Journal of Automation, Mobile Robotics and Intelligent Systems
Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS) is an international scientific quarterly published in Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP located in Warsaw, Poland. JAMRIS publishes since 2007 high-quality technical, position and state-of-the-art papers in the fields of theory, design and applications of broadly perceived intelligent technologies, notably based on artificial intelligence and computational intelligence, particularly for analysing and solving scientific and engineering problems and systems. The articles are published in English, and they are peer-reviewed and open-access. ISSN: 1897-8649 (Print) | ISSN: 2080-2145 (Online) | DOI: 10.14313/JAMRIS/
Łukasiewicz – Industrial Research Institute for Automation and Measurements PIAP
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A Custom Robotic Vehicle Prototype for Disinfecting Workplaces
The SARS‐CoV‐2 pandemic has heightened the need for advanced and automated disinfection methods to ensure workplace safety and hygiene. This study presents the de‐ sign and implementation of a robotic vehicle capable of autonomously disinfecting high‐risk areas in diverse work environments based on human activity levels. The sys‐ tem integrates a machine vision module using YOLOv5 for real‐time human detection, a Decision Support System to assess contamination risks, an autonomous navigation module for path planning, and a user‐friendly interface for operator control. By leveraging real‐time data, the robot precisely applies disinfectant to identified high‐risk zones, dynamically adjusting the spray volume based on the level of contamination. The system was validated in a real‐world workplace setting, demonstrating its ability to autonomously perform targeted disinfection, offering a scalable solution to support workplace hygiene.
Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
A Kinematic Prototype of a Compliant Artificial Prosthetic Knee Joint
This study presents the development and kinematic evaluation of a compliant artificial knee joint prototype fabricated using multi-material 3D printing. The design integrates a rolling-contact compliant mechanism—derived from the CORE and D-CORE concepts—into a prosthetic knee construction, aiming to replicate biological joint behavior while reducing weight, mechanical complexity, and friction. The prototype underwent iterative refinement, including geometric modifications, material selection for flexure bands, and structural asymmetry to mitigate overextension and increase durability. The resulting joint exhibited hybrid kinematic characteristics, blending features of both single-axis and polycentric knee mechanisms, and achieved a functional flexion range of approximately 142 degrees. Initial cyclic tests confirmed satisfactory stiffness and shape recovery of flexure bands with optimized dimensions, although long-term fatigue performance remains a challenge. A basic spring-damper system was also integrated, potentially aligning the prototype with K-1/K-2 prosthetic classification. However, full validation requires further mechanical testing in accordance with ISO 10328 standards, as well as optimization of the damping system for commercial viability. This research demonstrates the feasibility and potential of compliant mechanisms in lower-limb prosthetics while identifying critical areas for future development.
Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach
A Single-Input Single-Output (SISO) linear system can represent various physical systems. One of the challenges in controlling practical systems is that the settling point and settling time cannot be prescribed together. In this paper, a novel Self-Tuning Control (STC) algorithm is presented, which ensures system stability while allowing the simultaneous determination of the settling time and settling point. Four possible scenarios will be defined to investigate the effectiveness of the proposed control method. Three different practices will be described in the simulation section for each scenario. Linear Quadratic Regulation (LQR) will be employed for comparison purposes. MATLAB software will be used to test and simulate these practices. The simulations will demonstrate that the STC is an optimal solution for SISO linear systems, comparable to LQR, with the significant advantage of guaranteeing the attainment of the settling point at the predefined settling time.
Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
This study compares two artificial intelligence approaches for parking occupancy detection: computer vision and convolutional neural networks (CNN). A dataset of 1,000 parking images was captured and labeled, using OpenCV in Python for computer vision processing and the YOLO V5 model for CNN. Results showed that the YOLO V5 model achieved 88% precision and 82% sensitivity, outperforming the computer vision method, which achieved 80% precision and 79% sensitivity. The research suggests that while CNNs offer superior performance, computer vision is a more economical option in contexts with limited resources. Future research will focus on evaluating the YOLOv7 version to reduce false positives and combining techniques to achieve a balance between accuracy and efficiency under variable conditions.
