Parameter Uncertainties Research Articles

Solving the Robust Shortest Path Problem with Multimodal Transportation

This paper explores the challenges of finding robust shortest paths in multimodal transportation networks. With the increasing complexity and uncertainties in modern transportation systems, developing efficient and reliable routing strategies that can adapt to various disruptions and modal changes is essential. By incorporating practical constraints in parameter uncertainty, this paper establishes a robust shortest path mixed-integer programming model based on a multimodal transportation network under transportation time uncertainty. To solve robust shortest path problems with multimodal transportation, we propose a modified Dijkstra algorithm that integrates parameter uncertainty with multimodal transportation. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments on test sets of different scales and a comparison of the runtime using a commercial solver. The experimental results on the multimodal transportation networks demonstrate the effectiveness of our approach in providing robust and efficient routing solutions. The results demonstrate that the proposed method can generate optimal solutions to the robust shortest path problem in multimodal transportation under time uncertainty and has practical significance.

Constraining non-unitary neutrino mixing using matter effects in atmospheric neutrinos at INO-ICAL

The mass-induced neutrino oscillation is a well established phenomenon that is based on the unitary mixing among three light active neutrinos. Remarkable precision on neutrino mixing parameters over the last decade or so has opened up the prospects for testing the possible non-unitarity of the standard 3ν mixing matrix, which may arise in the seesaw extensions of the Standard Model due to the admixture of three light active neutrinos with heavy isosinglet neutrinos. Because of this non-unitary neutrino mixing (NUNM), the oscillation probabilities among the three active neutrinos would be altered as compared to the probabilities obtained assuming a unitary 3ν mixing matrix. In such a NUNM scenario, neutrinos can experience an additional matter effect due to the neutral current interactions with the ambient neutrons. Atmospheric neutrinos having access to a wide range of energies and baselines can experience a significant modifications in Earth’s matter effect due to NUNM. In this paper, we study in detail how the NUNM parameter α32 affects the muon neutrino and antineutrino survival probabilities in a different way. Then, we place a comparable and complementary constraint on α32 in a model independent fashion using the proposed 50 kt magnetized Iron Calorimeter (ICAL) detector under the India-based Neutrino Observatory (INO) project, which can efficiently detect the atmospheric νμ and ν¯μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\overline{\ u}}_{\\mu } $$\\end{document} separately in the multi-GeV energy range. Further, we discuss the advantage of charge identification capability of ICAL and the impact of uncertainties in oscillation parameters while constraining α32. We also compare the α32 sensitivity of ICAL with that of future long-baseline experiments DUNE and T2HK in isolation and combination.

Design and Experimental Validation of an Adaptive Multi-Layer Neural Network Observer-Based Fast Terminal Sliding Mode Control for Quadrotor System

This study considers the numerical design and practical implementation of a new multi-layer neural network observer-based control design technique for unmanned aerial vehicles systems. Initially, an adaptive multi-layer neural network-based Luenberger observer is designed for state estimation by employing a modified back-propagation algorithm. The proposed observer’s adaptive nature aids in mitigating the impact of noise, disturbance, and parameter variations, which are usually not considered by conventional observers. Based on the observed states, a nonlinear dynamic inversion-based fast terminal sliding mode controller is designed to attain the desired attitude and position tracking control. This is done by employing a two-loop control structure. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme in the presence of disturbance, parameter uncertainty, and noise. The numerical results are compared with current approaches, demonstrating the superiority of the proposed method. In order to assess the practical effectiveness of the proposed method, hardware-in-loop simulations are conducted by utilizing a Pixhawk 6X flight controller that interfaces with the mission planner software. Finally, experiments are conducted on a real F450 quadrotor in a secured laboratory environment, demonstrating stability and good tracking performance of the proposed MLNN observer-based SMC control scheme.

