Parameter Uncertainties Research Articles

Traditional heat diffusion systems are typically regulated using Proportional–Integral–Derivative (PID) controllers. PID controllers still remain the backbone of numerous industrial control applications due to their simplicity, robustness, and efficiency. However, traditional tuning methods—such as Ziegler–Nichols or Cohen–Coon—often exhibit limitations when applied to systems with nonlinear dynamics, time-varying behaviors, or parametric uncertainties. To address these challenges, Fuzzy Logic Controllers (FLC) have emerged as a promising hybrid strategy, by translating quantitative and imprecise linguistic inputs into quantitative control actions, thereby enabling more adaptive and precise regulation. This is achieved through the integration of fuzzy inference mechanisms that dynamically adjust PID gains in response to changing system conditions. This study proposes a fuzzy logic control strategy for a heat diffusion system and conducts a comparative analysis against conventional PID control. The methodology encompasses system modeling, design of the fuzzy inference system, and simulation studies. To improve transient response and address time delays, additional features such as Anti-Windup compensation and a Smith Predictor are integrated into the control scheme. The final validation step involves the introduction of simulated environmental disturbances, including abrupt temperature drops, to evaluate the controller’s robustness. Simulation results demonstrate that the proposed FLC provides superior dynamic performance compared to the conventional PID controller, achieving approximately 5–7% faster rise time and 8–10% lower settling time. The incorporation of an anti-windup mechanism did not yield significant benefits in this application. In contrast, the integration of a Smith Predictor further reduced oscillatory behavior and substantially improved disturbance rejection, tracking accuracy, and adaptability under simulated thermal variations. These results underscore the effectiveness of the FLC in handling systems with time delays and nonlinearities, reinforcing its role as a robust and adaptable control strategy for thermal processes with complex dynamics.

Disseminated Histoplasmosis in People Living With HIV: What Are the Care Costs for Brazil?

Non-intrusive simultaneous calculation of nuclear data sensitivity and uncertainty of core simulator parameters for the full-scale Krško NPP core model

This research proposes a novel cascaded controller attached with filters for regulating the load frequency control of an islanded AC microgrid (MG) system. Significant frequency variations could arise from rapid load fluctuations and uncertainties in renewable energy sources (RESs). Electric vehicles (EVs) have also been connected to this MG system in a residential parking area. A random entrance/exit time of EVs in the parking area and failures in RESs may also result in a significant frequency deviation, which is not yet addressed in any literature. This research primarily examines frequency deviations arising from the random entry and exit of EVs in the parking lot and from RES outages. The controller's gain setting has been adjusted by the extended stochastic coati optimization approach. Using MATLAB/Simulink simulation, a detailed investigation verifies the proposed controller's effectiveness and robustness against various operating situations and internal parametric uncertainties. Frequency stability has been evaluated using Bode plots and eigenvalue analysis. The result has also been validated through hardware simulation using OPAL-RT.

The electrode rolling process critically determines the consistency and performance of lithium-ion batteries, where precise micro-displacement control of the rolling mill servo system governs electrode thickness uniformity and energy density. Conventional PID or model-based controllers often encounter limitations due to parameter uncertainties, nonlinear friction, and external disturbances during high-precision rolling operations. To address these challenges, this paper proposes an adaptive robust control (ARC) strategy that combines parameter adaptation with robust compensation. The proposed controller enables stable and accurate micro-displacement tracking under uncertain conditions, enhancing reliability and robustness. Simulation and experimental studies on a prototype rolling mill validate the effectiveness of the method, confirming its capability to improve system stability and reduce sensitivity to variations and disturbances. The results highlight the potential of ARC to provide a practical and reliable control solution for intelligent manufacturing equipment in next-generation lithium-ion battery production.

In modern communication networks, the Transmission Control Protocol (TCP) plays a vital role in regulating end-to-end data flows. However, network parameter uncertainties and interference introduced by competing UDP flows often lead to congestion collapse, packet loss, and reduced throughput. To address these challenges, this paper proposes a novel event-triggered sliding mode control (ET-SMC) strategy for active queue management in TCP networks. This approach combines the robustness of global sliding mode control with the efficiency of event-triggered mechanisms, significantly reducing redundant control operations while maintaining system stability. Lyapunov stability theory is used to rigorously prove that all signals in the closed-loop system are bounded, effectively avoiding the Zeno phenomenon. Numerical simulations demonstrate that the ET-SMC strategy ensures queue stability, reduces control update frequency, and achieves superior performance compared to traditional PI-based and time-triggered sliding mode controllers.

