Synchronization analysis of tri-motor vibration system considering an improved Active Disturbance Rejection Control algorithm

A multi-stage-based discretized jumping control algorithm of wheel-legged jumping robots

The Active Tuned Mass Damper (ATMD) system minimizes seismic vibrations in asymmetric floor buildings but faces challenges in precise frequency matching and optimal control under dynamic conditions. To address these issues, a novel Born Frequency Distribution-Ebola Neural Network Controller with Frequency Cyclic Linear Analysis is proposed. The ATMD design struggles with aligning damper and building frequencies during dynamic shifts, leading to mode coupling. So, a Born-Jordan Vibration Dose-based Eigensystem Frequency Distribution is introduced for optimal frequency matching and isolating Sole Causative Factors (SCF), thus enhancing the building's resilience during earthquakes. Existing control algorithms often focus on short-term metrics, ignoring peak amplitude and long-term structural integrity, which leads to sub-optimal control force estimation. To overcome this, an Ebola-optimized Graph Multivariable Adaptive Neural Network (E-GMANN) is utilized to dynamically estimate the damper mass and control force in real-time, and improves KPI tracking and system responsiveness. Moreover, Soil-Structure Interaction (SSI) in asymmetric buildings leads to liquefaction, twisting vibrations, and poor structural resistance. Thus, Frequency Domain-Cyclic Equivalent Linear Analysis (TD-CELA) is employed, which addresses non-linear soil behavior, detects moisture-induced variations, thereby enhancing the deformation prediction. As a result, the suggested model outperforms existing methods, significantly reducing vibration, acceleration, and displacement.

The rapid expansion of photovoltaic (PV) generation has increased the need for educational and experimental platforms that allow students and researchers to study the dynamics, control strategies, and power conversion stages of grid-connected PV systems under realistic operating conditions. Although Hardware-in-the-Loop (HIL) simulation is widely used to validate power electronic converters and control algorithms, many existing platforms rely on specialized real-time simulators that limit their accessibility in academic environments. This paper presents the design and implementation of a cost-effective HIL simulation platform for grid-connected PV systems intended for research and training applications. The proposed system integrates real hardware under test within a real-time environment that emulates PV array behavior and grid conditions, combining Controller Hardware-in-the-Loop (CHIL) and Power Hardware-in-the-Loop (PHIL) techniques. A Texas Instruments C2000 microcontroller is used as the real-time digital simulator, providing an accessible alternative to conventional real-time simulation platforms. The platform architecture, the real-time PV emulator, and the experimental implementation are described and validated through simulation and experimental results. Finally, guided laboratory practices are presented to support hands-on training in PV systems and power electronics.

Ultra-high molecular weight polyethylene (UHMWPE) fiber has emerged as a critical material advancing the development of minimally invasive medical devices, owing to its exceptional specific strength, outstanding wear resistance, and inherent bio-inertia. We systematically review the current application landscape of UHMWPE fibers in minimally invasive medicine, highlighting their broad use in orthopedic sutures and fixation systems, reinforcement layers for cardiovascular interventional devices, cables to drive surgical robots, and materials in frontier neural interfaces. These applications underscore the material's core advantages across diverse scenarios. However, its broader clinical translation faces multiple challenges, including surface bio-inertia, long-term dynamic durability, difficulty in processing, and a lack of standardization. To address these challenges, this article delves into comprehensive strategies encompassing surface engineering, composite material development, structural optimization, and intelligent control algorithms. Looking forward, UHMWPE fibers are poised to evolve towards intelligence and functional integration. Through deep convergence with flexible electronics and data-driven research, coupled with the establishment of robust standardization systems, UHMWPE fibers are expected to play an even more pivotal role in the next generation of advanced minimally invasive medical devices, ultimately propelling the field towards greater precision and personalization.

Predefined-time affine formation tracking control of unmanned surface vehicles with input saturation via adaptive fuzzy observers.

To ensure consistent mechanical and technological material properties, maintaining precise cooling times during welding is crucial. Tolerances in weld groove preparation may introduce localized variations in arc power due to varying contact-tip-to-work distance, resulting in changing energy input and thus variable cooling times. This phenomenon is shown at the example of tack welds using a sensor system comprising a moving thermal camera along with current and voltage measurements. Using tack welds as an example of discontinuities in the weld seam, it was shown that the heat input increases by approximately 8 to 10% depending on the magnitude of the contact-tip-to-workpiece-distance variation, resulting in an extension of the cooling time by 10–20%. To mitigate these variations in energy input, an algorithm was developed to identify these discontinuities within 0.1 s. The algorithm works as a semi-supervised discontinuities measurement and control algorithm based on the Mahalanobis distance and detects discontinuities in-situ with a height of 3 mm and a length of 7 mm. The application of this algorithm-supported adaptation of the energy input enables constant cooling times and weld seam geometries. Beyond such conventional welding applications, the presented measurement and control algorithm is also applicable in additive manufacturing scenarios.