Unveiling the Pivotal Influence of Sea Spray Heat Fluxes on Hurricane Rapid Intensification

Abstract Predicting rapid hurricane intensification remains a challenge, partially due to neglected factors like sea spray-mediated heat flux. To shed light on the specific roles of spray-mediated sensible and latent heat fluxes, we conducted sensitivity experiments with heat flux parameterizations. Our results demonstrate that the inclusion and variation of the spray-mediated sensible heat significantly reduce model errors when compared against dropsonde data. These findings uniquely quantify the pivotal role of spray-mediated sensible heat flux in accurately predicting hurricane rapid intensification compared to previous studies. Without sea spray processes, ocean-coupled model simulations could not reproduce the steep intensification rate observed in multi-case studies of four high-impact hurricanes. This study also highlights that dropsonde data, as well as directly observed flux, is useful in minimizing uncertainty in the flux parameterization used for hurricane simulations. In this paper, we show how spray-mediated heat flux affects hurricane energetics through turbulent heat exchange and subsequent humid air inflow through primary and secondary circulations. Our findings provide new insights into the transformative role of sea spray in turbulent heat exchange that drives rapid hurricane intensification.

Tracking performance and power quality enhancement of DFIG based wind turbine using robust integral terminal sliding mode control

The aim of this work is to develop a robust Integral Terminal Sliding Mode Control (ITSMC) strategy for a Doubly Fed Induction Generator (DFIG) wind turbine (WT) operating in challenging conditions. This controller is designed to manage the system's rotor side converter (RSC) and grid side converter (GSC), ensuring maximum power production, improved power quality, and accurate tracking of system state variables, even in the face of fluctuating wind conditions, parameter uncertainties, and external disturbances. The stability of the proposed controller is assured using the Lyapunov theory. The performance of ITSMC is compared with that of the conventional sliding mode controller (SMC) and backstepping controller through simulation tests, using the integral absolute error (IAE) as a performance metric. The results highlight the superiority of ITSMC, demonstrating its ability to precisely track reference values, eliminate static errors, and minimize chattering. ITSMC consistently achieves the lowest IAE for all tracked state variables and across all test scenarios. A comparative study analyzing the Total Harmonic Distortion (THD) of the proposed controller versus SMC, the backstepping controller, and other similar works in the literature shows a marked improvement in the THD of stator and filter currents by at least 24.53 % and 7.96 %, respectively. These findings underscore the controller's capability to enhance the power quality delivered to the grid.

A lie group PMP approach for optimal stabilization and tracking control of autonomous underwater vehicles

AbstractIn this research, we explore a finite horizon optimal stabilization and tracking control scheme for the dynamical model of a 6‐DOF Autonomous Underwater Vehicle (AUV). Dynamical equations of the AUV are represented in a Lie group () framework, encompassing both translational and rotational motions. Utilizing a left Lie group action on , we define error function for velocities via a right transport map to effectively address optimal trajectory tracking. The optimal control objective is formulated as a trade‐off problem, aiming to minimize both errors and control effort simultaneously. Left action on yields the left trivialized Hamiltonian function from which the concomitant state and costate dynamical equations are derived using Pontryagin's Minimum Principle (PMP). Consequently, the resulting two‐point boundary value problem is solved to obtain optimal trajectories. We demonstrate the optimality of the resulting solution obtained from the derived control law. For ensuring boundedness in the presence of small disturbances, this study incorporates the effects of internal parametric uncertainties associated with added mass and inertia components, along with the influence of external disturbances induced by ocean currents. Through simulation validations, we confirm the alignment of our results with the theoretical developments, demonstrating that the proposed control law effectively mitigates both parametric uncertainties and ocean current disturbances.

Uncertainty Propagation of Monte Carlo Cross-Section Generation for Graphite-Moderated Pebble Bed Reactors

Monte Carlo codes have become increasingly popular for generating homogenized few-group cross-section data, especially for advanced reactor designs that have complex geometries and nontraditional compositions. However, the stochastic nature of Monte Carlo processes has the potential to introduce additional statistical uncertainties in the overall uncertainty in the prediction of core behavior. The work performed in this research quantified the additional uncertainty introduced by the use of Monte Carlo multigroup cross sections into the analysis of graphite-moderated pebble bed reactors. In this research, the objective was achieved by performing uncertainty quantification for the key output parameters in deterministic steady-state and transient safety calculations. The results show that when the homogenized multigroup cross sections are generated with a sufficient number of neutron histories in the Monte Carlo calculation, the uncertainties in the subsequent deterministic simulations caused by the Monte Carlo cross-section uncertainty are negligible compared to the contributions from the uncertainties of other input parameters.

Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications.

Diagnosis of DC-DC Converter Semiconductor Faults Based on the Second-Order Derivative of the Converter Input Current

The deployment of DC microgrids presents an excellent opportunity to enhance energy efficiency in buildings. Among other components, DC-DC converters play a crucial role in ensuring the interface between the microgrid and its energy generation, storage, and consumption components. However, the reliability of these energy conversion solutions remains somewhat limited. Adopting strategies for accurate monitoring and diagnostics of the DC-DC converter topologies that best suit each equipment’s constraints is, therefore, of critical relevance. Solutions available in the literature concerning fault diagnostics on DC-DC converters do not consider the application of such converters in the household and tertiary sector environments and associated constraints—cost effectiveness, robustness against parameter uncertainty of the converter model, and obviation of the need for historical data. On this basis, this paper presents a simple and effective fault diagnostic strategy, based on a time-domain analysis of the second-order derivative of the converter input current. Its implementation is straightforward and can be integrated into the pre-installed converter control unit. The unique features of the fault diagnostic algorithm show good results for a broad range of operating points, along with insensitivity against load transients and supply voltage fluctuations.

Effect of Uncertainty in Geotechnical and Flow Parameters on Landslide-induced Force for Building Response Analysis Under Flow-type Landslides Using Uncoupled Approach

Effect of Uncertainty in Geotechnical and Flow Parameters on Landslide-induced Force for Building Response Analysis Under Flow-type Landslides Using Uncoupled Approach

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

AFFINE MODELS WITH PATH-DEPENDENCE UNDER PARAMETER UNCERTAINTY AND THEIR APPLICATION IN FINANCE

In this paper, we consider one-dimensional generalized affine processes under the paradigm of Knightian uncertainty (the so-called nonlinear generalized affine models). This extends and generalizes previous results in Fadina et al. (2019) and Lütkebohmert et al. (2022). In particular, we study the case when the payoff is allowed to depend on the path, like it is the case for barrier options or Asian options. To this end, we develop the path-dependent setting for the value function relying on functional Itô calculus. We establish a dynamic programming principle which then leads to a functional nonlinear Kolmogorov equation describing the evolution of the value function. While for Asian options, the valuation can be traced back to PDE methods, this is no longer possible for more complicated payoffs like barrier options. To handle such payoffs in an efficient manner, we approximate the functional derivatives with deep neural networks and show that the numerical valuation under parameter uncertainty is highly tractable. Finally, we consider the application to structural modelling of credit and counterparty risk, where both parameter uncertainty and path-dependence are crucial and the approach proposed here opens the door to efficient numerical methods in this field.

A Comparative Study of Single-Chain and Multi-Chain MCMC Algorithms for Bayesian Model Updating-Based Structural Damage Detection

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.

River quality management: Integrating uncertainty, failure probability, and assimilation capacity

Managing river water quality is challenging due to uncertainties in hydraulic and hydrologic parameters. This study integrates the symmetric exponential function (SEF) approach for solving the advection-dispersion equation with the Monte Carlo method in MATLAB. This combination allows us to explore the river's assimilation capacity and the failure probability (Pf) of maintaining desired water quality standards. Here, Pf represents the likelihood of pollutant concentrations exceeding acceptable limits under varying river conditions. A key contribution of this study is the introduction of a novel equation, developed using the Genetic Programming (GP) soft computing tool, to calculate assimilation capacity considering the failure probability of water quality provision. This equation provides a valuable tool for risk assessment in water resource management by quantifying pollutant assimilation dynamics. Its robustness is validated through high Coefficient of Determination (R2) and Overall Index (OI) values near 1, along with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study identifies critical river characteristics, such as flow velocity and pollutant load, significantly influencing the reliability index (β). By outlining how adjustments in these parameters can achieve a target reliability index (β=4.526), our study offers a practical approach to safeguarding river ecosystems. For example, increasing flow velocity by 76 % can shift the river from a safe state (Pf=3×10−5) to a hazardous state (Pf=1), while a 44 % decrease in velocity allows for 57 % more pollutant assimilation. These findings highlight the importance of flow control as a cost-effective strategy for mitigating high pollutant concentrations and ensuring sustainable water quality management. By integrating numerical approaches with reliability sampling methods and soft computing techniques, this study enhances understanding of river system dynamics and supports informed decision-making for protecting water resources.

