Advanced Control Research Articles

A Control Strategy for Salient-Pole Permanent Magnet Axial-Gap Self-Bearing Motors Considering Stator Inductance Variation

Axial-Flux Self-Bearing Motors (AFBMs) are distinguished by considerable variations in inductance resulting from axial displacement. Conventional controllers are frequently engineered to accommodate a wide range of inductance changes. Nevertheless, this methodology is often suboptimal, particularly when inductance is not accurately determined. This paper puts forward a novel inductance approximation function for the stator, designed to minimize modeling errors. Furthermore, an advanced control strategy is presented, based on a sliding mode controller with online adaptive parameter tuning. The experimental results demonstrate the efficacy of the proposed control strategy, exhibiting enhanced performance, stability, and robustness across a range of operating conditions.

Double gap surface plasmons enabled ultra-thin meta-F–P cavity for light filtering in the near-infrared

Over the past years, metasurfaces have demonstrated remarkable and diverse capabilities in advanced control over light properties. Embedding metasurfaces into the Fabry–Pérot (meta-F–P) cavity reduces the required cavity length and provides new degrees of freedom for tuning. Most meta-F–P cavities exhibit excellent color filtering effects within the visible spectrum. However, achieving single mode-F–P resonance across the entire near-infrared range remains challenging due to the phase condition limitations of the metasurfaces. Here, we explore the integration of silver metasurfaces into an F–P cavity with a cavity length of only 150 nm. The very short cavity length allows for the existence of gap surface plasmons between the silver metasurfaces and both the top and bottom silver mirrors of the F–P cavity. This setup achieves narrowband filtering in an ultra-wide spectral range from 626.6 to 2548.3 nm while consistently maintaining single-mode resonance. Furthermore, we analyze the filtering effects of embedding anisotropic structures into the F–P cavity under x- and y-polarized incident light, revealing polarization-dependent filtering capabilities. Embedding the metasurface within the F–P cavity also allows for stable responses to different angles of incident light. This study underscores the potential of meta-F–P cavities in advancing optical filter technology for diverse applications in spectroscopy, telecommunications, and sensing.

A modified mental workload model based on CREAM in the complex scenarios of advanced main control room in the nuclear power plant

A modified mental workload model based on CREAM in the complex scenarios of advanced main control room in the nuclear power plant

Gasification-MILD combustion technology with ash-sludge recirculation for reducing multi-phase net dioxin discharges from a waste incineration.

This study addresses the challenge of reducing "net" toxic pollutant discharge, specifically dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), while minimizing the energy consumption and costs associated with detoxification. Our research focuses on reintroducing fly ash and scrubber sludge (ASR) into a hazardous waste thermal treatment system equipped with gasification-intense low oxygen dilution (GASMILD) and an advanced air pollution control system (APCS). This approach yielded a remarkable PCDD/F removal efficiency exceeding 99.9% for both mass and toxic equivalent (TEQ), achieving a net destruction of PCDD/Fs within the system. The success can be attributed to the ASR material's richness in ultrafine particles, which act as nucleation cores during combustion, promoting particle growth and enhancing the capture of PCDD/Fs in the APCS. This leads to a significant reduction (over 93%) in both PCDD/F mass and World Health Organization-toxic equivalent (WHO-TEQ) concentration in the flue gas. While a phase transition of gaseous PCDD/Fs within the APCS caused a shift in the particle size distribution towards larger particles, the overall PCDD/F mass concentration in the stack emissions remained lower with ASR. Notably, the reintroduced PCDD/Fs tend to deposit in the nasal region of the respiratory tract, potentially reducing the health risks associated with deeper lung deposition. This research demonstrates the effectiveness of ASR in achieving net PCDD/F destruction within a GASMILD combustion system, offering a promising strategy for cleaner and more sustainable waste management practices. From a managerial perspective, this approach provides a cost-effective solution for PCDD/F mitigation, contributing to reduced emissions and improved public health.

DAMNet: Dynamic mobile architectures for Alzheimer's disease.

Alzheimer's disease (AD) presents a significant challenge in healthcare, highlighting the necessity for early and precise diagnostic tools. Our model, DAMNet, processes multi-dimensional AD data effectively, utilizing only 7.4 million parameters to achieve diagnostic accuracies of 98.3% in validation and 99.9% in testing phases. Despite a 20% pruning rate, DAMNet maintains consistent performance with less than 0.2% loss in accuracy. The model also excels in handling 3D (Three-Dimensional) MRI data, achieving a 95.7% F1 score within 805s during a rigorous three-fold validation over 200 epochs. Furthermore, we introduce a novel parallel intelligent framework for early AD detection that improves feature extraction and incorporates advanced data management and control. This framework sets a new benchmark in intelligent, precise medical diagnostics, adeptly managing both 2D (Two-Dimensional) and 3D imaging data.

