Robust Control Research Articles

Reliable biomedical imaging demands rigorous quality control, yet high-throughput microscopy remains prone to diverse artifacts. We present AutoQC-Bench, a software based on a reconstruction-driven diffusion model flagging abnormal images without prior knowledge, and along with a benchmark of 8000 images capturing common quality issues. The software outperforms existing methods, generalizes across modalities, and supports large-scale bioimaging studies. The software and benchmark are openly shared to advance robust microscopy quality control.

Tissue homeostasis requires a precise balance between stem cell self-renewal and differentiation. While fate decisions are known to be closely linked with cell cycle progression, the functional significance of this relationship is unclear. We propose a mechanistic framework to analyse cellular dynamics when cell fate is coupled to cell cycle duration. Our model highlights a unique aspect of cell cycle regulation where mitogens serve as control parameters for a bifurcation governing the G1-S transition. Under competitive feedback from cell-cell interactions, the cell cycle regulatory network fine-tunes near the critical point of this bifurcation. Critical positioning lengthens G1 while amplifying cell-to-cell variability in mitogenic signalling and biochemical states. Such regulation confers significant advantages for controlling cell population dynamics, with alternative topologies enabling rapid tissue growth and repair or efficient mutant rejection. Counter-intuitively, we propose that stem cells may couple prolonged G1 with increased self-renewal propensity to efficiently suppress mis-sensing mutants. Our theory provides a distinct explanation to dynamical and statistical patterns of G1 lengthening and predicts regulatory strategies across development, homeostasis, and ageing.

For unmanned aerial vehicles (UAVs) to operate safely and dependably, accurate state estimation is essential. However, environmental factors that affect measurement quality and sensor biases can impair performance. This paper proposes an Adaptive State-Augmented Kalman Filter (A-SAKF) that integrates two complementary mechanisms: (i) state augmentation for online sensor bias estimation, and (ii) innovation-based adaptive adjustment of measurement covariance. Together, these features enable the filter to maintain robust state estimation performance in the presence of bias errors and uncertain measurement noise conditions. Validation through three simulation scenarios demonstrates the effectiveness of the proposed framework. In Scenario 1, the method correctly estimates and compensates for a 2.0 cm bias in the infrared sensor. In Scenario 2, the velocity estimates eliminate overshoot and reduce settling time by 18% compared to a baseline controller. In Scenario 3, under degraded foggy conditions, the adaptive weighting mechanism recovers LiDAR trust levels within 4.5 s after a 35% drop, thereby preserving altitude tracking accuracy. These results highlight the filter’s capability to address both systematic bias and dynamically varying measurement reliability. By dynamically down-weighting the distorted LiDAR sensor data, the system demonstrates in simulation a steady and precise altitude estimate, showing improved resilience compared to fixed-covariance filters. The proposed filter demonstrates improved state estimation performance for UAVs under uncertain and biased sensor conditions, achieving lower errors than conventional EKF variants in diverse simulation scenarios. The current evidence is limited to simulation-based validation, and future work will extend testing to hardware-in-the-loop and public UAV datasets to further substantiate real-world applicability.

ABSTRACT This work focuses on the trajectory tracking control of robot manipulators subject to model uncertainties and unknown additive disturbances. The controller design makes use of a self‐adjusting adaptive fuzzy logic‐based term, fused with a robust integral of the sign of the error feedback. In the proposed adaptive fuzzy logic framework, means and variances of the membership functions are updated dynamically during each iteration, allowing for a more precise estimation of the parametric uncertainties. The stability of the closed‐loop system and the convergence properties of the states are established via Lyapunov‐based arguments, where asymptotic stability of the joint tracking error is ensured. Numerical simulations have been conducted to further support the theoretical findings.

