Control Algorithm Research Articles

Energy optimization using adaptive control algorithm to enhance the performance of SDN_IOT environment

Cybersecurity attacks are evolving against the normal flow oftraffic/data in the network. The information stolen by the intruders/attackers can interrupt network services and compromise the integrity of the IoT devices. The most prevalent extortions to SDN-IoT networks are Distributed Denial of Service (DDoS) attacks, which flood the attack request until the server/device crashes/shuts down. This paper presents an algorithm for detecting DDoS attacks earlier from the network using the entropy method and mitigating the attacks earlier using stochastic techniques. The detection and mitigation of DDoS attacks from the SDN_IoT environment calculates the energy utilized by the devices connected to the network using Adaptive optimization techniques. This technique concludes the optimum dissemination of network resources and the deployment of defense mechanisms to mitigate the control of attacks. The efficacy of the suggested approach is proven by comprehensive simulations that use realistic attack scenarios and representative SDN-IoT network topologies. The system reduces energy consumption by 18%, improves detection accuracy by 2% (from 97.5% to 99.4%), and lowers the false positive rate to 0.01% compared to existing algorithms. The suggested approach also improves throughput by 2.6%, decreases detection time by 70%, and reduces CPU use by 15%, all of which contribute to quicker mitigation of DDoS attacks. The method’s capacity to optimize network security and resource consumption is shown by these results, establishing it as a strong option for safeguarding SDN-IoT systems.

Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards

This study proposes an enhanced multi-agent swarm control algorithm (EN-MASCA) to solve the problem of efficient patrolling of drone swarms in complex durian orchard environments. It introduces a virtual navigator model to dynamically adjust the patrol path of the drone swarm and perform obstacle avoidance and path optimization in real time according to environmental changes. Different from traditional algorithms that only rely on fixed path planning, the virtual navigator model significantly improves the flexibility and stability of the drone swarm in complex environments. It also applies deep reinforcement learning algorithms to path planning and obstacle avoidance of drone swarms for the first time, improving the algorithm’s adaptability and optimization capabilities by learning dynamic information in complex environments. This innovation significantly improves the applicability of existing methods in complex terrain and dynamic obstacle environments. Finally, it incorporates the simulation characteristics of biological swarm behavior, and on this basis, comprehensively optimizes the flight path, obstacle avoidance and swarm stability of the drone swarm. By improving control strategies and parameter design, it improves the trajectory consistency and mission completion efficiency of the UAV swarm during flight. In the experimental part, this study verified in detail the advantages of the EN-MASCA algorithm in terms of flight trajectory, flight stability, cluster consistency and task completion efficiency by constructing a six-degree-of-freedom UAV motion simulation model and real environment simulation. It provides an efficient and intelligent solution for collaborative patrol operations of drones in durian orchards, which has important practical application value and promotion prospects.

Design and Implementation of MPPT Techniques on PV Systems using Fuzzy Logic controller, ANN, and Genetic Algorithm

Design and Implementation of MPPT Techniques on PV Systems using Fuzzy Logic controller, ANN, and Genetic Algorithm

Machine Learning Control for Assistive Humanoid Robots Using Blackbox Optimization of PID Loops Through Digital Twins

With the latest technological advancements in Machine Learning, the focus shifted significantly from classical data analysis to controlling various robotics systems. Such an approach, compared to classical control methodologies, provides a simultaneous design, validation and robustness analysis that is performed in an autonomous manner. Although the dynamic performance is not formally guaranteed, the more learning iterations are performed, the more the confidence in the designed solution increases. In this work, we address the problem of accelerating Machine Learning Control (MLC) algorithms by parallelizing the learning with the aid of a simulated test environment containing a Digital Twin of a NAO robot. The increase of the modeling robustness and of the generality of the control algorithm is ensured by performing random positioning tasks with each learning episode. The solutions are further leveraged through Transfer Learning using the real robot and the results are validated and compared. Our main goal is to provide a design framework for assistive robots, which bring significant societal benefits, although require high reliability and safe operation. In this respect, a thorough statistical study concerning the comparison of two typical MLC algorithms, namely the Genetic Algorithm and the Bayesian Optimization, is included.