Grey Wolf Optimization Algorithm for a Concurrent Real-Time Optimization Problem in Game Theory
In this paper we present a grey wolf algorithm for a concurrent real time optimization problem in searching for optimal game solving solution. There are many solutions to solve the game. Each solution can demand different optimal values of different parameters. However some ways in which the players try to solve the game do not lead to success. The optimization problem consists of two phases. Each phase Impacts the second one in real time. The first phase is responsible for the optimization parameters choice. The second phase validates the choice and makes the optimization of the parameters. As a optimizing method we chose grey wolf optimization. At the beginning the algorithm generates some number of solutions. The solution which has the value of the parameters the closest to maximum is a position of an alfa wolf. The rest of solutions are, according to the values of the parameters, split to be the positions of beta, delta and omega wolfs.
Autonomous Goal Following for Quadruped Robot using Fuzzy Proportional Control
In this paper a fuzzy Proportional (PD) Controller was designed and implemented for dynamically adapting the velocity and motion parameters in a quadruped robot for autonomously following a goal. The FNK0050 Freenove quadruped robot was utilized for the experiments, which has 12 degrees of freedom and this is why it has higher complexity. Experimental results show that the proposed fuzzy PD controller surpasses the standard PID controller provided as default by the manufacturer of the robot.
Mutual Inductance Model of the Mechanical Stress Sensitivity of the Power Transformer Functional Parameters
The mutual inductance model enables testing the behavior of power transformers under different operation conditions, especially under the environmental influences on the transformer’s core. This paper presents the investigation results of the stress dependence of the magnetic relative permeability of the power transformer. It was observed that under tensile mechanical stresses up to 98 MPa applied to the core, the transformer input current amplitude increases by almost 67%, whereas the transformer's reactive power increases by 53%. In industrial systems, such changes can potentially lead to unwanted power system shutdowns due to overloading. This effect should be considered during the development of critical power systems.
Copyright (c) 2024 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
Patterns of Acoustic Emission Changes with Alterations in the Damage Area of a Composite Material According to the Mises Criterion
In this paper, the use of a highly sensitive acoustic emission method for studying the deformation and failure processes of composite materials is considered, which provides a substantial amount of information about phenomena occurring at the sub-micro, micro, and macro levels. However, the additional influence of various factors leads to the problem of interpreting and identifying the recorded information. Addressing this problem involves determining the influence of different factors on acoustic emission signal parameters and their sensitivity to the influencing factor. In this study, during the failure of a composite material under transverse force according to the Mises criterion, an analysis is conducted on the impact of changes in the number of composite material elements (damage area) on the amplitude-time parameters of the acoustic emission signal based on a developed signal model. The results of the simulation allowed for the identification and description of patterns in the changes of amplitude-time parameters of acoustic emission signals (maximum amplitude, area under the signal curve, and signal duration) with variations in the number of composite material elements. These patterns enable the determination of the sensitivity of acoustic emission signal parameters to the influencing factor. The findings of this study may be of interest in the development of methods for monitoring, diagnosing, and predicting the failure of composite materials and products through the registration and analysis of acoustic emission signals.
Formation Control of Multi-agent Nonlinear Systems using the State-Dependent Riccati Equation
This work investigates the formation control of a multi-agent robotic system via the state-dependent Riccati equation (SDRE). The system's agents interact with each other and follow a leader in point-to-point motion control (regulation). The number of agents is unlimited in conventional multi-agent formation control considering a complex dynamic for each agent, though the complexities of the algorithms usually result in small-scale simulations. Here a formalism is proposed that considers fully coupled nonlinear dynamics for robotic systems in multi-agent system formation control with a large number of agents. The interaction of the agents with each other and obstacle avoidance are embedded in the design through the weighting matrix of states in the SDRE. The input constraint also limits the actuators to create a more realistic scenario. Two dynamical systems have been modeled and simulated in this work: wheeled mobile robots (WMR) and multirotor unmanned aerial vehicles (UAVs). The simulation results show success in the implementation of a total of 1,089 agents in the desired square formation shape in the UAV case study, and a figure of 45 agents and 1,050 differential wheeled mobile robots in the circular desired shape, considering obstacle avoidance and also collision avoidance between the agents.
Monocular 3D Object Localization using 2D Estimates for Industrial Robot Vision System
3D Object Localization has been emerging as one of the main challenges in Machine Vision tasks. In this paper, we proposed a novel 3D object localization method, leveraging a blend of deep learning techniques primarily rooted in object detection, post-image processing, and pose estimation algorithms. Our approach involves 3D calibration methods tailored for low-cost industrial robotics systems, requiring only a single 2D image input. Initially, object detection is performed using the You Only Look Once (YOLO) model, followed by an R-CNN model for segmenting the object into two distinct parts, i.e., the top face and the remainder. Subsequently, the center of the top face is served as an initialization position, and being refined with a novel calibration algorithm. Experimental results demonstrate a notable reduction in localization error by 87.65% when compared to existing methodologies.