The nursing robot, equipped with a 6-degree-of-freedom (6-DOF) humanoid manipulator, has been applied in elderly and disabled care to execute complex and random nursing tasks with its advantages in automation and intelligence. Especially, when the nursing robot performs daily care tasks such as serving tea and pouring water, the good trajectory tracking performance of its manipulator is a crucial capability. However, nonlinear coupling, model uncertainty, joint friction, unknown external disturbances, and particularly the fact that manipulator does not satisfy Pieper criterion-are the main challenges, which degrade control performance. Few existing studies have simultaneously addressed all these issues to improve the control accuracy of the manipulator. Therefore, to achieve the good tracking performance for manipulator, a robust control method combining sliding mode control (SMC), radial basis function neural network (RBFNN), and nonlinear disturbance observer (NDO) is proposed. An improved Jacobian-based gradient descent method solves inverse kinematics, with the improved gradient descent driven inverse kinematics (IGDIK) module ensuring accuracy; RBFNN compensates for model uncertainty; NDO handles disturbances and friction. Simulations and experiments demonstrate enhanced trajectory tracking accuracy and stability, validating its effectiveness for the target manipulator.

Abstract Cooperative rendezvous of a fixed-wing unmanned aerial vehicle (UAV) and a moving platform is a challenging and significant issue, where environmental disturbances, computational load, and time-varying rendezvous points remain the prevalent challenges. Aiming at the stabilizing control of the fixed-wing UAV under the disturbed environment, a Gaussian process (GP)-based robust model predictive control with Laguerre functions and shrinking horizon strategy is proposed. Firstly, a new online Gaussian process prediction method is developed to predict the future trajectory of the moving platform. Then, a robust control scheme is designed to compensate for the effect of bounded disturbances on the UAV. Furthermore, to decrease the computational load of solving the optimization problem in real-time, a novel prediction horizon update strategy and Laguerre functions are developed. Finally, the reliability and effectiveness of the proposed algorithm are verified by the joint experiment results. Compared with the existing approaches, the proposed method achieves a 30.33% reduction in computational load.

Thruster-driven autonomous underwater vehicles (AUVs) are designed to maneuver at low speeds and carry out dynamic station keeping operations. In this paper, a dynamic model of a thruster-actuated axisymmetric AUV operating at low speed is presented along with a four-quadrant thruster model. The hydrodynamics of the AUV were estimated using Semi-empirical formulation and computational fluid dynamics simulations. To simulate realistic thruster forces, a four-quadrant thruster model is integrated into the AUV’s maneuvering dynamics. A robust control strategy based on the uncertainty and disturbance estimation (UDE) method is formulated for depth, pitch, and heading control of a thruster-driven AUV. UDE control consists of nominal feedback plus the estimator which utilizes a low-pass filter with a proper bandwidth to estimate the unmodeled dynamics and unknown external disturbances. We introduce an optimization framework for tuning the filter time constant value to improve the performance and energy efficiency. The effectiveness of the proposed control system is demonstrated through numerical simulations considering model uncertainty, thruster nonlinear model, input saturation, external disturbances, and measurement noises. The proposed UDE control design is compared with Time Delay Estimation (TDE) control and Sliding Mode Control (SMC), and implementation issues are addressed. The comparative analysis shows that the UDE control provides enhanced robustness and performance across various maneuvering scenarios, making it a viable solution for the AUV motion control. Experiments were conducted on a testbed AUV for trajectory tracking in depth and yaw degrees of freedom to demonstrate practical realization of the UDE control. The experimental results confirm the effectiveness of the proposed control strategy in presence of system uncertainties.

