in DC–DC buck power converter. The proposed combination of novel Zernike radial neural network estimator and the adaptive backstepping controller effectively compensates for wide range of perturbations affecting the converter system, in an online manner. The closed loop stability of the DC–DC buck power converter with the proposed neuro-adaptive backstepping controller is shown using Lyapunov stability criterion. Numerical simulations are conducted to examine the effectiveness of the proposed controller under start-up response and step changes in the load, source voltage and reference output voltage. Furthermore, the simulation findings are validated by conducting extensive real-time investigation on a laboratory prototype, under a wide range of operating points. The results obtained show a significant improvement in the transient response of both output voltage and inductor current of the converter, relative to the relevant control methods proposed in the recent past.

The Light Emitting Diodes (LEDs) are gaining more importance in several lighting applications due to their advantages, such as high efficiency, long life, and environment friendliness, over conventional lighting sources. The driver circuit is a significant component in an LED lighting system to provide regulated power to the lamp. Numerous, DC-DC converter topologies have been proposed for LED lighting applications. Under which low- and medium-power lighting applications such as domestic lighting, traffic lighting, and decorative lighting, non-isolated driver circuits are more beneficial. However, in high-power applications such as street lighting and industrial lighting, isolated and soft switching converters are mostly used as LED driver circuits. Due to high-power capability, reduced switching losses, less component count, high frequency of operation, and high efficiency, soft switching converters are drawing more attention in high-power applications. This paper presents a comparative analysis of resonant LED driver topologies proposed for multiple load lighting applications. Simulations of a few full bridge LED driver topologies have been carried out using MATLAB/Simulink environment. Various performance parameters are evaluated, and finally, conclusions are drawn.

Applications of power electronic converters have increased invariably in fields of engineering such as robotics, e-mobility and smart grids. DC-DC converters are employed as a switching devices to obtain a required amount of DC voltage in various industrial applications. Under the class of non-isolated DC-DC power converters, the buck converters are of specific interest, as they provide lower DC output voltage than the source DC voltage. In order to obtain a faithful output voltage tracking despite disturbances affecting the system, the converter is connected in the closed feedback loop. In this respect, this paper presents the design, development and experimental findings of Laguerre neural network driven adaptive control of DC-DC buck power converter. The stability of the proposed controller is established through Lyapunov stability criterion. Further, the results are compared with adaptive backstepping control method, by subjecting the converter to start-up test, step changes in the load resistance, input voltage and reference voltage tests. Thereafter, the performance is evaluated on DSP-based dSPACE 1104 processor in the laboratory. Finally, the results are compared in terms of settling time of output voltage state. The results indicate an enhanced dynamic performance of both output voltage and inductor current with the action of proposed controller, thus making it suitable for fast practical applications.

Lighting systems using light-emitting diode (LED) have drawn significant attention across the world. This is due to their promising features such as high energy efficiency, reduced greenhouse gas emission, and eco-friendly nature. However, these systems require constant current regulators to provide constant illumination. This article proposes a soft-switched full-bridge LED driver circuit for dc-grid applications with dimming control operation. The circuit consists of a soft-switched full-bridge converter to power different LED lamps with reduced device count from dc-grid voltage. The semiconductor switches of the full-bridge converter conduct a small current during on time due to interleaved inductor and equal current sharing of lamp-2 and lamp-3. This feature reduces the conduction losses. In addition, the proposed converter yields less component count per lamp, dimming operation through on–off control and zero voltage switching, which results in low switching losses. The detailed steady-state analysis of the proposed converter for dc-grid applications with dimming control operation is presented in this work. The performance of the proposed converter is compared with other similar topologies available in the recent literature. Numerical simulations and real-time experimental validations are conducted to evaluate the steady-state performance of the proposed converter topology for LED applications, driving multiple lamp loads from dc-grid. It has been established that the efficiency of the proposed full-bridge converter is 97.52% at the rated power.