Multi-Input Melanoma Classification using MobileNet-V3-Large Architecture
All over the globe there exists a serious problem with skin cancer, most especially melanoma; a malignancy which is known to behave aggressively and able to metastasize. Detecting this early is the key to saving life. This study introduces a new method of classifying melanoma using an advanced model known as MobileNet-V3-Large. This technique differs from others in that it considers both the images of the skin lesion and tabular data including factors consisting of patient’s approximate age, gender and the location of the lesion on the body of the patient is considered here. Such an approach empowers the predictions of whether skin lesion may be malignant or benign. Tested on huge collection consisting of skin images combined with the tabular data, it was established that the method outperforms others already existing. The results of this study showed that a high accuracy of 99.56% was achieved using the proposed model. The study indicates that utilizing multi-input method will substantially enhance diagnosis for melanoma hence reducing mortalities in the future.
Optimization of the Nominal Capacity of the Energy Storage System for Ensuring Security of Electrical Energy Supply
This paper presents an optimization model for determining the nominal capacity of an energy storage system is presented, which transfers excess amounts of electrical energy from solar power plants as part of a group of distributed generation power plants, based on the criterion of the minimum cost of supplying electricity to end consumers. The use of the developed optimization model allows to determine the optimal parameters of the energy storage system to achieve an economic balance between the excess amounts of electric energy of solar power plants that are transferred and the amount of restrictions on inverters that are subject to payment. Based on the developed method, based on the forecast level of electric energy consumption and statistical data on the level of electric energy production at wind and solar power plants for the territory of Ukraine, the installed capacity of the wind, solar, reserve power plant and the nominal capacity of the two-hour energy storage system were determined, which will provide the minimum levelized cost of energy of supplying to consumers during the entire period of commercial operation of a group of distributed generation power plants.
Health Recommender System for Sleep Apnea using Computational Intelligence
In health care, there is a growing interest on building recommendation systems for sleep apnea management. These systems use data from a variety of sources, including patient-reported outcomes and electronic health records, to assess sleep quality, breathing patterns, and medical treatment adherence. Leveraging artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and cloud platforms, the system analyzes these data to uncover patterns and correlations. It then creates individualized patient profiles that incorporate details about diet, medical history, and sleep habits. Based on these profiles, customized recommendations are generated to enhance sleep apnea management. These recommendations may encompass treatment options and lifestyle adjustments, Yoga, exercise, etc. to improve treatment effectiveness and overall well-being for individuals with sleep apnea. This review article discusses available literature on sleep apnea, its diagnosis, and the role played by ML and deep learning classifiers in the prediction and classification of the disease. The article also presents a comparative analysis on performance measures for these methods. This article highlights the research scope for incorporating technologies such as AI, the IoT, and computational intelligence in improving the diagnosis, remote monitoring, and treatment of sleep apnea.
Identification of Keywords for Legal Documents Categories using SOM
This study aims to use the decision-making process in categorizing legal documents by identifying keywords characterizing each legal domain class. The study utilizes the Kohonen Self-Organizing Map method and the Global Vectors for Word Representation (GloVe) model to create an efficient document classification system. As a result, a satisfactory classification accuracy of 71.69% was achieved. The article also discusses alternative approaches implemented to improve classification accuracy, such as the use of Named Entity Recognizer (NER) tools and the RoBERTa model, along with a comparison of their effectiveness. Challenges related to the uneven distribution of categories in the dataset are also mentioned, and potential directions for further research to enhance the classification results of legal documents are presented.
The Design of 3D-Printed Open Bearings for Human Assisting Robots
Wearable technologies, including exoskeletons, significantly impact supporting the motion of people with disabilities. However, activating internal/external rotations in extremity segments requires placing body segments inside bearings, as their rotation axes must overlap. This hinders the exoskeleton mounting process and even excludes some medical cases from using the devices. For this reason, the design of innovative 3D-printed open bearings was presented in this paper. It consists of anthropometric modelling, computer-aided mechanical design, multibody dynamics simulations, strength analysis, and parametric optimisation to obtain minimal mass while complying with the strength requirements. The design process resulted in reducing the overall mass of the design by 40%. Moreover, it proved that the powder additive manufacturing techniques combined with the thin sliding layers printed with FFF/FDM technology are more appropriate for the intended use than monoliths manufactured with FFF/FDM technology. The presented methodology is universally applicable to other robots interacting with humans, which require the use of open bearings without drives, also if manufactured subtractively.