This paper proposes a novel virtual system-based online tuning strategy for initial gains of adaptive controllers applied to grid-tied converters with LCL filters. The method relies on the implementation of a robust model reference adaptive control (RMRAC) law combined with a full adaptive super-twisting sliding mode action and a disturbance rejection mechanism. To auto-tune the related adaptive gains, a virtual system is excited by a frequency-rich reference signal, ensuring persistent excitation of the regressor and fast convergence of the adaptive gains before the inverter is connected to the physical grid. Once convergence is achieved, the tuned controller is seamlessly transferred to the real plant, where the reference is provided by a grid synchronization unit based on a Kalman filter phase-locked loop. Experimental results demonstrate smooth synchronization, bounded control signals, reduced transient responses, and improved robustness against parametric uncertainties, load disturbances, and grid harmonics, while maintaining acceptable current THD levels. Beyond this case study, the proposed auto-tuning approach can be extended to other RMRAC-based adaptive control schemes, as long as the plant can be properly modeled and simulated, enabling initial gain adjustment without empirical tuning or offline optimization.

Health technology assessments traditionally rely on clinical trial data, leaving uncertainties about real-world cost effectiveness. This post-market economic evaluation used registry data to estimate the real-world cost effectiveness of bortezomib, lenalidomide and dexamethasone (VRd) versus standard of care as it existed prior to VRd funding for newly diagnosed, transplant eligible and ineligible multiple myeloma, as subsidised by the Australian government in 2019. We conducted the economic evaluation from the perspective of the Australian healthcare system using the EpiMAP Myeloma model, a discrete event simulation model powered by risk equations based on data from the Australia & New Zealand Myeloma and Related Diseases Registry. This approach captured individual patient heterogeneity and treatment pathways through up to nine lines of therapy. We assessed differences in quality-adjusted life-years and costs over a lifetime horizon, discounting both at the standard Australian rate of 5% per annum. Costs were valued in 2025 Australian dollars and non-parametric bootstrapping was used to quantify parameter uncertainty. Brtezomib, lenalidomide and dexamethasone was associated with 0.16 incremental quality-adjusted life-years (95% confidence interval [CI] 0.10, 0.21) and A$16K incremental costs (95% CI A$12K, A$120K). Improved response to therapy with VRd was predicted to marginally increase receipt of autologous stem cell transplantation by 1.1% (95% CI 0.6, 1.7), significantly increase receipt of maintenance therapy by 13.8% (95% CI 10.4, 17.3) and marginally decrease the proportion of patients progressing to subsequent lines. None of the bootstrap iterations fell below the traditional A$50K/quality-adjusted life-year threshold. The 2019 decision to universally fund VRd for newly diagnosed multiple myeloma did not result in a cost-effective allocation of healthcare resources when judged against the traditional A$50K/quality-adjusted life-year threshold. Our findings provide nuanced insights into the real-world cost effectiveness of VRd, highlighting how post-market evaluations can inform refinement of funding decisions for complex therapeutic interventions.

In modern financial markets, accurately predicting excess asset returns is crucial for portfolio optimization and risk management. Traditional point estimation methods often struggle to capture the uncertainty and non-linear characteristics of financial markets, whereas Bayesian methods can effectively integrate expert knowledge with market data by incorporating prior information. This paper systematically describes the construction and inference process of a Bayesian linear regression model based on the NormalInverse-Gamma (NIG) conjugate prior for predicting excess asset returns. By formulating the return prediction challenge within a Gaussian linear model framework and assigning normal and inverse-gamma priors to the regression coefficients and noise variance respectively, we derive analytically tractable posterior distributions and obtain Students t-shaped posterior predictive distributions. This framework not only retains the closed-form update benefits of conjugate analysis but also explicitly quantifies parameter and forecast uncertainty.