Direct torque control (DTC) is one of the most prominent control techniques used by permanent magnet synchronous motor (PMSM) drives in industry applications. Nevertheless, the presence of hysteresis controllers and inaccurate voltage switching table in traditional DTC results in higher torque and flux ripple. This study proposes an enhanced DTC functioned Surface-mounted PMSM (S-PMSM) drive with mitigation of torque and flux ripple. The operation relies on generating the reference voltage vector (VV) in a stationary reference frame, which supports control of torque and flux without hysteresis controllers. The reference VV generation is simple and does not affect control robustness. The position of reference VV in a sector is used to build the voltage vector (VV) switching table. As a result, the application of nearest discrete VV to reference VV produces optimal torque and flux control. Moreover, redundant switching combinations of null VV are effectively used for possible minimization of switching frequency of two-level voltage source inverter (VSI) supplied S-PMSM drive. Therefore, proposed DTC gains improved S-PMSM drive response along with switching frequency reduction. In dSPACE-RTI 1104 platform, experimental response of S-PMSM drive under various operating conditions have been depicted to highlight the proficiency of proposed DTC in comparison with existing DTC.

In this article, an efficient soft-switched light emitting diode (LED) driver with input regulation is proposed. The converter drives multiple lamps, and it is divided into two sections. Lamp-2 and lamp-3 are driven by a full bridge converter (FBC), while lamp-1 is placed in series with the input dc voltage source. Power is delivered to lamp-1 without passing through the FBC, which results in improved efficiency. The main benefits of the presented LED driver are: 1) lower current ratings of the FBC switches; 2) ripple-free lamp currents; 3) zero voltage switching (ZVS); 4) high power efficiency; 5) drives multiple lamps; 6) input regulation for source variation; and 7) lower components per lamp. To reduce the current rating of FBC switches, two identical lamps are powered using interleaved inductors. Owing to this, the lamps experience ripple-free currents. Further, due to this, the ZVS is achieved which results in high efficiency. A closed-loop buck-boost converter will compensate for the variations in input by adjusting the duty cycle. The converter operating modes, steady state, and efficiency analysis are discussed in detail. Moreover, to indicate the performance of the converter, a 130 W prototype is built, and experimental results are presented.

Background and Objective: Persistent diabetes results in diabetic retinopathy (DR), affecting the retinal blood vessels (BVs), causing lesions. Rapid identification and treatment are crucial for preventing vision loss. Low ophthalmologist to patient’s ratio results automating the DR detection a dire need. Therefore, a feature extraction method is proposed using a Mamdani fuzzy inference system (FIS) classifier for efficient identification. Methods: Mathematical morphology, region growth, and 12-region search computation have been used to mask the BVs and macula. The masked green plane image was subjected to Nick's thresholding to locate the dark lesions, from which statistical features were extracted and employed in the Mamdani FIS to classify the DR. Results: On evaluating a total of 909 images from the MESSIDOR database shows, average sensitivity, specificity, area under the curve receiver operating characteristics, and accuracy of 99.7%, 99.8%, 99.4%, and 99.6%, respectively. The algorithm performs well in real-time images from two local hospitals. Conclusion: The proposed technique provides a powerful yet flexible tool for improving the diagnosis and treatment of this condition that threatens vision, as it combines the strengths of fuzzy logic, clinical knowledge, and adaptive learning to provide precise, timely, non-invasive, and economical solutions.