Smart Glove: A Cost-Effective and Intuitive Interface for Advanced Drone Control

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse.

Operating Point Optimization of Agricultural Tractor–Implement Combinations as Constraint Optimization Problem

Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO2 emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition, overall process efficiency is an ambiguous optimization objective in agricultural processes as it relates resource consumption to harvest yields, which are only known at the end of a harvest season. The presented approach defines process constraints, ensuring optimization does not negatively affect harvest yield. These constraints allow for the formulation of explicit objective functions that are observable during the operation. The method establishes a mathematical foundation for the optimization of agricultural processes. The mathematical principles of the theoretical framework and the techniques used to define control constraints are explored, whereby the applicability to alternative objectives like optimizing the overall process cost is highlighted. To demonstrate the practical utility of the proposed approach, an optimization cycle is applied to a real-world scenario: adapting the working speed during the tillage process using a cultivator to maximize energy efficiency. The approach simplifies the optimization problem by formulation as a constraint optimization problem, allowing for improving the operating point of tractor–implement combinations with respect to observable process objective functions. The results underline the importance of advanced control strategies in agricultural machinery, advancing precision agriculture and promoting sustainable farming practices.

Trajectory tracking of QUAV based on cascade DRL with feedforward control

The adoption of advanced control strategies has become increasingly critical for the quadrotor unmanned aerial vehicle (QUAV). Deep reinforcement learning (DRL) stands at the forefront of these developments, showcasing significant utility, particularly in the realm of static target tracking for QUAVs. However, existing DRL methodologies encounter substantial challenges with latency issues when applied to trajectory tracking scenarios. This paper tackles this challenge by incorporating a feedforward technique, which empowers agents to analyze high order trajectory information. Initially, a cascade DRL controller for fixed point tracking is trained, where multiple agents are separately responsible for translation and rotation movement along each axis. Then, the controller is implemented for trajectory tracking by incorporating the feedforward information from the trajectory, which significantly solves the latency issue. Through extensive tracking demonstrations and quantitative analysis, the proposed integrated scheme demonstrates a significant enhancement in the trajectory tracking performance of QUAVs.

From BIM to Digital Twin: A transformation process through advanced control modeling and automated commissioning using daylight and artificial lighting as examples

From BIM to Digital Twin: A transformation process through advanced control modeling and automated commissioning using daylight and artificial lighting as examples

Exploring building performance evaluation methods to estimate energy savings following field implementation

Research on energy conservation measures and advanced control strategies is becoming increasingly present to improve building energy performance and may reach the stage of field testing in existing buildings. Therefore, it becomes critical to properly assess the benefits following on-site implementation. Different approaches have been conducted in the past to predict building energy performance and evaluate energy savings. However, it remains not clear how the calculation methods and their underlying assumptions eventually influence the savings estimates. This paper aims to compare the energy savings obtained following the implementation of data-driven measures in an existing building and estimated using three different approaches that are commonly found in the literature. The intricacies of these baseline model-free and baseline model-based methods were investigated, and various aspects were explored such as weather normalization, baseline modelling time-step (monthly, daily, hourly, sub-hourly), input sets (including weather, time-index variables, room air temperatures, heating system conditions), and techniques (linear, multi-linear, gaussian process regression). Results show that savings estimates can significantly vary from one performance evaluation method to another, and from one model to another, highlighting the importance of properly selecting both the evaluation method and the baseline model. Baseline model-based approaches were found more appropriate than the other methods, and daily linear regressions against outdoor air temperature and hourly gaussian process regression using weather and time-index variables were found suitable for building performance evaluation.

Decentralized Framework for Securing Smart Grids Using Blockchain and Machine Learning

Abstract: The transition to smart grids has revolutionized energy distribution, enabling more efficient and flexible power management through advanced communication and control systems. However, this interconnected structure makes smart grids vulnerable to cyberattacks, such as False Data Injection Attacks (FDIA), Distributed Denial of Service (DDoS) attacks, and data manipulation. These threats undermine the stability and reliability of the grid, and existing centralized security frameworks are often ill-equipped to address them due to their susceptibility to single points of failure and limited scalability. To overcome these challenges, this paper introduces a decentralized security framework that combines blockchain technology with machine learning (ML). The framework leverages blockchain to provide a transparent, immutable, and decentralized ledger, employing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA) to ensure secure data validation. Alongside this, ML models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), are used to detect anomalies in time-series data, such as FDIA, with high precision. Smart contracts embedded in the blockchain enable automated, real-time responses to threats, such as isolating compromised nodes or rerouting energy flows to maintain grid stability. Through simulations replicating real-world cyberattacks, the proposed framework demonstrated over 95% detection accuracy, a 30% reduction in response times, and enhanced computational efficiency with lower energy consumption. These results affirm the effectiveness of the framework as a scalable and resilient solution for modern smart grid security.