This research introduces an innovative control method designed for the synchronization and management of chaotic systems through the application of advanced neural network techniques. Specifically, a neural network-based sliding mode control framework is employed to enhance system stability and precision in synchronization tasks. Chaotic systems, particularly when arranged in master–slave configurations, exhibit behaviors that are highly sensitive to initial conditions and parameter variations, making them ideal candidates for the proposed approach. The core of this methodology leverages neural networks to estimate unknown nonlinear functions and dynamically adjust the control coefficients, ensuring high accuracy and adaptability in real-time. One of the key contributions of this study lies in addressing the complex issues of parametric uncertainty, external disturbances, and unmodeled dynamics that typically challenge conventional sliding mode control methods. By incorporating neural networks, the controller is equipped to effectively mitigate these uncertainties, ensuring robust performance even in the face of significant system variability. The adaptive nature of the control system allows for continuous adjustment, resulting in improved synchronization accuracy and faster convergence times. The stability and robustness of the proposed control system are rigorously proven using Lyapunov-based methods. Simulations show that synchronization of nonlinear chaotic systems occurs within 10 s, even under varying conditions. This efficiency demonstrates its practical applications in secure communications, biological systems, and power grids, marking a significant advancement in chaotic system control with broad industrial potential.

The rapid urbanization and industrialization of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) pose a severe challenge for rational land use. This study presents a multi-factor land-use suitability assessment system with economic, social, and environmental dimensions. System reliability and stability are confirmed by a Cronbach’s α coefficient (>0.7). We innovatively integrate the PS-DR-DP model with the Monte Carlo and Markov models. The Markov model analyzes transition probabilities between different land capacity states. The Monte Carlo method quantifies key parameter uncertainties through extensive random sampling, while the Markov chain-Monte Carlo approach dynamically evaluates and predicts land capacity. From 2002 to 2022, overall GBA land-population carrying capacity is stable above 0.6 and keeps rising, reflecting improved regional land capacity and successful coordinated development. However, the forecast results indicate that land capacity will first increase and then decrease between 2023 and 2042, with most cities reaching a peak carrying capacity (S-value approaching or exceeding 2) in 2027. This peak is followed by a projected decline, and by 2042, the overall land capacity may drop to around 0.5, signaling a significant long-term risk of overload. If current development trends continue, the region faces significant long-term risks of declining carrying capacity, particularly if the transition to a sustainable, innovation-driven economy is not managed effectively. This highlights the profound challenge of balancing economic growth, urbanization, and ecological protection. These recommendations offer scientific evidence and decision-making support for sustainable GBA development.

Abstract This paper proposed a novel fractional-order filter-PID controller design approach for Second-Order Plus Time Delay (SOPTD) processes within an Internal Model Control (IMC) based Smith predictor framework, using robustness criterion, i.e., maximum sensitivity (Ms) as a design specification. The proposed controller design is systematically structured into a fractional-order IMC filter and an integer-order PID controller, with a systematic optimized methodology for tuning the fractional filter parameters based on a predefined maximum sensitivity. Adding fractional-order parameters extends the range of accomplishable control dynamics beyond those of traditional integer-filter-PID controllers. The proposed approach enhances flexibility in tuning, performance and robustness by employing fractional-order IMC filter with conventional integer-order counterparts. The proposed approach’s superiority is demonstrated by comparing evaluations with the literature, depicting that it significantly minimizes control effort, reduces the Integral of Absolute Error, and fast settling time. Additionally, robust analysis of parameter uncertainties shows that the controller can maintain the required performance even in significant process fluctuations. The proposed method is experimentally validated on a non-interacting liquid level system.