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on-board (adaptive) learning to counteract the unknown non-linear time-varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

Angular velocity control in DC-DC converter-driven direct current (DC) motors exhibit several challenges in numerous applications. This article proposes a novel single functional layer Legendre neural network integrated adaptive backstepping control technique for the DC-DC step down converter-permanent magnet DC (PMDC) motor system. The proposed controller first aims to estimate the uncertainties in an online mode and then compensate the same efficiently during the robust control action. The closed loop feedback stability of the entire system under the action of proposed controller and the online adaptive learning laws are proved using Lyapunov stability criterion. Further, the proposed controller is numerically simulated for various test conditions including; (a) startup response, (b) a step change in the load torque and (c) reference angular velocity tracking. The transient performance measures of angular velocity such as peak overshoot, peak undershoot and settling time have been observed under the proposed control design and compared with the response obtained from proportional-integral-derivative (PID) controller. Finally, the results presented demonstrate the efficacy of the proposed controller in yielding an enhanced performance under both nominal and perturbed test conditions over a wide operating range.

In this paper, a battery charging topology has been designed and developed for the fast charging of Li-Ion batteries. The charging circuitry comprises of a Proportional-Integral-Derivative (PID) controlled DC-DC buck converter system for reducing the charging time in Li-Ion batteries. Battery charging time depends on several factors and the charging current is one of the major criteria. In this work, the buck converter is used to attain a high charging current, besides providing the regulated voltage to the battery. Initially, the AC supply obtained from the mains is converted to DC using an AC-DC rectifier. The rectifier output is further fed to the buck converter to increase the output current of the circuit. The buck converter reduces the output voltage and increases through it. The circuit parameters are designed by considering the commercially available Lithium-ion battery LIR18650 as the load with a capacity of 2600 mAh and a nominal voltage of 3.7 V. The considered battery requires a standard charging current of 0.5 A, however the circuit is designed to provide the rapid charge current of 1.3 A as the output by using the buck converter. The converter is operated in continuous conduction mode and helps in charging the battery under constant current mode. In order to avoid interruption to the charging current when there is a simultaneous discharge of the battery, further improvement in the closed-loop control action is made by employing PID controller. Extensive simulation work have been conducted using the MATLAB/Simulink tool. The results obtained suggests there is a significant reduction of charging time under different conditions compared to the conventional method of battery charging.

This paper presents an exhaustive search approach to determine the best location and size of PV placement for power loss minimization of radial distribution networks. In this approach, the network power loss is determined by placing PV in each location, one at a time, and the size of PV in the same location is varied between 10 and 300 kW with an increment of 10 kW. The combination of location and size of PV which provides the minimum network power loss can be the best location and size of PV for power loss minimization of radial distribution networks. The forward–backward sweep load flow algorithm is used to incorporate the PV model. The 33-bus radial distribution network is used to demonstrate the approach. The simulation results show that the placement of a suitable size of PV in some specific locations significantly reduces the network power loss.

With the rapidly expanding infrastructure of the solar energy system, the need for an accurate solar prediction has become an essential part of the renewable energy sector. Over the past decade, various machine learning (ML) algorithms have been used for this purpose. Although the prediction of solar irradiance forecasting has been discussed in a large number of studies, the use of meta-heuristic optimization techniques has not been explored to select features for the forecasting model. This study comprises two meta-heuristic optimization techniques such as simulated annealing (SA) and ant colony optimization (ACO) for feature selection. The results show that feature selection based on meta-heuristics gave better results than models without feature selection. Amongst the two optimization methods, ACO outperformed SA with some exceptions. For SA, the declining order of performance observed is extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), decision tree (DT) and support vector regression (SVR), while for ACO the declining order observed is XGBoost followed by MLP, RF, DT and SVR. This manuscript indicates the potential capability of meta-heuristic techniques for accurate prediction of global horizontal irradiance (GHI) given a wide array of feature variables.