Optimum Material Removal in Electrical Discharge Machining through Advanced Control Integration of PSoC Microcontroller

Electrical Discharge Machining (EDM) is an advanced non-traditional method for precision material removal using repeated electrical discharges. Itutilizes a programmable tool electrode to intricately shape designs. Existing research has highlighted that current EDM control systems are often intricate, sluggish, and costly. In addressing these limitations, there is a notable opportunity to enhance the design of an advanced EDM control system, that is both sophisticated and cost-effective. Therefore, the authors proposed an integration of a pulse generator, control system, timer, and flushing system using a Programmable System-on-Chip (PSoC) microcontroller to regulate the gap between an electrode a workpiece, flushing system, and the overall EDM machining process. The pulse generator algorithm was developed to effi ciently manage T<sub>on</sub> and T<sub>off</sub>, while the Proportional Integral Derivative (PID) algorithm was employed to uphold the gap between the electrode and the workpiece. Additionally, the timer algorithm was closely associated with the machining process timing. Activation of the second servo pump at 80% of machining was implemented to enhance the flushing pressure rate. The EDM-PSoC system was utilized with the experiment conduction at three different current settings (2A, 4A, and 6A) over five periods, and results were compared with the EDM-Existing system. The experimental outcomes revealed a notable increase in Material Removal Rate (MRR),averaging 0.1348 mm<sup>3</sup>/min at 6A. Furthermore, the EDM-PSoC system demonstrated high consistency in each repetition experiment conducted at lower currents, achieving MRRs of 0.0142 mm<sup>3</sup>/min at 2A and 0.0560 mm<sup>3</sup>/min at 4A. Comparatively, the EDM-PSoC system improved the average MRR by 49% compared to the traditional EDM-Existing system. The depth of the machined workpiece produced by the EDM-PSoC system was deeper than the EDM-Existing system at equivalent time settings. Consequently, the findings indicate that the EDM-PSoC system achieved a higher MRR relative to the conventional EDM procedure, thus improving significantly the system efficiency.

Assessment of noise pollution and specific mitigation measures to reduce it in educational settings and its impact on students' academic performance: A review

Noise pollution causes many problems in the educational process and adversely affects the performance of educators and students. This study reviewed the research, studies, surveys, and questionnaires on the subject and examined the effects of noise pollution on the academic performance of students and certain mitigating measures to control it. This document provided a comprehensive overview of noise measurements, comparison with guidelines/standards, factors affecting noise in college and university campuses, external and internal noise sources in the educational environment, effects of noise on health and student performance, sound quality of classrooms, and noise reduction. The results of the review revealed that most academic students are exposed to noisy environments, which have significant physiological and psychological effects on health. The review recommended certain noise control and management measures that include the use of noise barriers, construction of green spaces, design of noise reduction infrastructure, implementation of awareness programs, strict enforcement of laws and noise regulations, the development of advanced noise control technologies, integrated strategies, and sustainable environmental planning to reduce the menace of noise pollution.

Safety-assured high-speed navigation for MAVs.

Micro air vehicles (MAVs) capable of high-speed autonomous navigation in unknown environments have the potential to improve applications like search and rescue and disaster relief, where timely and safe navigation is critical. However, achieving autonomous, safe, and high-speed MAV navigation faces systematic challenges, necessitating reduced vehicle weight and size for high-speed maneuvering, strong sensing capability for detecting obstacles at a distance, and advanced planning and control algorithms maximizing flight speed while ensuring obstacle avoidance. Here, we present the safety-assured high-speed aerial robot (SUPER), a compact MAV with a 280-millimeter wheelbase and a thrust-to-weight ratio greater than 5.0, enabling agile flight in cluttered environments. SUPER uses a lightweight three-dimensional light detection and ranging (LIDAR) sensor for accurate, long-range obstacle detection. To ensure high-speed flight while maintaining safety, we introduced an efficient planning framework that directly plans trajectories using LIDAR point clouds. In each replanning cycle, two trajectories were generated: one in known free spaces to ensure safety and another in both known and unknown spaces to maximize speed. Compared with baseline methods, this framework reduced failure rates by 35.9 times while flying faster and with half the planning time. In real-world tests, SUPER achieved autonomous flights at speeds exceeding 20 meters per second, successfully avoiding thin obstacles and navigating narrow spaces. SUPER represents a milestone in autonomous MAV systems, bridging the gap from laboratory research to real-world applications.

