The integration of Digital Twin (DT) technology with Machine Learning (ML) introduces an advanced paradigm for intelligent energy management in IoT-enabled hybrid microgrids incorporating electric vehicle (xEV) charging infrastructure. The proposed framework combines real-time IoT sensing, DT-based predictive modeling, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to achieve autonomous anomaly detection, fault prediction, and resilient system operation. DT continuously mirrors the physical microgrid by comparing simulated system states with real-time IoT measurements, enabling early identification of anomalies arising from sensor faults, communication failures, missing data, or inaccurate measurements. Although IoT devices provide continuous monitoring of critical parameters such as voltage, current, power flow, and battery state, their vulnerability to disruptions is effectively mitigated through reliable DT-generated predictions. The ANFIS controller utilizes validated DT data to dynamically adapt control strategies, ensuring stable energy flow under both normal and disturbed operating conditions. Simulation results demonstrate that the system maintains approximately 93% operational efficiency when IoT data deviations remain below 7%, and sustains at least 90% efficiency during severe disturbances with data loss reaching up to 70%. Comparative analysis indicates a 25% improvement in energy efficiency, a 30% reduction in energy wastage, and a 40% faster fault detection response compared to conventional controllers. The proposed DT-ML-ANFIS framework offers a robust, scalable, and reliable solution for next-generation smart microgrids.

Vehicle lateral control under low-adhesion conditions is challenging due to rapid variations in tire-road friction and nonlinear vehicle dynamics during aggressive steering maneuvers. Model Predictive Control (MPC) is well suited for such problems but is highly sensitive to the selection of cost-function weights. This paper presents a $\mu$-aware MPC framework in which MPC weighting parameters are optimized using the Aquila Optimizer (AO), a recent meta-heuristic optimization algorithm with improved convergence characteristics. A double lane-change maneuver with a linearly decreasing road friction profile is used to evaluate controller performance. The proposed AO-tuned MPC is compared with a conventionally tuned MPC and a Particle Swarm Optimization (PSO)-based MPC under identical conditions. Simulation results demonstrate that the AO-tuned MPC achieves significantly improved sideslip angle and yaw-rate tracking with smoother steering action, particularly during lowfriction counter-steering phases. Quantitative analysis shows a substantial reduction in RMSE compared to conventional and PSO-based MPC, confirming the effectiveness of the proposed approach for robust vehicle lateral control under time-varying friction conditions.

Chemotherapy aims to eliminate rapidly proliferating cancer cells, but its success depends on precise drug dosage control to balance efficacy and toxicity. This study proposes a fractional-order proportional-integral with fractional-order filter (FOPIFOF) controller for closed-loop chemotherapy drug scheduling. Designed via an indirect fractional-order internal model control (IFOIMC) strategy, the controller requires only a single design parameter. A frequency-shifted fractional-order benchmark cancer patient model, identified using the FOMCON toolbox, is employed, with the shift parameter (psi) optimized for minimum integrated time absolute error (ITAE). Performance is benchmarked against conventional PID, Dragonfly Algorithm-based integral-proportional-derivative (DAIPD), DA-tuned internal model control (DAIMC), and DA-tuned fractional-order IMC (DAFOIMC). Results show a total drug dose variation of 8.09, substantially lower than DAFOIMC (28.46) and DAIMC (14.71), indicating smoother infusion. The proposed approach improves integral of absolute error by 78.6% over DAIPD, 22.4% over DAIMC, and 20.5% over DAFOIMC, while maintaining robustness under +/- 20% variations in patient parameters. The findings demonstrate that the proposed FOPIFOF controller offers accurate drug concentration tracking, reduced toxicity, and enhanced robustness, making it a promising and practical method for improving chemotherapy drug delivery systems.

Integrating Electric Vehicles (EVs) into power grid presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. This system dynamically optimizes the coordination of renewable energy sources solar PhotoVoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.

A simple and robust automatic suction pressure control system is designed for a newly developed portable meconium aspirator system (PMAS). From the step response data of the system, the transfer function (TF) of PMAS is obtained. Using the obtained TF, a proportional-integral-derivative controller in series with a fractional-order filter (PIDFF) is designed using a fractional-augmented internal model control (FAIMC) strategy. The adjustable parameter of the PIDFF controller is obtained using the reference to disturbance ratio (RDR) and random search algorithm. The designed controller is then simulated using MATLAB which is interfaced with the PMAS hardware. Simulation and experimental results in comparison with prevalent fractional-order schemes demonstrate the superior control performance achieved by the proposed pressure control system in terms of overshoot-free servo tracking and faster disturbance rejection. It is vindicated that the suction pressure is maintained in the desired value effectively amid disturbances and uncertainties in the obtained transfer function. Finally, a robust stability analysis of the suggested design is also carried out.