Abstract This paper presents a high‐frequency sliding mode control (HFSMC) approach that utilizes a nonlinear disturbance observer (NDO) and an improved tracking differentiator (TD) for achieving robust trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) in the presence of lumped disturbances and parameter uncertainties. Firstly, the quadrotor control system is decoupled into an inner‐loop subsystem focused on attitude adjustment and an outer‐loop subsystem dedicated to position control. The hierarchical control mechanism of the inner–outer loop solves the under‐actuation problem. Secondly, NDO is utilized to estimate and counteract lumped disturbances in real time, enhancing the system disturbance rejection capability. Additionally, a high‐frequency switching function is introduced into the reaching law to improve the reaching speed and handle parameter uncertainties, while the improved TD is used to smooth the desired attitude signals and their derivatives, reducing the chattering inherent in sliding mode control. This scheme effectively alleviates control input chattering while enhancing controller robustness, offering a simpler design and stronger disturbance rejection compared to nonsingular fast terminal sliding mode control (NFTSMC). Finally, the stability of the system is proven using globally uniformly ultimately bounded (GUUB) and Lyapunov theory. The effectiveness of the control strategy was validated through simulation experiments.

Abstract This article proposes the design of an adaptive fractional‐order neural network controller based on the backstepping sliding mode control approach for fully actuated 3 degree‐of‐freedom (DOF) autonomous underwater vehicles (AUVs) in the presence of uncertainties and external disturbances. Compared to existing results, the design of an adaptive fractional‐order controller for tracking the desired trajectory of AUV is represented for the first time in this paper. Due to the presence of parametric structural uncertainties, a radial basis function neural network (RBFNN) approximation is employed in combination with adaptive control systems. Furthermore, the robustness of the control law is enhanced against unstructured uncertainties (external disturbances and modeling errors) by employing the ‐modification approach and proving their boundedness. Moreover, the control system stability is analyzed using the fractional Lyapunov approach, demonstrating that all closed‐loop control signals are uniformly bounded. Additionally, the convergence of the Lyapunov variables (tracking error) to a very small neighborhood of zero is proven by adjusting the design parameters of the controller.

This paper investigates the sparse optimal allocation of sensors for detecting sparse leaking emission sources. Because of the non-negativity of emission rates, uncertainty associated with parameters in the forward model, and sparsity of leaking emission sources, the classical linear Gaussian Bayesian inversion setup is limited and no closed-form solutions are available. By incorporating the non-negativity constraints on emission rates, relaxing the Gaussian distributional assumption, and considering the parameter uncertainties associated with the forward model, this paper provides comprehensive investigations, technical details, in-depth discussions and implementation of the optimal sensor allocation problem leveraging a bilevel optimization framework. The upper-level problem determines the optimal sensor locations by minimizing the Integrated Mean Squared Error (IMSE) of the estimated emission rates over uncertain wind conditions, while the lower-level problem solves an inverse problem that estimates the emission rates. Two algorithms, including the repeated Sample Average Approximation (rSAA) and the Stochastic Gradient Descent based bilevel approximation (SBA), are thoroughly investigated. It is shown that the proposed approach can further reduce the IMSE of the estimated emission rates starting from various initial sensor deployment generated by existing approaches. Convergence analysis is performed to obtain the performance guarantee, and numerical investigations show that the proposed approach can allocate sensors according to the parameters and output of the forward model. Computationally efficient code with GPU acceleration is available on GitHub so that the approach readily applicable.

A consistent deposition trend across reservoirs was identified using parametric and nonparametric methods at the 5% significance level, corroborated by elevated Hurst coefficients reflecting long-term temporal persistence. These insights informed the formulation of a Linear Regression Trend Model (LRTM) within a spatio-temporal framework for sediment distribution prediction. The LRTM exhibited strong goodness-of-fit across reservoirs—mimicking natural deposition behaviour—with Nash–Sutcliffe efficiency (0.99–1.00), standard error of estimate (1.53–19.20), and relative error (−0.35% to 3.52%), outperforming benchmark methods: Empirical Area Reduction and Area Increment approaches. To address limitations in sedimentation modelling under parametric uncertainty, a dual-layered framework was developed integrating regression diagnostics, perturbation-based sensitivity analysis, and elevation-specific coherence metrics. Applied across reservoirs with varying sensitivity regimes, it revealed an inverse relationship between persistence and diagnostic weight. This coherence-sensitive extension enhances regime classification and forecasting precision, offering a scalable, empirically defensible tool for long-term sediment prediction across diverse reservoir contexts.