In this paper, the observer-based H ∞ control issue is investigated for a class of discrete-time cyber-physical systems over full-duplex relay (FDR) networks, where self-interferences (SIs) and denial-of-service (DoS) attacks are both taken into consideration. A FDR is deployed between the sensor and controller aimed at extending the communication range. In this relay-aided network, the FDR receives data from the sensor and then forwards it to the controller simultaneously, and the SI induced by these concurrent transmission behaviours is incorporated into the analytical framework. Additionally, the data transmission over the relay-to-controller channel is exposed to DoS attacks, which are modelled by a Bernoulli-distributed sequence. The objective is to devise an observer-based controller to satisfy the following requirements: (1) stability of the closed-loop system; (2) H ∞ performance against external disturbances; and (3) resilience with respect to SIs and DoS attacks. Finally, an extensive simulation on the tracking problem of mobile robots is provided to demonstrate the effectiveness of the proposed control algorithm. Specifically, the simulation results show that the proposed controller achieves a minimum H ∞ performance index of 0.0304 and exhibits lower tracking errors compared to the conventional baseline controller without anti-DoS compensation, thereby validating its robustness against DoS attacks.

This article proposes a novel control framework for hydraulic excavators based on a high-order fully actuated (HOFA) system approach. First, a comprehensive HOFA model is established, which describes the excavator dynamics in task space, joint space, and drive space. The proposed control algorithm systematically addresses multisource uncertainties, including kinematic calibration errors, as well as structured and unstructured parameter uncertainties in the dynamic and actuator models, via physically derived adaptive neural network compensation integrated into the controller. By decoupling the kinematic and dynamic loops, the algorithm simplifies controller design and theoretical analysis. Furthermore, by integrating the HOFA approach with task-space sensory feedback, the controller enables direct task specification and high-precision control of the excavator bucket tip. Finally, Lyapunov-based theoretical analysis proves asymptotic convergence of task-space tracking errors, and both simulation and experimental results validate the effectiveness of the proposed algorithm.

BACKGROUNDSimultaneous pancreas-kidney transplantation (SPKT), an established treatment for patients with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and end-stage renal disease (ESRD), provides metabolic stabilization and improved survival. Although perioperative glycemic control is crucial for graft viability; intraoperative management remains understudied, and standardized phase-specific protocols are lacking.AIMTo explore the feasibility, safety, and immediate outcomes of a six-phase intraoperative glycemic control algorithm for SPKT.METHODSThis retrospective case series included 11 patients with T1DM or T2DM and ESRD who underwent SPKT at a quaternary care center between January 2024 and May 2025. All patients were managed using a six-phase institutional glycemic control algorithm that maintains intraoperative blood glucose levels within predefined targets. Data on clinical, metabolic, surgical variables; complications; and early outcomes were collected.RESULTSIn most cases, intraoperative blood glucose levels were maintained within target ranges; however variations were observed [range: 63-453 mg/dL (3.5-25.2 mmol/L)]. No severe hypoglycemia or ketoacidosis occurred. During the first 24 postoperative hours, seven patients (63.6%) achieved euglycemia without exogenous insulin, whereas two required transient insulin therapy. Six patients (54.5%) had postoperative complications, including thrombotic (n = 4), infectious (n = 2), and reperfusion syndrome (n = 1). One patient experienced more than one event. All events were successfully managed, without graft loss, acute rejection, or in-hospital mortality.CONCLUSIONThe phase-specific intraoperative glycemic protocol for SPKT is feasible and safe. However, prospective studies with larger cohorts are warranted to assess its long-term impact on graft survival and patient outcomes.

Severe pyogenic soft-tissue infections (SSTI) are a frequent cause of morbidity among people living with HIV (PLHIV) in resource-limited hospitals. Drawing on five months of frontline work in a district surgical unit in South Sudan, this field report distills practical lessons into a simple, resource-adapted algorithm for triage, source control, antibiotics, and wound care. Core steps include bedside sepsis screening with qSOFA, prompt empiric antibiotics aligned with the WHO EML/AWaRe approach, and decisive operative debridement without waiting for advanced diagnostics when necrotizing infection is suspected, followed by planned re-look procedures. Low-cost wound-care options (eg, diluted hypochlorite/povidone-iodine transitioning to saline gauze) and loss-to-follow-up-aware discharge practices are emphasized. The aim is to standardize care and shorten time to debridement in district-level services rather than report outcomes. Keywords: pyogenic soft-tissue infection; HIV; resource-limited settings; necrotizing fasciitis.