The potential for Internet of Things (IoT) technology to transform energy management has led to significant interest in its incorporation into smart grid systems. This review discusses the state of IoT-powered smart grids today, focusing on applications, current technology, and power quality (PQ) issues. Key problems including harmonics, transients, and voltage fluctuations are identified, and mitigation techniques using sophisticated filters and intelligent systems like fuzzy logic control (FLC) and artificial neural networks (ANN) are investigated. Concerns about interoperability and scalability are among the other challenges the review lists for IoT implementation. The revolutionary potential of IoT in improving smart grid efficiency and dependability is highlighted by our findings, which provide valuable insights for scholars and practitioners seeking to develop this sector.

To tackle the challenge of improving Power Quality (PQ) in modern power grids, we introduce an innovative Internet of Things (IoT)-based Smart Grid (SG) energy surveillance system. Our research is driven by the necessity to enhance power quality and optimize energy management in increasingly complex grids that incorporate renewable energy sources like Solar PV and Wind Generating Systems. Traditional methods for managing power quality often fall short, resulting in inefficiencies and potential disruptions. Our solution features an advanced IoT-based system that utilizes the Adaptive Neuro-Fuzzy Inference System (ANFIS), combining Artificial Neural Networks (ANN) and Fuzzy Logic Systems to enhance power distribution and control. This system uses a Wireless Sensor Network for real-time data collection and analysis, allowing for precise monitoring of electricity usage and improved energy management and cost reduction. Our findings indicate that this innovative approach not only boosts power quality but also significantly enhances the efficiency of renewable energy sources, showing a 20.50% performance increase during the startup phase of Solar PV-Wind Generating Systems. This highlights the system’s potential to advance power quality management and provide substantial benefits in energy regulation and cost efficiency.

The potential for Internet of Things (IoT) technology to transform energy management has led to significant interest in its incorporation into smart grid systems. This review discusses the state of IoT-powered smart grids today, focusing on applications, current technology, and power quality (PQ) issues. Key problems including harmonics, transients, and voltage fluctuations are identified, and mitigation techniques using sophisticated filters and intelligent systems like fuzzy logic control (FLC) and artificial neural networks (ANN) are investigated. Concerns about interoperability and scalability are among the other challenges the review lists for IoT implementation. The revolutionary potential of IoT in improving smart grid efficiency and dependability is highlighted in our findings, which provide valuable insights for scholars and practitioners seeking to develop this sector.

In this work, a novel control strategy is proposed to obtain the stability boundary in addition to reduce the transients around the equilibrium points. To encounter the described problem, a new approach of combining the bifurcation analysis with the state feedback controller is proposed. A bifurcation analysis at different equilibrium points is performed to obtain the stable region of operation. In addition to this, the transients behavior of the system is also obtained simultaneously in the form of eigenvalues plots. The objective of the proposed controller is to generate a control law and state variables to reduce the transients keeping the system within the stability boundary by tuning the reference input matrix. From the obtained simulation results, it is seen that, by combining the bifurcation analysis with state feedback controller, the transients and the steady state error are reduced by selecting the purely negative real eigenvalues and reference input matrix respectively. The obtained closed loop control law and the state variables utilizing Ackerman's Formula are found within the stability limit. A sensitivity index obtained from local sensitivity analysis verifies the relationship between the stability boundary at different longitudinal velocity on a low-friction road obtained from bifurcation analysis.

The vehicles are prone to accidents during cornering on a wet or low friction coefficient roads if the longitudinal velocity (Vx) and steering angle (δ) are increased beyond a certain limit. Therefore, it is of major concern to analyze the behaviour and define the stability boundary of the vehicle for such scenarios. In this paper, stability analysis of a 2 degrees of freedom nonlinear bicycle model replicating a car model including lateral (sideslip angle β) and yaw (yaw rate r) dynamics only operating on a wet surface road has been performed. The stability is analysed by utilizing the phase plane method and bifurcation analysis. The obtained converging and diverging nature of the trajectories (β, r) depicts the stable and unstable equilibrium points in the phase plane. The movement of these points results in the transition of the stability known as bifurcation due to the change in the control parameters (Vx, δ). The Matcont/Matlab is utilized to obtain the bifurcation diagrams and the nature of bifurcations. The obtained results show that a saddle node (SNB) and a subcritical Hopf bifurcation (HB) exists for steering angle (±0.08 rad) and higher than (±0.08 rad) with Vx = (10-40) m/s respectively. The SNB and HB denotes the spinning of the vehicle and sliding of the vehicle respectively, thus generating a unstable behaviour. A stability boundary is obtained representing the stable and unstable range of parameters.