Tanzanian sign language recognition system for an assistive communication glove sign tutor based on the inertial sensor fusion control algorithm

This paper presents a sign language recognition system for sign tutoring assistive hand data gloves for hearing-impaired people. In this study, specially designed 5-fingered data gloves are used for interaction and communication with hearing-impaired or hard-of-hearing people using signs. In this paper, a sign language recognition scheme based on an inertial sensor fusion control algorithm is proposed to achieve efficient and accurate real-time sign language recognition. The fusion control algorithm uses a feedback control idea to fuse two traditional attitude information calculation methods, reducing the impact of the environment on the sensor. The attitude information of the tested object in the instantaneous state can be accurately obtained. The algorithm uses the classification methods of support vector machine (SVM), K-nearest neighbor method (KNN) and feedforward neural network (FNN) classifier adaptive model to classify the data collected by the sign language data through data fusion, data preprocessing and feature extraction. The results show that the proposed sensor fusion control algorithm effectively obtains real-time poses. The recognition accuracy of the sign language recognition scheme for 26 kinds of Tanzanian sign languages is 96.5%. The proposed scheme will lay a solid foundation for sign language recognition systems and provide a reference for relevant research on sensor fusion control.

Efficient Dual-Loop PFC for Challenging Dynamic Processes

When controlling difficult industrial processes characterised by open-loop instability and/or poor damping, predictive functional control (PFC) practitioners often face two critical design challenges. The first one arises due to the intrinsic simplicity of the PFC control algorithm that, instead of optimising the future control trajectory in real time like other predictive controllers, simply assumes constant control action, thereby producing unreliable model predictions in the long range and thus inconsistent closed-loop performance. The second issue is related to the controller tuning, which may become ambiguous and unsystematic due to the existence of an inconsistent relationship between the controller parameters and closed-loop behaviour. This paper presents a dual-loop control strategy that aims at mitigating both weaknesses simultaneously by combining the concepts of pre-stabilisation and relative tuning within the framework of predictive functional control. Two challenging industrial case studies have been analysed through computer simulations that successfully validate the efficacy of the proposal under various real world scenarios.

Variable Trust Control Setting for Autonomous Vehicle Highway Navigation and Improved User Experience

This paper addresses the development of a model-based design approach to enhance the acceptance of safe manoeuvrability of autonomous vehicles (AVs) on highways. A variable trust control setting (TCS) is introduced that empowers users to 'feel in control' of the AV, potentially increasing confidence in, and acceptance of, the technology. This setting is grounded in deontological ethics and utilises virtual boundaries (VBs) to guide driving decisions, i.e., the distance between two AVs interacting with one another. The approach is simulated using a dynamic bicycle model that represents each AV, controlled through an adaptive model-predictive control (MPC) algorithm. The paper outlines the MPC approach, the dynamic bicycle model, and the associated velocity control algorithm. Metrics are introduced to quantify safety of specific AV manoeuvres during interactions with other AVs, enabling the examination of various scenarios. A novel simulation package has been developed to investigate the impact of the proposed variable TCS, focusing on how VBs and steering limitations influence the safety and comfort of AVs during overtaking manoeuvres. The findings demonstrate the effectiveness of this approach, showing that it could potentially allow users to actively manage the safety and comfort aspects of AV operation.

Healthy Mom Zone Adaptive Intervention With a Novel Control System and Digital Platform to Manage Gestational Weight Gain in Pregnant Women With Overweight or Obesity: Study Design and Protocol for a Randomized Controlled Trial.

Regulating gestational weight gain (GWG) in pregnant women with overweight or obesity is difficult, particularly because of the narrow range of recommended GWG for optimal health outcomes. Given that many pregnant women show excessive GWG and considering the lack of a "gold standard" intervention to manage GWG, there is a timely need for effective and efficient approaches to regulate GWG. We have enhanced the Healthy Mom Zone (HMZ) 2.0 intervention with a novel digital platform, automated dosage changes, and personalized strategies to regulate GWG, and our pilot study demonstrated successful recruitment, compliance, and utility of our new control system and digital platform. The goal of this paper is to describe the study protocol for a randomized controlled optimization trial to examine the efficacy of the enhanced HMZ 2.0 intervention with the new automated control system and digital platform to regulate GWG and influence secondary maternal and infant outcomes while collecting implementation data to inform future scalability. This is an efficacy study using a randomized controlled trial design. HMZ 2.0 is a multidosage, theoretically based, and individually tailored adaptive intervention that is delivered through a novel digital platform with an automated link of participant data to a new model-based predictive control algorithm to predict GWG. Our new control system computes individual dosage changes and produces personalized physical activity (PA) and energy intake (EI) strategies to deliver just-in-time dosage change recommendations to regulate GWG. Participants are 144 pregnant women with overweight or obesity randomized to an intervention (n=72) or attention control (n=72) group, stratified by prepregnancy BMI (<29.9 vs ≥30 kg/m2), and they will participate from approximately 8 to 36 weeks of gestation. The sample size is based on GWG (primary outcome) and informed by our feasibility trial showing a 21% reduction in GWG in the intervention group compared to the control group, with 3% dropout. Secondary outcomes include PA, EI, sedentary and sleep behaviors, social cognitive determinants, adverse pregnancy and delivery outcomes, infant birth weight, and implementation outcomes. Analyses will include descriptive statistics, time series and fixed effects meta-analytic approaches, and mixed effects models. Recruitment started in April 2024, and enrollment will continue through May 2027. The primary (GWG) and secondary (eg, maternal and infant health) outcome results will be analyzed, posted on ClinicalTrials.gov, and published after January 2028. Examining the efficacy of the novel HMZ 2.0 intervention in terms of GWG and secondary outcomes expands the boundaries of current GWG interventions and has high clinical and public health impact. There is excellent potential to further refine HMZ 2.0 to scale-up use of the novel digital platform by clinicians as an adjunct treatment in prenatal care to regulate GWG in all pregnant women. DERR1-10.2196/66637.