Techno-economic analysis of the direct solar conversion of carbon dioxide into renewable fuels

Direct conversion of carbon dioxide (CO2) using sunlight into commercially viable renewable fuels will be one key solution for decarbonization and storing renewable solar energy. However, the direct conversion of CO2 using sunlight faces uphill challenges, especially the techno-economic viability (TEV), as the produced fuels must compete with fossil fuels or at least with fossil fuel alternatives, which are cheap. This work proposes an innovative structure design for photoelectrochemical reduction of CO2 into renewable fuels and performs a detailed techno-economic analysis (TEA) using a generalized gross margin (GM) model. The proposed structure uses low-cost and earth-abundant crystalline silicon-based photoanode with triangle nano-strips on a thin-film substrate to efficiently convert solar energy into renewable fuels. The proposed structure shows 20.01% of power-conversion efficiency (ηPEC). The techno-economic GM model considers all relevant cost parameters to assess the TEV of the produced hydrogen (H2) and hydrocarbon (C1–C3) fuels from CO2 reduction processes. The TEV of the produced renewable fuels is analyzed and presented against critical device parameters, such as the photocurrent density (J), catalyst’s durability (tdur), Faradaic efficiency (FE), and catalyst cost (Ccat). We also analyzed how sensitively the TEV of the produced fuels depends on the uncertainty of different cost parameters assuming base-, worst-, and optimistic-case scenarios. We find that carbon monoxide (CO) and formic acid (HCOOH) fuels will be commercially viable in the base case, while H2 and propanol (C3H7OH) will be only in the optimistic case.

Model simplification to simulate groundwater recharge from a perched gravel-bed river

Gravel-bed rivers are an important source of groundwater recharge in some regions of the world. Their interactions with groundwater are complex and highly variable in space and time, with considerable water storage in the riverbed sediments. In losing river sections, where most of the groundwater recharge occurs, the river can be separated from the regional groundwater system by an unsaturated zone (i.e., perched). The complexity of groundwater–surface water interactions in these environments calls for the use of 3D fully integrated hydrological models to represent them, but their computational intensity limits their practicality for parameter inference, uncertainty quantification and regional scale problems. On the other hand, the simple groundwater–surface water exchange functions currently implemented in regional scale groundwater models are not suited to represent complex gravel-bed river systems such as braided rivers. There is therefore a need for developing groundwater–surface water exchange functions tailored to gravel-bed rivers that can be used in regional scale models.To address this issue, we developed a model simplification framework that combines a 3D integrated surface and subsurface hydrological model, a 2D cross-sectional river-aquifer model and a 1D conductance-based analytical model. We aim at broadly simplifying the 3D model while ensuring the appropriate simulation of groundwater recharge. We demonstrate our modelling approach on the Selwyn River (New Zealand) using piezometric data and groundwater recharge estimates derived from field observations and satellite imagery.Our results indicates that groundwater recharge from this river can be simulated using a simple 1D analytical model, which can easily be implemented in regional groundwater models (e.g., MODFLOW models). However, to represent properly the time variability of groundwater recharge, it is essential to use the groundwater level in the shallow aquifer associated with the river as input to the regional groundwater model. Our approach is generally transferable to other gravel-bed rivers but requires some observations of river losses for proper calibration.