In this paper, an adaptive controller is proposed for DC–DC buck converters featuring prescribed time bound estimation of unknown system uncertainties and exogenous disturbances followed by nominal output performance recovery. The objective of the proposed control is to attain a robust output voltage tracking in buck converter in presence of parametric, non-parametric, matched and mismatched perturbations across wide operating range. Different from neural network estimators and characterizing substantially low computational complexity, an online estimator is presented to reconstruct the incurred uncertainty. The estimated additive uncertainty is thereafter fed to the nominal backstepping controller for subsequent compensation in finite time. Exact recovery of nominal output voltage tracking is claimed in a piecewise sense owing to the accuracy and precise estimation of the unknown unparametrized lumped uncertainty manifested in the form of large sudden variations in load and input voltage. Rigorous performance and stability analysis of the online estimator, along with similar analysis of the overall tracking control system are undertaken. Extensive numerical study is carried out to investigate the performance of the proposed control scheme. Further, experimentation of the proposed controller on a dc–dc buck converter using control desk DS1103 with an embedded TMS320F240 processor has been performed. The obtained experimental results demonstrate a good agreement with the simulation findings.

This paper deals with the output voltage regulation problem of dc-dc boost converter feeding a resistive load. A new control mechanism based on Chebyshev neural network embedded in an adaptive backstepping framework is proposed for the boost converter control. Since the converter is complex, time varying and non-linear in nature, it exhibits high sensitivity to unanticipated disturbances in the load current. Hence, designing a robust control mechanism to attain a satisfactory transient and steady state performance over a wide range of operating points is a challenging task. In this work, a control law is derived based on the systematic and recursive design strategy of adaptive backstepping method. A single layer functional link Chebyshev neural network is employed for a fast estimation of uncertain and time varying load profile of the boost converter. The stability of overall converter equipped with the proposed controller is proved using Lyapunov stability criterion. Further, in order to validate the proposed methodology, the boost converter is simulated in MATLAB/Simulink software and is subjected to different load perturbations. The efficacy of the proposed control is highlighted by evaluating it against the conventional adaptive backstepping control under identical conditions. The results obtained reveals that the proposed control is much faster in estimating the unknown load parameter and offers satisfactory output voltage tracking, yielding fast response and low peak overshoot/undershoot in the event of unknown load perturbations. Experimental investigation using dspace DS1103 controller is further carried out to validate the efficacy of proposed control scheme.

DC-DC step-down/buck converters are prominent part of DC power supply system. The dynamics of DC-DC step down converter are nonlinear in nature and are largely influenced from both parametric and external load perturbations. Under its closed loop operation, obtaining a precise output voltage tracking besides satisfactorily inductor current response is a challenging control objective. In this regard, this article proposes a novel Laguerre neural network estimation technique for the approximation of unknown and uncertain load function, followed by its subsequent compensation in the adaptive backstepping controller. A detailed design of the proposed estimator and adaptive backstepping controller along with closed loop asymptotic stability have been presented. Further, the proposed control mechanism is evaluated through extensive numerical simulations while subjecting the converter to input voltage, reference voltage and load resistance perturbations. Furthermore, the results are verified by testing the proposed controller on a laboratory prototype with DSP based TM320F240 controller board. The transient performance metrics such as settling time and peak overshoot/undershoot are evaluated and compared against adaptive backstepping control and PID control methods. Finally, the analysis of results reveals that the proposed control methodology for DC-DC step down converter offers a faster transient output voltage tracking with smooth and satisfactory inductor current response over a wide operating range.

An affiliation for Dr. Tousif Khan Nizami is missing from the original article. In addition to the Department of Electronics and Electrical Engineering at Indian Institute of Technology, Dr. Tousif Khan Nizami is affiliated with the Department of Electrical and Electronics Engineering at SRM University-AP, Amaravati 522 502, India.

In this paper, the issue of output voltage regulation in buck type dc-dc converters is addressed using a current sensorless control technique. The proposed strategy integrates a finite time current observer with an adaptive backstepping control scheme to yield a cost-effective and robust control mechanism. The overall controller stability in the sense of Lyapunov is proved. Applicability of the proposed control is verified experimentally on a buck converter in the laboratory. The control scheme is implemented on dSPACE DS1103 platform based on DSP TM320F240 processor. To examine the efficacy of the proposed method, the buck converter is subjected to a wide change in input voltage, load resistance and reference voltage. For comparison purpose, a conventional adaptive backstepping control scheme is evaluated under identical conditions of experimental study to examine the merit of the proposed control. The results obtained reveal that the proposed control is prompt in rejecting perturbations and achieves a smooth, reliable and satisfactory output voltage regulation with faithful and time bound estimation of inductor current. Thereby, this investigation demonstrates the validity of the proposed control in maintaining a stringent output voltage regulation in buck converters.