This study investigated internal financial control policy, and financial budget implementation on financial accountability within Kenyan county governments, with a specific focus on Murang'a County. The research was anchored on Agency Theory and Systems Theory, which provided the framework for analyzing the principal-agent dynamics and the interconnectedness of control components within the county's financial systems. The study employed a cross-sectional research design and collected primary data from 80 respondents within the Department of Finance and Economic Planning of Murang'a County, selected using a stratified sampling technique. Data was gathered via structured questionnaires and analyzed using both descriptive and inferential statistics, including correlation and multiple linear regression, with the aid of the Statistical Package for the Social Sciences (SPSS). The key findings revealed significant positive relationships between financial accountability and each of the following variables: internal financial control policies, and financial budget implementation, Regression analysis indicated that these four variables collectively accounted for 66.3% of the variation in financial accountability, The study concludes that robust internal control systems are fundamental to enhancing financial accountability in devolved governments. Consequently, it is recommended that county governments should prioritize strengthening budget execution processes, intensifying fraud awareness campaigns, and reinforcing internal control policies and risk assessment mechanisms to ensure greater fiscal transparency and stewardship of public funds.

Abstract Integrated power systems (IPS) aboard electrified ships require energy management strategies that ensure safe, autonomous operation. Next-generation platforms are expected to make such decisions with minimal human oversight. However, the complex, multidomain, multitimescale dynamics of IPS—combined with high ramp rate loads like electronic warfare systems—pose significant challenges. Additionally, these systems often face uncertain, time-varying, mission-specific constraints that create nonconvex feasible regions, limiting the effectiveness of conventional energy management approaches. This work presents a hierarchical, two-stage framework for safe and adaptive energy management in shipboard IPS. At the upper level, a sampling-based rapidly exploring random tree (RRT) algorithm identifies feasible long-term power and energy trajectories within nonconvex constraint spaces. At the lower level, a robust model predictive control (MPC) scheme ensures accurate trajectory tracking with bounded error, accommodating the dynamics of major components while maintaining constraint satisfaction. The framework is demonstrated on a two-zone IPS model with a high ramp rate load. Simulation results show the proposed planner efficiently generates feasible mission plans that adapt to evolving constraints, while the MPC controller ensures reliable tracking and constraint adherence. By bridging long-term planning with short-term control, this architecture enables safe, flexible, and autonomous operation of complex shipboard power systems. It addresses key limitations of existing strategies in managing nonconvex constraints and dynamic mission contexts, making it well-suited for resilient autonomy in future maritime platforms.

As a cornerstone of net-zero emission strategies and Gen-IV nuclear technologies, the commercialization of Lead-based Fast Reactor (LFR) is impeded by lead-bismuth eutectic (LBE)-induced cladding corrosion. Although active oxygen control demonstrates promise under laboratory conditions, its long-term effectiveness under realistic conditions remains uncertain due to multiphysics interactions and prohibitive computational costs. To address this challenge, we introduce a high-fidelity and high-accuracy surrogate model, K2K (Kriging to Kolmogorov-Arnold Networks) with a predictor-corrector structure combined with a gradient penalty operator, thereby effectively enhancing model accuracy while mitigating non-physical extrapolations. The application of K2K enables the identification and localization of cladding failure mechanisms. Leveraging these insights, we develop a robust and comprehensive oxygen concentration control strategy, encompassing feasible concentration ranges and optimal values to support the safe, reliable, and long-term operation of LFR. Finally, we explore the potential of K2K model for analyzing multiphysics behaviors in energy systems.

This paper presents a safe landing methodology for Unmanned Aerial Vehicles (UAVs) when the GPS-based navigation system fails or is denied or unavailable. The approach relies on the estimation of a flat landing area when landing is required in an unknown area. The proposed system is based on a lightweight computer vision algorithm that enables real-time identification of suitable landing zones using a depth camera and an onboard companion computer. Analysis of small, spatially distributed areas to calculate the mean altitude and standard deviation across regions enables reliable selection of flat surfaces. A robust landing control algorithm is activated when the area meets strict flatness conditions for a continuous period. Real-time experiments confirmed the effectiveness of this approach under disturbances, showing reliable detection of the safe zone and the robustness of the proposed control algorithm in outdoor environments.