Evaluating the abutment stability of high arch dams is crucial for ensuring their safe operation under complex and evolving environmental conditions. The material properties of the dam and foundation vary significantly over time, and the randomness of these time-varying parameters introduces uncertainties into dam stability analysis. However, traditional methods often rely on deterministic parameters, neglecting the time-varying and uncertain nature of dam and foundation materials, potentially leading to inaccurate or misleading assessments. To address these limitations, a probabilistic evaluation methodology is developed that explicitly incorporates the time-varying characteristics and uncertainties of material parameters into abutment stability analysis. A novel reliability metric, termed the assurance rate, is introduced. It is defined as the cumulative probability that the safety factor exceeds a specified threshold, providing a more realistic and dynamic measure of structural reliability. The method incorporates a time-dependent reliability analysis, considering the probability of failure as a function of time, alongside the assurance rate. To capture the temporal variability of material behavior, an inversion method is established by integrating empirical mode decomposition, response surface methodology, and intelligent optimization algorithms into a unified framework. To further incorporate parameter uncertainty, the inverted parameters and their statistically derived probability distributions are embedded into Monte Carlo simulations. Application to the Dagangshan (DG) arch dam shows that the reliability indicators show clear time-varying characteristics, ranging from 4.62 to 5.01 for the left abutment and 4.70–5.03 for the right. And the probability of exceeding the allowable safety factor ( K = 3.5) consistently remains above 99.99%, confirming the dam’s structural reliability. The proposed method overcomes the limitations of traditional approaches that rely solely on a single safety factor to evaluate abutment stability, offering a more comprehensive and dynamic framework for rational assessment of dam abutment stability.

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance.

Accurate and efficient estimation of Sobol’ sensitivity indices is a cornerstone of variance-based global sensitivity analysis, providing critical insights into how uncertainties in input parameters affect model outputs. This is particularly important for large-scale environmental, engineering, and financial models, where understanding parameter influence is essential for improving model reliability, guiding calibration, and supporting informed decision-making. However, computing Sobol’ indices requires evaluating high-dimensional integrals, presenting significant numerical and computational challenges. In this study, we present a comparative analysis of two of the best available Quasi-Monte Carlo (QMC) techniques: polynomial lattice rules (PLRs) and modified Sobol’ sequences. The performance of both approaches is systematically assessed in terms of performance and accuracy. Extensive numerical experiments demonstrate that the proposed PLR-based framework achieves superior precision for several sensitivity measures, while modified Sobol’ sequences remain competitive for lower-dimensional indices. Our results show that IPLR-α3 outperforms traditional QMC methods in estimating both dominant and weak sensitivity indices, offering a robust framework for high-dimensional models. These findings provide practical guidelines for selecting optimal QMC strategies, contributing to more reliable sensitivity analysis and enhancing the predictive power of complex computational models.

To accurately assess the seismic risk of bridges, this study systematically conducted probabilistic seismic hazard–fragility–risk assessments using a reinforced concrete continuous girder bridge as a case study. First, the CPSHA method from China’s fifth-generation seismic zoning framework was employed to calculate the Peak Ground Acceleration (PGA) with 2%, 10%, and 63% exceedance probabilities over 50 years as 171.16 gal, 98.10 gal, and 28.61 gal, respectively, classifying the site as being with 0.10 g zone (basic intensity VII). Second, by innovatively integrating the Response Surface Method with Monte Carlo simulation, the study efficiently quantified the coupled effects of structural parameter and ground motion uncertainties, a finite element model was established based on OpenSees, and the seismic fragility curves were plotted. Finally, the risk probability of seismic damage was calculated based on the seismic hazard curve method. The results demonstrate that the study area encompasses 46 potential seismic sources according to China’s fifth-generation zoning. The seismic fragility curves clearly show that side piers and their bearings are generally more susceptible to damage than middle piers and their bearings. Over 50 years, the pier risk probabilities for the intact, slight, moderate, severe damage, and collapse are 68.90%, 6.22%, 15.75%, 7.86%, and 1.27%, while the corresponding probabilities of bearing are 3.54%, 44.11%, 25.64%, 7.74%, and 18.97%, indicating significantly higher bearing risks at the moderate damage and collapse levels. The method proposed in this study is applicable to various types of bridges and has high promotion and application value.