Robust blade pitch control of semi-submersible floating offshore wind turbines based on the modified super-twisting sliding-mode algorithm

In this paper, a novel robust collective blade pitch controller (CBPC) is proposed for the semi-submersible floating offshore wind turbine (FOWT) above the rated wind speed. The proposed CBPC is based on the modified super-twisting sliding-mode (MSTSM) algorithm. Firstly, based on a control-oriented model of the semi-submersible FOWT, the dynamics of the rotor speed and the platform pitch rate considering the lumped disturbances, which consist of external disturbances, parametric uncertainties and unmodeled dynamics, are derived. Afterward, the MSTSM algorithm-based CBPC (MSTSM-CBPC) is designed for regulating the generator power to its rated value and reducing the platform pitching motion. Comparative co-simulation tests among the gain-scheduling proportional-integral CBPC, the standard STSM algorithm-based CBPC and the proposed MSTSM-CBPC are performed. Simulation results validate the effectiveness and the superiority of the proposed MSTSM-CBPC.

Lower-order linear active disturbance rejection control for electro-hydraulic servo system based on structural invariance compensation

High observer gain is the key to ensuring the full-order active disturbance rejection control (ADRC) performance for electro-hydraulic servo (EHS) systems. However, the noise sensitivity and output time delay limit the observer gain. In this paper, a lower-order linear ADRC is proposed with lower gain and uncompromised disturbance rejection ability. First, the internal nonlinear dynamics and external unknown disturbances of the EHS system are eliminated by introducing the structural invariance compensation (SIC). Then, the original EHS model is transformed into a one-order integral chain structure. A one-order linear ADRC, solving the compensation error of SIC caused by model parameter uncertainty, could be directly developed based on the equivalent model. In addition, a practical and novel b0 tuning method and frequency-domain stability analysis technology are developed. Extensive comparative experiments are carried out to illustrate the effectiveness of the proposed method.

The direction of core solidification in asteroids: Implications for dynamo generation

Paleomagnetic studies of meteorites over the past two decades have revealed that the cores of multiple meteorite parent bodies, including those of certain chondritic groups, generated dynamo fields as they crystallised. However, uncertainties in the direction and mode of core solidification in asteroid-sized bodies have meant using the timings and durations of these fields to constrain parent body properties, such as size, is challenging. Here, we use updated equations of state and liquidus relationships for Fe-FeS liquids at low pressures to calculate the locations at which solids form in these cores. We perform these calculations for core-mantle boundary (CMB) pressures from 0–2 GPa, and Fe-FeS liquid concentrations on the iron-rich side of the eutectic, as well as two values of iron thermal expansivity that cover the measured uncertainties in this parameter, and adiabatic and conductive cooling of these cores. We predict inward core crystallisation from the CMB in asteroids due to their low < 0.5 GPa pressures regardless of the uncertainties in other key core parameters. However, due to low internal pressures in these cores, remelting of any iron snow, as proposed to generate Ganymede’s present-day field, may be unlikely as the cores are approximately isothermal. Therefore a different mode of inward core solidification is possibly required to explain compositionally-driven dynamo action in asteroids. Additionally, we identify possible regimes at higher > 0.6 − 2 GPa pressures in which crystallisation can occur concurrently at the CMB and the centre.

DUAL LOOP VOLTAGE DROOP REGULATED CONTROLLER FOR DC MICROGRID USING HYBRID PSO AND GGO ALGORITHMS

Abstract DC microgrids are effectively employed in distribution network integration with renewable energy sources. The droop control technique is commonly utilized for DC microgrid regulation during load sharing. It minimizes the potential instability caused by disturbances such as input voltage changes, uncertainty parameters, and constant power load (CPL). Nevertheless, conventional droop control is inadequate in achieving precise current and satisfactory voltage regulation distribution for load sharing. The proposed approach comprises two separate loops for the DC bus voltage regulation and load current distribution, delivering a CVL and CPL to overcome the single optimization voltage controller technique. The primary loop uses the PSO algorithm to precisely manage the current load sharing among two parallel converters. Due to this the instability problems arising from disturbances, and it mitigated by the proposed topology. The multiobjective optimization technique provides notable resilience, rapid dynamic response, and robust stability over considerable variations in load. The secondary loop utilises the GGO algorithm to optimize the parameter to minimize the DC bus voltage regulation, voltage management, reasonable distribution of load power, and suitable reliability. The performance of the voltage regulation enhance &amp; settling time for output voltage has been mitigated more than 31ms times through the proposed controller compared to conventional controller under the variation of CVL, CPL, and input voltage disturbance demonstrated in simulation results.