Current cancer treatment strategies prioritize algorithmic robustness and efficiency but frequently neglect critical aspects of patient safety and comfort. These approaches typically rely on chemotherapy-based mathematical models optimized solely for short-term tumor reduction, disregarding the broader impact on patient health. This study introduces a patient-centered approach to optimize cancer treatment, balancing treatment efficacy and toxicity. The proposed research method incorporates both radiation therapy and chemotherapy simultaneously in the form of ordinary differential equations (ODE)-based mathematical dynamics. These updated dynamics are then utilized to propose a novel control mechanism that integrates nonlinear sliding mode control (SMC) with reinforcement learning-based proximal policy optimization (PPO) algorithm. Conventional sliding mode control (SMC) algorithm is first modified by replacing its signum function-based switching control with a sigmoid function to address issues like chattering and transients in treatment control. This smooth SMC is then incorporated within the framework of PPO to dynamically adjust treatment schedules, reduce drug and radiation dosages, smooth administration of treatment dosages, and enhance patient health indicators. Results showed that the proposed hybrid PPO method effectively lowered chemotherapy and radiotherapy dosages while maintaining tumor suppression, minimizing treatment toxicity, and improving immune cell recovery. In quantitative comparisons, the proposed PPO algorithm reduced baseline dosages by up to 76.8% for chemotherapy and 66% for radiotherapy and achieved tumor suppression 5.67% faster than conventional multi-input optimization methods. It also lowered cumulative treatment intensity by over 92%, demonstrating a substantial enhancement in patient safety. The methodological originality of this study lies in integrating nonlinear smooth SMC with reinforcement learning-based PPO within a patient-centered ODE modeling framework that jointly represents radiotherapy, chemotherapy, tumor dynamics, immune cell dynamics, healthy cell preservation, and health indicator state. The proposed framework provides a relevant, toxicity-aware computational approach for radio-chemotherapy dosage optimization, demonstrating lower simulated treatment intensity while maintaining tumor suppression under stated model assumptions. As a pre-clinical computational proof of concept, this work establishes a robust and interpretable basis for future treatment-planning studies, subject to retrospective clinical validation, patient-specific parameterization, and prospective safety evaluation.

Autonomous hydraulic excavators are widely used in construction, mining, and material-handling operations, offering improved efficiency and safety. Precise trajectory tracking is essential for such systems. However, inherent nonlinearities and significant time delays in the hydraulic actuators hinder accurate control for autonomous operations. To address these challenges, a nonlinear model predictive control (NMPC) algorithm is proposed. Specifically, a Hammerstein–Wiener structure is employed to model the nonlinear hydraulic system, with parameters identified from experimental data. Based on this model, an NMPC trajectory-tracking algorithm is developed, which accounts for actuator and control input constraints. To mitigate the intrinsic 0.5-second response delay of the hydraulic system, a predictive delay compensation strategy is introduced, whereby predicted joint states over the next 0.5 seconds serve as real-time control references. Simulation results demonstrate that the proposed controller substantially outperforms proportional–integral–derivative (PID) and fuzzy PID methods, maintaining the bucket-end error within 20 cm. Field experiments on an autonomous excavator implemented under the robot operating system (ROS) framework confirm that the maximum trajectory-tracking error remains within 50 cm, validating the effectiveness and robustness of the proposed NMPC approach under real-world operating conditions.

Hybrid control scheme of nonlinear model prediction and adaptive terminal sliding mode for underwater vehicles based on threshold switching.

ABSTRACT This paper presents a cross-platform validation methodology for Model Predictive Control (MPC) systems in nuclear applications, using open-source tools. The approach employs a dual framework validation strategy: a commercially validated MATLAB/Simulink implementation serves as the reference benchmark, while an equivalent Python-based implementation is systematically verified. The methodology is demonstrated through a steam generator control case study for a Low Temperature Reactor (LTR), using a simplified lumped parameter model linearized at five operating points. Controller performance was quantitatively compared across 13 transient scenarios using Normalized Root Mean Square Deviation (NRMSD), with all key outputs showing NRMSD < 0.5%. The MPC strategy reduced water level overshoot by 48% and settling time by 62% compared to conventional PID control, while effectively handling constraints and non-minimum phase dynamics. Results confirm that open-source platforms can serve as credible environments for prototyping nuclear grade control algorithms, providing a transparent pathway from design to deployment.