Quantifying and simulating the weather forecast uncertainty for advanced building control

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modelling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen–Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

Operational Pitch Actuation Dynamics for Offshore Wind Turbines Ranging from 5 to 50 MW

ABSTRACTModern wind turbines have been continuously growing in size due to the increased power generation and reduced costs associated with larger rotors and more abundant wind resources offshore. In order to effectively implement pitch control on blades that are longer, more flexible, and heavier than ever before, modern electric pitch systems must provide enough torque to overcome blade pitch inertia and loads while providing suitable control response frequencies. Despite this need, there is limited published research on the sizing of such pitch systems at extreme scales. This study models peak pitching power and pitch actuator torque requirements in Regions 2 and 3 turbulent wind conditions. The developed model considers blade pitch response, pitching moments, pitch system dynamics, and blade aeroelasticity. The model is applied using an integrated wind turbine code used to simulate the turbine response of a 25‐MW offshore reference turbine with advanced pitch control under standardized turbulent wind conditions. The results show that the fastest pitch response requirements occur in Region 3 wind speeds just above the rated wind speed and that the peak pitch actuator torque requirements are correlated with maximum pitching moments. The model is extended to turbines ranging from 5 to 50 MW to develop a simplified scaling power law based on only the product of blade mass and mean chord length. This scaling law predicts maximum pitch actuator torque and maximum power consumed from pitch actuation based on results from computational simulations of multiple extreme‐scale reference turbines. This study provides useful insights for the design and sizing of pitch systems in large‐scale wind turbines.

Soft Robot Kinematic Control via Manipulability-Aware Redundancy Resolution

Abstract This paper addresses the kinematic control of a redundant soft robotic arm. Kinematic full pose control for soft robots is challenging because direct application of the classical controllers developed based on rigid robots to soft robots could lead to unreliable or infeasible motions. In this study, we explore the manipulability property of soft robotic arm and develop an advanced resolved-rate controller which prioritize position over orientation control and switches its modes and gains based on position and orientation manipulabilities, enabling stable motion even when the robot is close to singular and near-singular configurations. The simulation and experimental results indicate that our proposed method outperforms previous methods in terms of both accuracy and smoothness during operations.

Establishing robust infection control at a new hospital: the AIIMS Rajkot experience

The establishment of robust infection control practices is crucial in any new healthcare facility to ensure patient safety and prevent healthcare-associated infections (HAIs). All India Institute of Medical Sciences (AIIMS) Rajkot, a newly inaugurated medical institution in Western India, faced the significant challenge of implementing these practices from the ground up, beginning with its Outpatient Department services initiation on 31 December 2021. The AIIMS Rajkot initiated its infection control program by forming a Hospital Infection Control Committee and appointing dedicated Infection Control Officers and Infection Control Nurses. This initiative was supported by comprehensive training programs, the implementation of advanced infection control measures such as bundle care, HAI surveillance, and the development of outbreak investigation teams. Furthermore, the hospital focused on the continuous improvement of these practices through routine audits and the use of a digital Infection Control Risk Assessment form. The institution successfully integrated a structured infection control framework that includes rigorous training sessions, the implementation of a Hospital Infection Control Manual, and the establishment of a robust needle stick injury management team. These measures have led to improved compliance with infection prevention protocols, as evidenced by hand hygiene audits and reduced incidence of HAIs. The journey of establishing infection control practices at AIIMS Rajkot highlights the importance of a structured approach in new healthcare settings. The successful integration of these practices not only enhances patient and staff safety but also sets a standard for future healthcare facilities. The AIIMS Rajkot’s proactive measures and continuous improvement efforts serve as a model for effectively addressing the challenges of infection control in a dynamic healthcare environment.

Advanced Control System for Real-Time Regulation of Dissolved Oxygen in Aquaculture Systems

Advanced Control System for Real-Time Regulation of Dissolved Oxygen in Aquaculture Systems

Emergence of flocking behaviors transferring previously evolved alignment robot controllers

Flocking is a crucial collective behavior in swarm robotics. Reynolds introduced the boids model as a means to imitate flocking behaviors in artificial agents. This model relies on three fundamental local rules: separation, cohesion, and alignment. This paper examines the development of flocking behaviors only through the evolution of the alignment rule. Initially, we employ a genetic algorithm to develop the alignment behavior inside a group of stationary robots. The advanced alignment robot controller is a continuous-time recurrent neural network (CTRNN). Afterwards, we include the developed controller into a three-layered subsumption architecture in order to accomplish flocking behavior. Aside from the advanced alignment behavior, the architecture also incorporates a rudimentary manually designed obstacle avoidance behavior and a subroutine for moving forward. The initial experiment centers on the progression of alignment among the robots. Advanced communication techniques result in a scalable and precise alignment, where both the message content and its related context are very pertinent. The second experiment investigates the development of flocking behavior. The results indicate that the suggested subsumption architecture is capable of achieving efficient flocking behaviors. In addition, the robot swarm has the ability to navigate around barriers and continue to exhibit flocking behavior once the impediments have been bypassed. Our research indicates that the formation of a cohesive group can occur by implementing a single developed rule, complemented with well designed actions for avoiding obstacles and navigating the environment.