A neuro-adaptive backstepping control (NABSC) method using single-layer Chebyshev polynomial based neural network is proposed for the angular velocity tracking in buck converter fed permanent magnet dc (PMDC)-motor. Owing to their universal approximation property, neural networks have been utilized for approximating the unknown nonlinear profile of instantaneous load torque. The inherent computational complexity of the neural network based adaptive scheme has been circumvented through the use of orthogonal Chebyshev polynomials as basis functions. A detailed stability and transient performance analysis has been conducted using Lyapunov stability criteria. The proposed control scheme is shown to yield a superior output performance with enhanced robustness for wide variations in load torque and set-point changes, compared to existing conventional approaches based on adaptive backstepping. The theoretical propositions are verified on an experimental prototype using dSPACE, Control Desk DS1103 setup with an embedded TM320F240 Digital Signal Processor proving its applicability to real-time electrical systems. The efficiency of the proposed strategy is quantified using performance measures and are evaluated against the conventional adaptive backstepping control (ABSC) methodology. Ultimately, this investigation confirms the effectiveness of the proposed control scheme in achieving an enhanced output transient performance while faithfully realizing its control objective in the event of abrupt and uncertain load variations.

This article presents an experimental realization of adaptive backstepping control methodology on a cascaded buck converter permanent magnet dc (PMDC)-motor combination for angular velocity control. The experiment aims at illustrating the practical applicability of adaptive control to power converters fed with a DC motor load. The systematic design procedure of conventional backstepping control design is enhanced by incorporating an online adaptive control mechanism to estimate the unknown non-linear load torque. Asymptotic stability of the closed loop system under the action of proposed control law is ensured and update law is derived satisfying Lyapunov stability criterion. The experimental investigation is conducted using dSPACE, Control Desk DS1103 setup with an embedded TM320F240 Digital Signal Processor. The buck dc-dc converter fed PMDC motor system is subjected to a wide variation in load torque and set point angular velocity tracking. The results obtained through adaptive backstepping control scheme have been evaluated against the conventional backstepping control mechanism. Results highlight a superior performance using adaptive backstepping control by producing an accurate and time bound estimation of unknown load torque, under both nominal and perturbed conditions, thereby improving the transient and steady state response of desired angular velocity.

This paper proposes a novel integrated robust system identification and state of charge estimation of Li-ion battery. A finite time extended state observer is used for state of charge (SOC) estimation of Li-ion battery. A relay feedback test has been adopted to yield accurate battery parameters in noisy environment. New identification method using state space based relay feedback approach is employed with a disturbance nullifier to extract the parameters of battery model. Thereafter, the objective of SOC estimation is carried out by designing a novel finite time extended state observer based on higher order sliding modes. The unknown battery model parameters are determined first, followed by the estimation of SOC next, under both charging and discharging conditions. This investigation finds a high amount of accuracy in the proposed battery model identification method, besides an accurate and time bound estimation of SOC using the proposed observer scheme.