Predictor-based observer and resilient controller design for aperiodic sampled-data systems with disturbance and output delay

In this paper, the control problem based on state/disturbance observers is studied for a class of networked aperiodic sampled-data systems with unknown disturbances and output delays. A novel observer structure is devised to estimate states and disturbances by predicting the actual output of the system. Moreover, the disturbance constraint is broadened to encompass wider types such as unbounded finite derivatives. Based on the obtained estimation of disturbance and state, a resilient controller is proposed to compensate for the impact caused by controller parameter perturbations. In particular, a new class of Lyapunov-like functionals is constructed to extend the sampling interval associated with exponential convergence. By employing matrix analysis and integration techniques, sufficient criteria are established to guarantee the exponential convergence of the networked aperiodic sampled-data closed-loop dynamics. The obtained criteria reveal that the estimation error of the disturbance depends only on the errors of the predictor and state observation, benefiting from the novel structure of the devised observer. The parameter gains of the observer and controller are readily determined by solving a set of convex optimisation constraints. The effectiveness and superiority of the proposed observer-based control algorithm are confirmed through developed examples.

Empowering telecommunication towers employing improved war strategy optimization method

In the field of telecommunication towers, specifically focusing on Base Transceiver Station (BTS) units, this research presents a revolutionary power supply system that is characterized by optimization and environmental cleanliness. The primary goal is to develop a reliable and continuous energy supply for these isolated units. In order to accomplish this objective, the use of PEMFCs (Proton Exchange Membrane Fuel Cells) is utilized. To provide a constant and controlled voltage of output from the PEMFC to the BTS, a proportional-integral (PI) controller based on improved war strategy optimization is used. The purpose of this controller is to improve the efficacy of the model and efficiently adjust to different operating situations. By conducting a comparison study with other methodologies, the suggested system showcases its advantages in terms of both efficiency and dependability. The research results presented in this study provide a substantial and noteworthy addition to the domain of sustainable energy solutions for communications infrastructure. Through the use of PEMFCs and the integration of modern control algorithms, the suggested system presents a very favorable method for supplying power to telecommunication towers. This approach not only enhances the operational efficiency of these towers but also contributes to the reduction of their environmental footprint.

Evaluation of FxLMS and FxRLS algorithms in active vibration control using piezoelectric stack actuators for gear transmission

Abstract Control algorithm plays a crucial role in active vibration control (AVC) for gear transmission. This study aims to evaluate the performance of adaptive control algorithms, filtered-x least mean square (FxLMS) and filtered-x recursive least square (FxRLS), in the AVC of gear transmission. A dynamic model incorporating shafts, gear pairs, and bearings is developed. Based on the vibration response of the dynamic model, a AVC system using piezoelectric stack actuators is designed. Theoretical analysis and numerical simulations are conducted to compare the optimal weight search trajectories of the two algorithms under various operating conditions. Results reveal that the FxRLS algorithm is less sensitive to the eigenvalue spread of the correlation matrix than the FxLMS algorithm. Co-simulations and experimental validation confirm the vibration suppression capabilities of the AVC system. While both algorithms effectively reduce vibrations, the FxRLS algorithm demonstrates superior convergence speed and vibration suppression performance.