Modern left ventricular assist devices (LVADs) remain associated with driveline infections and insufficient responsiveness to patient activity demands. Transitioning to wireless charging presents an opportunity to mitigate driveline infections, but using wireless charging systems and implanted battery storage necessitates reducing the LVAD's power consumption. To address these challenges, a real-time automatic speed modulation control algorithm was developed that adjusts pump speed based solely on the energy required to maintain the LVAD's magnetic levitation balance, obviating the need for additional sensors. This metric serves as a surrogate for patient activity state, enabling automatic pump speed modulation in response to changes in demand. The algorithm was implemented and tested in a previously validated numerical mock circulatory loop (nMCL) coupled with a detailed LVAD model that accurately represents real-life magnetic levitation physics and control dynamics. Comparative simulations of patient models with and without the automatic speed modulation showed that, during exercise, the controller-enhanced model achieved greater circulatory support, whereas during sleep, reduced pump speeds did not compromise hemodynamic output. These results indicate that the proposed control strategy not only optimizes energy usage-extending battery life and supporting wireless charging-but also confers physiological benefits by adjusting pump performance to meet varying patient demands. Furthermore, this work provides a promising framework for improving LVAD functionality and patient outcomes, with potential implications for future device design and clinical implementation.

Connected and automated vehicle (CAV) platoons provide significant advantages in enhancing traffic efficiency and safety through vehicle-to-vehicle cooperative driving. However, owing to the uncertainty of human-driven vehicles in mixed traffic environments, platoons must frequently split to avoid potential collisions and merging is required to maintain platoon following. To address this challenge, this paper proposes a cooperative control architecture for CAV platoons that includes a single-vehicle cruising control mode and a platoon-following control mode, enabling independent operation of each mode and discrete event transitions around split and merge maneuvers. In single-vehicle mode, a driving safety potential field model is proposed for collision-avoidance trajectory planning, and a distributed model predictive control algorithm is designed to achieve the distinct control objectives of the two modes. Then, a long short-term memory (LSTM) neural network and fuzzy logic are combined to predict collision risk and determine platoon split events. A cooperative control system is implemented to ensure continuous control and flexible switching between the two modes. Finally, joint simulations in PreScan, CarSim, and MATLAB/Simulink were conducted to evaluate the performance of the system across various obstacle scenarios. The results demonstrate that the proposed control architecture effectively coordinates vehicle maneuvers and adapts platoon formation to changes in traffic conditions.

This paper studies the problem of adaptive fuzzy output feedback funnel secure control for vehicle platoon systems under replay attacks, which contains nonlinear dynamics and unmodeled dynamic. Fuzzy system is adopted to identify unknown nonlinear dynamics, then fuzzy observer is designed to estimate the immeasurable states, and funnel functions are introduced to constrain the distance between each vehicle. By introducing Lipschitz conditions, it is possible to analyse the error changes of systems subjected to replay attacks and obtain the threshold for error changes. By using the changing supply function technique to address the unmodeled dynamics, an observer-based robust secure adaptive fuzzy funnel formation control scheme is developed. Based on Lyapunov stability theory, it can ensure all signals of the controlled system are bounded, and the desired spacing and avoid collision can be maintained. Finally, a simulation is considered to verify the effectiveness of the developed control algorithm.

Grid stability is of prime importance for grid-tied solar power systems as they are prone to power quality issues caused by the varying intensity of sun radiation and grid disturbances. To maintain grid stability, various PV power tracking algorithms have been developed. However, classical power tracking models often fail to maintain grid stability and sustain required power reserves under real-time variations in grid conditions and solar generation. To address this limitation, a Dynamic Reserve Power Point Tracking (DRPPT) control algorithm is proposed to ensure grid stability by dynamically adjusting reserve power. By continuously monitoring the PV array and grid conditions, the proposed controller determines the dynamic solar reserve power and accordingly selects the suitable operating mode. The operating point of PV array is then regulated by Flexible Power Point Tracking (FPPT) technique, which performs fine-tuning of the reference voltage followed by grid injection. By combining FPPT and Maximum Power Point Tracking (MPPT) functionalities, the proposed DRPPT controller maintains optimal reserve levels, ensuring the grid can rapidly respond to sudden changes in power supply or demand while meeting customer requirements. The proposed model is tested on hardware and simulated on software, and both results show the ability of DRPPT algorithm to handle real-time grid frequency changes and adapt the RPPT operation accordingly to meet grid stability standards. The proposed model has achieved superior THD mitigation, thereby improving grid stability, with 53.75%, 50%, and 7.5% lower THD compared to the conventional RPPT, Global FPPT (GFPPT), and Genetic Algorithm (GA)-based FPPT techniques, respectively.

Environmental benefits and impacts forecasting for three-phase induction motors operations in marine applications: A multiple linear regression approach