This paper focuses on the design of dynamic output feedback Robust Model Predictive Control (RMPC) for polytopic uncertain systems incorporating full-duplex relays (FD relays). In light of the transmission capabilities of sensors, an FD relay system transmitting signals at specific power levels is utilised to accomplish remote signal transmission and mitigate prevalent channel fading issues in networked transmission systems. Leveraging the mathematical expectation of the quadratic function within traditional Model Predictive Control (MPC), this paper formulates a novel infinite-horizon objective function, considering both the polytopic characteristics of system parameters and the self-interference induced by the relay. Singular Value Decomposition (SVD) is applied to address the unknown variable coupling problem arising from unmeasurable states in the system. An auxiliary optimisation problem for designing the necessary controller was studied, and sufficient conditions for ensuring the mean-square stability of the system were derived. Ultimately, the effectiveness and superiority of the proposed strategy were verified through two examples.

Six Sigma Optimized Lipid-based Delivery System for Ibuprofen: Enhanced Solubility, Robust Process Control, and Bioequivalence

This paper examines the interrelationship between Corporate Governance (CG), Internal Control System (ICS), and Organizational Performance (OP), with a particular focus on the effectiveness of the ICS in relation to the quality of its components. Drawing on recent literature and empirical evidence, the study demonstrates that strong governance frameworks—characterized by board independence, effective audit committees, and proactive risk management—are closely linked to robust internal control environments. Together, these mechanisms enhance transparency, reduce risks, and foster stakeholder trust. The analysis further highlights that governance and internal control are evolving beyond compliance, increasingly serving as strategic levers for creating sustainable value. The findings underscore important implications for practitioners and policymakers. Organizations are encouraged to strengthen internal controls, invest in audit and risk management capacity, and embed ethical and sustainability considerations into governance structures. Regulators, in turn, should support frameworks that promote both accountability and innovation. Overall, the study contributes to a deeper understanding of how governance and control mechanisms can secure organizational resilience and drive long-term performance in a rapidly changing business environment.

In the field of rotating machinery, such as marine propulsion shafting, magnetic bearing-supported propulsion systems have garnered significant attention due to their non-mechanical contact advantages. To address the problem that the design of magnetic bearing controllers, based on theoretical models, neglects the dynamic characteristics of practical components like power amplifiers and displacement sensors, making it difficult to achieve ideal performance in practical applications, this paper proposes a control method for Hybrid Magnetic Bearings (HMBs) that combines a time-domain identification model with robust control. The method first models the power amplifier, HMB, and displacement sensor as an equivalent single system and obtains its high-precision transfer function model by performing system identification on its time-domain data using the least squares method. Based on this foundation, a PID controller is designed using the loop-shaping method to enhance the system’s robustness and control performance. Both simulations and experiments on an HMB test rig confirmed the controller’s effectiveness. The system showed excellent levitation, dynamic stability, and disturbance rejection, with experimental results closely matching simulations. The experimental results are consistent with the simulation results. This method provides a practical and feasible technical approach for enhancing the control performance of magnetic bearing-supported propulsion shafting.

ABSTRACT Inverted pendulum is an example of a classical problem in control theory that has been widely used for investigating control algorithms like state feedback, artificial neural networks, fuzzy control, and robust control. The piecewise Affine‐fuzzy (PWA‐Fuzzy) approximation of an inverted pendulum on a cart has been investigated in previous literature. The main drawbacks of PWA approximation, i.e., discontinuity in control signal and chattering between different regions, are addressed by the PWA‐Fuzzy model. In this paper, with the aim of stabilizing the system in the open‐loop unstable equilibrium point, a patched‐fuzzy state feedback (PFSF) controller is designed as an improved form of the conventional state feedback controller for the PWA‐Fuzzy model of an inverted pendulum on a cart. Because of the intention to implement the mentioned controller for a real plant, the identification of PWA‐Fuzzy model parameters by the linear least squares method based on the numerical method is presented. Furthermore, the implementation of the mentioned controller using two different techniques, including analog and digital circuits, is presented. Finally, in order to evaluate the proposed method, the simulation and experimental results are compared.