This paper introduces a novel Distributed Robust Volt-VAr Control Strategy (DRVVCS) aimed at achieving accurate proportional reactive power sharing in islanded microgrids, emphasizing adaptability, simplicity, and reduced reliance on communication. This scheme also enhances the flexibility and scalability of the system, which are generally challenging in distributed control schemes. The system’s seamless transition between different network topologies and accurate reactive power sharing under varying loads with suggested DRVVCS are evaluated through comprehensive simulations. The proposed scheme is also tested on a standard IEEE 33-bus system with three sources and a standard IEEE 69-bus system with three and five sources to observe its effectiveness in different operating scenarios. The proposed DRVVCS is compared with conventional droop control and other existing methods to showcase its effectiveness and versatility.

With the rising penetration of photovoltaic (PV) plants on low voltage distribution systems, the generation of current harmonics as well as its impact on transformer operation is a current concern. The present research work develops a mathematical relationship of solar intensity (I(t)) with PV-inverter-generated total harmonic distortion of current (THDi,inv.), and then uses IEEE recommendations to present the impact of THDi,inv. on the life of a three-phase distribution transformer (TPDT). The validation of the presented model is done by real-time data monitoring from a 100-kWp solar roof-top photovoltaic (SRTPV) system, integrated with an 11-kV grid supply through a 63-kVA TPDT in the composite environment of north India. According to the results, decreasing I(t) values from 857 W/m(2) to 35 W/m(2) raise THDi,inv. from 3.57% to 63.43%. It is also observed that the production of poor THDi,inv. is high in the winter season (daily average = 27.44%) in comparison to their values in the summer season (daily average = 15.21%). For I(t) values less than 315 W/m(2), the generation of large THDi,inv. (above 15%) takes place and it increases the loss of life of TPDT by a factor of 6.0. [DOI: 10.1115/1.4055101]

The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.

This paper presents a control strategy to achieve yaw and roll stability by taking into account the physical interaction between the yaw and roll dynamics to prevent vehicle collisions in hilly or curved terrain. The mathematical model is formulated utilizing a roll dynamic model with a small tyre slip angle and a bicycle model with two degrees of freedom considering coupling of yaw and roll dynamics. An adaptive model predictive controller and a PID controller are included in the proposed control methodology so that a real-time scenario of variation in the longitudinal velocity and friction coefficient is considered. Stability limits are established based on the yaw rate, sideslip, and roll motions of the vehicle, taking into account the effects of the road angle. The friction coefficients of 0.4 and 0.8 are chosen for wet and dry road surfaces to show manoeuvrability and force the vehicle to avoid rollover condition. Using numerical simulations in Matlab R2022a, the effectiveness of the designed controller is assessed. A root mean square error (RMSE) is calculated for the proposed methodology for the evaluation of the performance and the values are obtained as 3.032 and 3.912 for friction coefficient of 0.8 for yaw rate and roll angle respectively. On comparing with the other methodology, it is found that the performance of the proposed method is better based on RMSE. Also, the fluctuations at the corners are removed and the variables are bound inside the stability limit, thus avoiding the vehicle from accidents in hilly areas. The robustness of the controller towards increasing the mass of the vehicle by 5% and 10% is found to be good.

This paper proposes a trajectory tracking strategy based on a linear extended bicycle vehicle model and a linear tire model. Model predictive control is used to track the lateral positions, sideslip angle, yaw rate, and load transfer ratio. To solve the MPC objective function in the field of vehicle stability, evolutionary algorithms are implemented, including multi-objective genetic algorithm (MOGA), particle swarm optimization (PSO), and hybrid particle swarm optimization genetic algorithm (HPSOGA). These algorithms are used to optimize the weights as well as the control input. To handle constraints violations, the penalty function method is exploited. To demonstrate the sensitivity to the respective parameters, a local sensitivity analysis is performed on the sideslip angle. The obtained results show a simultaneous change in all output variables due to the state coupling effect. A root mean square error (RMSE) of hybrid algorithm for sideslip angle is calculated as 0.032 and for PID controller is obtained as 0.18. It is found that HPSOGA (hybrid algorithm) based MPC performs better in comparison to PID controller.

Due to government support and clean energy production, the integration of rooftop PV with industrial power systems is becoming very popular. This integration affects the Power Factor (PF) of electrical system and necessitates the development of relations between PF and PV parameters. In this study, a mathematical model is developed that relates solar intensity to PF and also to the capacitive reactive power (required to improve the PF). The model can be used to forecast the expected decrease in PF (after PV integration with the industrial power systems), so that corrective action can be taken ahead of time. To test the model, real-time measurements and experiments are conducted on a 100 kWp PV system integrated with 11 kV grid supply and industrial load of 150 kW under the composite climate of north India. The result shows that for a fixed load (108.75 kW and 46.62 kVAR), the linear increase in solar intensity from 20 W/m2 to 857 W/m2 reduces the PF from 0.92 lagging to 0.63 lagging in linear fashion. For a connected average load of 40.34 kW, the increase in average solar intensity from 250 W/m2 to 430 W/m2 reduces the PF from 0.95 lagging to 0.85 lagging (on annual basis) and the 10.60 kVAR rated shunt capacitor is required to improve the PF to unity.