Safety Control for Cyber–Physical Systems Under False Data Injection Attacks

Cyber–physical systems (CPSs) are increasingly susceptible to cyber threats, especially false data injection (FDI) attacks, which can compromise their stability and safety. Ensuring system safety while mitigating such attacks is a critical challenge. In the paper, we address the safety control issue for CPSs by designing a control strategy that considers both false data injection attacks and physical safety constraints. A baseline controller is first designed to guarantee system stability when there are no attacks and when the safety constraints are satisfied. To address FDI attacks, we propose a neural network-based estimator to detect and estimate the magnitude of such attacks. The attack estimate is then incorporated into the controller to dynamically adjust control actions, ensuring that the system remains stable and resilient to malicious interference. Furthermore, we introduce a safety control algorithm based on control barrier functions to enforce safety constraints, where the attack estimate is integrated to handle unknown attacks. Finally, the effectiveness of the proposed scheme is validated by simulation results, demonstrating that the combined control strategy outperforms traditional methods in both attack mitigation and safety enforcement.

Research on Path Tracking of Intelligent Hybrid Articulated Tractor Based on Corrected Model Predictive Control

To improve the path tracking performance of intelligent hybrid articulated tractors in all working conditions in unmanned operation, a path-tracking control method based on corrected model predictive control is proposed. The kinematic model of the tractor is established by analyzing the tractor’s kinematics. Taking the lateral and longitudinal errors as the target and the speed and articulation angular acceleration as the constraints, a tracking control algorithm based on model predictive control is proposed. In addition, to improve the transient performance of the tractor in the path tracking process, the proportional-integral-derivative controller and fuzzy controller are used to correct the model-predicted output articulation angular acceleration, forming a corrected model predictive control path tracking control method. To verify the effectiveness of the control method, model predictive control is used as a comparison method, and the effectiveness of the proposed method is verified based on the MATLAB 2024a simulation platform. The results show that compared with the MPC algorithm, the speed standard deviation is reduced by 2%, the longitudinal tracking error is reduced by 8%, and the lateral tracking error is reduced by 50%. The proposed method can effectively improve the path-tracking accuracy of the intelligent hybrid tractor.

Fuzzy control algorithm of cleaning parameters of street sweeper based on road garbage volume grading

The research examines the challenges city street sweepers face, which struggles to adapt cleaning settings based on varying road garbage volume, resulting in inefficient cleaning and high energy consumption. The study proposes a fuzzy control algorithm for adjusting the cleaning parameters of street sweepers based on road garbage volume grading. It starts by utilizing the YOLO (You Only Look Once) v5 deep learning model for target detection and garbage classification on road surfaces. The algorithm calculates road garbage volume grading coefficients based on the coverage and weight of different types of garbage. These coefficients, along with the street sweeper’s traveling speed, are used as input variables to develop a fuzzy control model for optimizing operational parameters such as the street sweeper disc brush and fan speeds. This model aims to achieve adaptive control of cleaning parameters, thereby enhancing the intelligence of street sweepers. Experimental results show that the proposed algorithm can achieve a 29.77% energy saving under the same road garbage conditions compared to the conventional gear control mode.

Smart Irrigation Control System in Precision Agriculture With Jubatus Climber Algorithm Optimized Distributed BiLSTM

ABSTRACTAgricultural productivity is crucial for population growth, but 83% of India's water usage is due to unplanned consumption. To address this, smart agriculture, which uses Internet of Things (IoT) technologies for efficient water management, is essential. Smart irrigation control is crucial for addressing water waste and promoting agricultural growth. To make agriculture smart, automation and IoT technologies emerged with the deep learning algorithms used in the research. Despite advancements, existing research models have drawbacks, including limited accuracy in predicting moisture content and inefficient resource utilization. To address the drawbacks of the existing techniques, a novel approach named distributed bidirectional long short‐term memory optimized with the Jubatus classifier algorithm (JCA‐DiBiLSTM) for automatic irrigation control is proposed. The proposed method integrates BiLSTM networks with a distributed computing architecture, enhancing predictive accuracy by capturing complex temporal dependencies in moisture content data. Additionally, the Jubatus classifier algorithm (JCA) optimizes the model parameters, improving the overall prediction performance. By leveraging JCA‐DiBiLSTM, significant advantages are observed in predicting the moisture content of agricultural land. The model demonstrates enhanced accuracy, outperforming traditional methods and mitigating over‐ or under‐irrigation risks. Furthermore, JCA‐DiBiLSTM ensures an accuracy and specificity of 94% and minimizes the mean square error (MSE) to 5.5 while maximizing the sensitivity to 94%.