This article presents a novel adaptive control method based on neural networks for robust output voltage tracking in buck converters over a wide operating range. Buck converters are significantly sensitive to input, parametric and load perturbations. The intrusion of mismatched uncertainties due to load changes make the controller design task a challenging issue. Hence, a feedback control law based on the adaptive backstepping control technique integrated with a single layer type II Chebyshev neural network (CNN) is proposed. The distinctive feature of the type II CNN is its quick and accurate estimation of time varying load disturbance which is thereafter utilized for subsequent compensation in the control law. The neural networks are trained online using a Lyapunov based learning algorithm. The efficacy of the proposed control is studied for wide variations in load resistance, input voltage and reference voltage and compared against control using conventional adaptive backstepping method. Simulations are performed in MATLAB tool and experimentation is conducted using dSPACE DS1103 setup with TM320F240 DSP. The results demonstrate a good agreement between the simulation and experimental findings. Further, the proposed control achieves a remarkable reduction in settling time and peak overshoot/undershoot in the event of occurrence of unanticipated disturbances.

This paper presents a novel control scheme for the output voltage tracking problem of DC-DC buck converter. The proposed control structure integrates a direct adaptive backstepping control approach with a single functional layer Hermite neural network (HNN). Buck converter is a variable structure model with a highly uncertain loading pattern. Hence an estimator based on HNN is employed to estimate the load perturbations influencing the converter. The accurate and timely information about the unanticipated load resistance strengthens the control law in order to attain the desired objective. The online adaptive laws are derived based on Lyapunov stability criterion ensuring the overall stability of the buck converter equipped with the proposed control. The performance of the proposed method is evaluated by subjecting the buck converter to a wide range variations in load resistance, input voltage and reference output voltage. Further, the merits of proposed control are highlighted by comparing the performance with the standard adaptive backstepping control technique under identical conditions of simulation study. Performance indices such as peak overshoot, peak undershoot and settling time have been evaluated, which clearly indicate the outperformance of proposed control.

A fast neuro-adaptive compensation based control scheme is proposed for dc-dc buck converters. The compensation of uncertainties is obtained by design and development of a single functional layer Hermite neural network. The estimates are further utilized for subsequent compensation in the online adaptation process using backstepping control. The stability of over all closed loop converter equipped with the proposed control is proved to be asymptotically stable under Lyapunov stability criterion. Extensive simulation and experiments are conducted to evaluate the response of buck converter under the action of proposed control at wide operating points. Further, the results are compared with recently published relevant control method. Finally, the performances indices suggests a significant improvement in the transient performance by yielding lesser settling time and lower peak overshoot/undershoot under proposed control, thereby confirming the validity proposed scheme.

A finite time current observer based adaptive backstepping control strategy is proposed for the output voltage regulation of buck type DC-DC converters. The proposed current observer is designed by utilizing only the output voltage information to reconstruct the inductor current profile while achieving an adaptive control by means of backstepping procedure. This method eliminates the usage of extra sensor involved in sensing the inductor current, thereby reducing the cost of control besides overcoming the problems of measurement noise encountered while sensing. Simulations have been performed on a buck converter using Matlab software under both continuous and discontinuous conduction modes. Further, the usefulness of proposed scheme is also examined by subjecting the buck converter system to sudden changes in load resistance. The results obtained reveal that the proposed observer is successful in not only estimating the nominal inductor current but also estimates the perturbed level of inductor current under load disturbances in finite time. Satisfactorily transient and steady state response in the output voltage are ensured by using the proposed method.

An experimental investigation into a hybrid back-stepping control proposed for the output voltage regulation of DC-DC buck converter is carried out in this paper. The proposed hybrid control algorithm utilizes a backstepping procedure in conjunction with a robust sliding mode control mechanism to counter both matched and mismatched uncertainties. An experimental prototype of buck converter is fabricated and controlled with the hybrid control scheme in the laboratory. The dynamic response under wide variation of operating points is investigated. The control scheme is further extended to a discontinuous conduction mode buck converter which is even more nonlinear in nature. A DSP based DS1103 platform is used to conduct experimentation. Experimental results demonstrate a satisfactory transient and steady state performance of both output voltage and inductor current.