This study aims to achieve disturbance rejection and set-point tracking performance while controlling the suction pressure in a Portable Meconium Aspirator System (PMAS) for newborn babies. Maintaining suction pressure of PMAS in the desired value with the help of a small 15 W DC vacuum pump and the pressure feedback signal is challenging because of disturbances, plant-model perturbation, and measurement noise in a feedback sensor of the PMAS system.First of all, the transfer function of a novel PMAS using a mini DC vacuum pump and BMP280 barometric pressure sensor is estimated with the help of input and output relationship. Then a fractional-order internal model control proportional-integral-derivative (FO-IMC PID) controller is designed for pressure control of a PMAS using the estimated transfer function. The FO-IMC PID controller is tuned using the reference to disturbance Ratio (RDR) technique and random search (RS) algorithm. Simulation results show the robust control performance in the presence of suction load variations, disturbances in pressure, and plant perturbations.

Incidence of non-uniform illumination (NUI) degrades the affected solar photovoltaic modules temporarily. However, in worst case, it may also lead to permanent degradation. Thus, non-uniform illumination sensing in terms of encompassed area over solar photovoltaic module becomes important for sustainable power generation. An integrated framework by combining optical and thermal images for non-uniform illumination sensing and classification (static and dynamic) is proposed. The proposed technique detects hotspots along with identification of the nature of shading on the solar photovoltaic modules. Additionally, Hungarian Kalman filter is implemented for estimating the coverage of the non-uniform illumination-affected region including its abrupt shape. The proposed estimation technique also calculates the total loss in the output energy of the solar photovoltaic system due to non-uniform illumination. Overall, the proposed methodology develops a hybrid imagery-dependent advanced early warning system for large-scale solar power plants which are cost-effective and bypass multi-sensor data fusion to attain real-time application.

Accidents during critical maneuvering on roads while overtaking or changing lanes are mainly due to the insufficient generation of the stability matrices of vehicles, including yaw rate. The parameters responsible for stable operation of the vehicle during these driving scenarios may vary, causing improper steering angle input actuation to the vehicle, due to which the desired yaw rate is not generated. To overcome this problem, corrective yaw moments are applied to the yaw dynamics to generate the desired yaw rate and operate the vehicle within the defined stability limit. Therefore - in this paper - to improve the yaw stability of the vehicle, a novel yaw rate gradient-based control approach is proposed for a linear time-varying (LTV) bicycle model. A 2 degrees of freedom bicycle model with the linear approximation for low slip angles in the magic formula tire model is utilized to develop the LTV model. The longitudinal velocity and friction coefficient are chosen as the parameters of interest to be varied during model simulation. Two different critical driving scenarios, including a sine maneuver and a double lane change, are chosen as input actuation with and without corrective yaw moments. The obtained simulation results unveil that, by the application of steering angle with a corrective yaw moment, the yaw stability has effectively been improved by obtaining a feasible adaptive model predictive control solution. Additionally, the root mean square error (RMSE) is calculated to evaluate the performance of the proposed methodology. A RMSE of 0.0768 and 0.0395 for steering without corrective yaw moment (CYM) is decreased to 0.0234 and 0.0214 for sine and double lane change steering input with CYM, respectively. Moreover, the proposed methodology is compared with previous methods and found to have better yaw stability.

The usage of electrical networks which are not connected to the electrical grid allows for the provision of electrical energy to remote places. We refer to these electrical networks as standalone microgrid systems. The design and control for a freestanding microgrid system based on Solar Photo Voltaic (PV) panel's, Permanent Magnet Synchronous Generator (PMSG) and Wind Energy Conversion System (WECS) which forms a Hybrid Renewable Energy System (HRES) are discussed in this study. When the Solar PV panel's output exceeds the load requirements, it is used to recharge the battery system and is drawn from the batteries when the PV panels' output is not sufficient to satisfy the maximum demand. The proposed Adaptive Neuro Fuzzy Inference System (ANFIS) controller safeguards the battery banks charging and discharging conditions. Irrespective of the battery's State of Charge (SOC), an ANFIS controller performs under normal operating configurations with typical settings as 20%< SOC< 80%. A special triggering command signal is sent and it turns-off the Solar PV panel's and wind turbine system when SOC hits 80%. If SOC falls below 20% the inverter turns off and disconnected from the system. Each case is supervised by the controller using MATLAB/Simulation Software.