Maximum Harvesting Power Algorithm in Magnetic Energy Harvester Considering Different Temperatures

A major challenge for practical magnetic energy harvesting (MEH) applications is achieving stable harvested power with high power density under a wide range of temperature variation. The amount of power harvested from the MEH is sensitive to ambient temperature because the characteristics of the magnetic material are greatly affected by temperature. From a practical point of view, previous studies have limitations because they do not consider thermal effects at all. In this paper, a novel control algorithm form maximum harvesting power in MEH is proposed by considering dynamic changes in temperature for the first time. In order to tackle this problem, a temperature-dependent B-H curve model is proposed, which considers the effect of temperature variation on the magnetic core. This study is the first to integrate thermal effects at the design stage of MEH. Theoretical analysis using the proposed B-H curve model demonstrates that the nonlinear behavior of magnetic materials can be accurately predicted under varying temperature conditions. Based on the above analysis, it was possible to extract the maximum harvested power while predicting shifts in the magnetic saturation point across a wide temperature range. Experimental results validate the effectiveness of the proposed design method, achieving a 26.5% higher power density compared to conventional methods that neglect thermal effects.

Prospects for Using Finite Algebraic Rings for Constructing Discrete Coordinate Systems

The method of non-standard algebraic extensions based on the use of additional formal solutions of the reduced equations is extended to the case corresponding to three-dimensional space. This method differs from the classical one in that it leads to the formation of algebraic rings rather than fields. The proposed approach allows one to construct a discrete coordinate system in which the role of three basis vectors is played by idempotent elements of the ring obtained by a non-standard algebraic extension. This approach allows, among other things, the identification of the symmetry properties of objects defined through discrete Cartesian coordinates, which is important, for example, when using advanced methods of digital image processing. An explicit form of solutions of the equations is established that allow one to construct idempotent elements for Galois fields GFp such that p−1 is divisible by three. The possibilities of practical use of the proposed approach are considered; in particular, it is shown that the use of discrete Cartesian coordinates mapped onto algebraic rings is of interest from the point of view of improving UAV swarm control algorithms.

Power Tracking and Performance Analysis of Hybrid Perturb–Observe, Particle Swarm Optimization, and Fuzzy Logic-Based Improved MPPT Control for Standalone PV System

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&amp;O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&amp;O-PSO, PSO-FL, and P&amp;O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&amp;O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches.

Voltage Control Nonlinearity in QZSDMC Fed PMSM Drive System with Grid Filtering

This publication investigates the control of output voltage-boosting in a Quasi-Z-Source direct matrix converter operating as part of a PMSM drive system with an RLC grid filter. The structure and control algorithms enabling regulation of the converter’s output voltage in both step-down and step-up modes are presented. These methods are based on the dq transformation, which provides a measurement signal for a linear PI-type controller. The article includes simulation results obtained using Matlab Simulink 2019a, which facilitated the preliminary verification of the applied structures and methods. The obtained model revealed the presence of nonlinearities in the Quasi-Z-Source voltage control system, which were subsequently confirmed through experimental verification. The system is stable but exhibits oscillatory behavior, with its parameters dependent on the amplitude of the step of the voltage gain coefficient. The voltage control system regulates the output voltage at least 10 times faster than a single period of the grid voltage sine wave. To enhance voltage control, a tunable controller with optimized parameters was proposed. The conducted studies demonstrated a 16.5% improvement in the IAE index and faster settling time for Quasi-Z-Source voltage control using the proposed controller compared to the reference controller.

Model-Free Adaptive Control for Attitude Stabilization of Earth-Pointing Spacecraft Using Magnetorquers

This paper presents an attitude stabilization algorithm for a Low Earth Orbit (LEO) Earth-pointing spacecraft using magnetorquers as the only torque actuators and employing Model-Free Adaptive Control (MFAC) as the control algorithm. MFAC is a data-driven control algorithm that relies solely on input–output data from the plant. This paper validates the effectiveness of the proposed approach through numerical simulations in a specific case study. The simulations show that the proposed algorithm drives the spacecraft’s attitude to three-axis stabilization in the orbital frame from arbitrary initial tumbling conditions. The numerical study also shows that the proposed control algorithm outperforms a model-based Proportional–Derivative (PD) control in terms of pointing accuracy at the expense of higher energy consumption.