ABSTRACT In this work, we propose a learning framework that can synthesize a robust control Lyapunov function and a stable state‐feedback controller for general nonlinear systems with quadratically bounded disturbances. While theoretically appealing, traditional approaches based on control Lyapunov functions face significant practical challenges due to unmodeled disturbances and estimation errors. To address this issue, our work employs a learning‐based approach that combines Lyapunov analysis with machine‐learning techniques. Additionally, we propose a random sampling‐based method to verify the validity of the Lyapunov condition. Furthermore, we extend our approach to synthesize stable controllers for control‐affine systems with unstructured uncertainties and actively learn the uncertainty from system trajectories. Finally, we show the effectiveness of our approach on four examples: the inverted pendulum, a third‐order strict‐feedback system, the cart‐pole system, and the 2D quadrotor system.

ABSTRACT This paper addresses the problem of adaptive robust attitude control for fixed‐wing unmanned aerial vehicles subject to actuator faults, input dead zones, matched disturbances, and mismatched disturbances. An adaptive sliding mode disturbance observer is developed to estimate unknown mismatched disturbances and reduce conservatism in gain selection. Based on the reconstructed mismatched disturbances, a modified sliding surface is constructed, upon which a barrier function‐based adaptive sliding mode controller is designed to compensate for lumped matched uncertainties and achieve accurate attitude tracking. A notable feature of the proposed controller is its ability to predefine the convergence neighborhood of the sliding variable while eliminating discontinuous terms, effectively mitigating chattering. Theoretical analysis establishes system stability, and numerical simulations validate the effectiveness of the controller.

In this multicenter, retrospective study involving 62 patients, we investigated whether switching from entecavir (ETV) or tenofovir disoproxil fumarate (TDF) to tenofovir alafenamide (TAF) represents a superior treatment strategy for patients with chronic hepatitis B (CHB) experiencing low-level viremia (LLV). The study determined that TAF significantly improved both virological and biochemical outcomes. At 48 weeks, the complete virological response (CVR) rate was 77.8% for those who switched from ETV and 81.8% for those who switched from TDF, with Hepatitis B virus deoxyribonucleic acid (HBV DNA) negativity reaching 81% by month 12. Additionally, significant normalization of liver enzymes, albumin, and platelet counts was observed across the cohort. While the switch from TDF was associated with a significant increase in triglycerides and high-density lipoprotein (HDL) and a decrease in estimated glomerular filtration rate (eGFR), no such changes were detected in the ETV group. This evidence suggests that TAF provides robust virological control in LLV patients and is associated with favorable biochemical improvements. However, due to the study’s limitations, the strong assertion that TAF promotes the regression of liver fibrosis and reduces the risk of hepatocellular carcinoma (HCC) must be interpreted with caution.

Approximately 2.75 million years ago, the Turkana Basin in Kenya experienced environmental changes, including increased aridity and environmental variability. Namorotukunan is a newly discovered archaeological site which provides a window into hominin behavioral adaptations. This site lies within the upper Tulu Bor and lower Burgi members of the Koobi Fora Formation (Marsabit District, Kenya), presently a poorly understood time interval due to large-scale erosional events. Moreover, this locale represents the earliest known evidence of Oldowan technology within the Koobi Fora Formation. Oldowan sites, older than 2.6 million years ago, are rare, and these typically represent insights from narrow windows of time. In contrast, Namorotukunan provides evidence of tool-making behaviors spanning hundreds of thousands of years, offering a unique temporal perspective on technological stability. The site comprises three distinct archaeological horizons spanning approximately 300,000 years (2.75 − 2.44 Ma). Our findings suggest continuity in tool-making practices over time, with evidence of systematic selection of rock types. Geological descriptions and chronological data, provide robust age control and contextualize the archaeological finds. We employ multiple paleoenvironmental proxies, to reconstruct past ecological conditions. Our study highlights the interplay between environmental shifts and technological innovations, shedding light on pivotal factors in the trajectory of human evolution.