Buck DC-DC converter is used in many applications to supply a fixed amount of DC voltage. They are highly sensitive to the frequently changing loading conditions. Such a situation demands a robust control mechanism which can guarantee satisfactory performance of the buck converter over a widely changing load. This can be made possible by developing an adaptive control scheme which can estimate the true values of the uncertain load parameters in the least possible time. This paper proposes an adaptive Chebyshev neural network (CNN) based backstepping control technique for the output voltage regulation of a DC-DC buck converter. The proposed control strategy utilizes neural networks in approximating the unknown non-linear nature of load resistance by using orthogonal basis Chebyshev polynomials. CNN approximation tool in conjunction with the conventional backstepping procedure yields a robust control mechanism. The weights of neural network are tuned online using adaptive laws satisfying the overall closed loop stability criterion in the Lyapunov sense. The performance of the proposed control is demonstrated for wide range perturbations by subjecting the buck converter to changes in load resistance, input voltage and reference output voltage. Simulation studies are conducted to evaluate the performance of the proposed controller against radial basis function neural network based adaptive backstepping control and conventional adaptive backstepping. The results obtained are further verified from experimentation on a hardware setup using DSP based TM320F240 processor. Thus, the investigation confirms effectiveness of the proposed control scheme as the output voltage shows a fast and accurate response besides successfully rejecting the disturbances acting upon it.

This article presents design of a Proportional-Integral-Derivative (PID) control for a buck converter by incorporating the principles derived from the processes of Artificial Immune System present in vertebrates in the control design algorithm. The buck converter represents a class of variable structure systems and its controller design by conventional means yields a near-satisfactory performance, however not the best transient and steady state dynamics at wider range of operating points. Therefore a feedback control design problem of buck converter is rearranged as an optimization goal and the concern parameters of the PID controller are found through an intelligent Artificial Immune System (AIS) based technique. Computations have been done by using Matlab software. The output voltage response of buck converter is tested for 1.) reference voltage change, 2.) load resistance change and 3.) input voltage change. To verify the findings obtained in simulations, a prototype of buck converter is build and controlled in the laboratory with the proposed methodology. The results found are then evaluated against the performance of conventional controller design and genetic algorithm (GA) based approach, followed by tabulation of performance measures, which clearly indicates that the AIS method of PID controller design provides better static and dynamic response in the output voltage besides achieving a faster convergence of parameters, thereby confirming the validity of new approach.

This paper proposes a novel control technique for the Buck type DC-DC converters using adaptive backstepping control and Chebyshev neural network. To enhance the transient performance of both the capacitor voltage and the inductor current under nominal conditions, input voltage fluctuations and load variations, this control algorithm has been proposed. The systematic design of backstepping controller has been improvised by incorporating the approximation of unknown load resistance parameter by a single layer Chebyshev neural network. Results have been compared with a recently developed adaptive terminal sliding mode control technique. The proposed method significantly improves voltage and current transient performances.

This paper presents a backstepping control technique in combination with the sliding-mode mechanism for simultaneous control of the capacitor voltage and inductor current in a DC–DC buck converter. The proposed hybrid controller is capable of tackling both the matched and mismatched types of uncertainties like input voltage change and load current variation. The backstepping control can reject both matched and mismatched types of uncertainties, whereas the sliding-mode control is robust against matched uncertainties only. The systematic controller design procedure of backstepping and invariance property of SMC for matched uncertainty have been utilized for robust tracking of both the capacitor voltage and inductor current simultaneously. It is found that by switching between these two different control structures, one exclusively for the matched and the other for the mismatched uncertainties, excellent transient and steady-state performances can be ensured. In the case of backstepping control, performance of the buck converter is largely dependent on design parameters. Hence, these design parameters are judiciously selected to assure optimum performance. Simulation studies have been carried out to verify the effectiveness of proposed hybrid control structure. Transient performances like peak overshoot, peak undershoot, settling time, and also steady-state error have been measured under widely varying changes in input voltage and load current. Simulation results demonstrate that as compared to existing controllers, the proposed hybrid control strategy offers superior transient and steady-state performances.