Implementation of Enzyme Family Classification by using Autoencoders in a Study Case with Imbalanced and Underrepresented Classes
In the field of Bioinformatics, the scientific community is fully aware of the challenges associated with enzyme classification. In this study, a novel strategy is proposed based on the use of Anomalous Autoencoders to characterize chitinases belonging to glycoside hydrolases. Python and TensorFlow programming technologies were employed to conduct this analysis. The designed classifier consists of two levels that determine both the enzymatic nature of an amino acid sequence and its corresponding chitinase enzyme family. These levels considered class imbalance and the underrepresentation of those enzyme families in the CAZy.org database. Furthermore, a comprehensive comparison was made with other available software in the field. To represent the amino acid sequences, embeddings generated from the ProtFlash model were used. The results obtained in this study confirm the effectiveness of the proposed implementation compared to the methods EzyPred, ECPred, and Proteinfer.
Construction Automation with Bio-inspired Hierarchical Extremely Modular Systems
This paper presents the concept of hierarchical extremely modular systems (EMS). The biology-inspired nomenclature, genetic encoding, and operations for this class of structures is introduced and illustrated with various examples. Four mutation types are introduced and briefly analyzed. A relatively good convergence of the evolution strategy-based algorithm applied for optimization of EMS is shown.
Controllability, Observability and Transfer Matrix Zeroing of the 2D Roesser Model
The article considers the fundamental properties of the two-dimensional (2D) system described by the Roesser model. The controllability and observability are analyzed and the sufficient conditions under which the transfer matrix is zero are given. It is shown that if the matrix of the state equation A and B or A and C of the Roesser model has full row rank (respectively, full column rank) then there exists a nonsingular matrix of transformation such that the new pair of new matrices is controllable (observable). The numerical examples are given to show the correctness of the obtained conditions.
Comparison of Open Source SDN Controllers and Cloud Platforms in Terms of Performance, Stability, and Infrastructure Flexibility
The IT industry is advancing rapidly, with virtually every branch of modern computing experiencing swift development. Concepts such as Cloud Computing and Artificial Intelligence no longer surprise anyone. Recently, Software Defined Networks (SDN) have been gaining significant popularity. This innovative approach to computer networks allows for greater flexibility and is, therefore, much more well-known in the world of cloud computing than in traditional network implementations. This paper introduces the concept of SDN and Network Functions Virtualization (NFV) and outlines all the challenges and security issues associated with the cloud environment. The dynamic nature of the IT landscape requires constant adaptation to emerging technologies, and SDN represents a noteworthy evolution in the realm of computer networking. Platforms such as SDN and open-source tools enabling the creation of private cloud environments such as OpenStack or OpenNebula were compared. At the same time, aspects like security, network performance, flexibility, and scalability were analyzed. Based on the prior analysis, a comprehensive cloud environment was built using the OpenStack solution and SDN - OpenDaylight was deployed. Additional tests conducted on the OpenStack cloud, both with and without SDN, demonstrated the superiority of SDN implementation in the cloud.
Segmentation of E-Commerce Users on Cart Abandonment and Product Recommendation Using Double Transformer Residual Super-Resolution Network
A novel approach named "Segmentation of E-commerce users on Cart Abandonment with Product Recommendation using Double Transformer Residual Super-resolution Network (SEC-CAPR-DTRSRN)" is proposed. This study commences with the collection of e-commerce data from a diverse multi-category store. The pre-processing phase uses Fairness-aware Collaborative Filtering (FACF) to suggest personalized items or content to users based on their preferences with behavior. Following pre-processing, the data undergoes segmentation using the Generalized Intuitionistic Fuzzy c-means Clustering (GIFCMC) technique to categorize users to target-based customer groups. To enhance cart transactions, Double Transformer Residual Super-resolution Network (DTRSN) is introduced for product recommendations. The exploration of cart abandonment identifies potential discord stemming from a misalignment between consumer perceptions and digital site experiences, potentially leading to cart abandonment due to user annoyance. While the DTRSN lacks an explicit adoption of optimization systems for calculating optimal parameters, the manuscript proposes the integration of Polar Coordinate Bald Eagle Search Algorithm (PCBESA). PCBESA is introduced to optimize Polar Coordinate Bald Eagle Search Algorithm DTRSN, ensuring precise product recommendations. The proposed (SEC-CAPR-DTRSRN) method is implemented, and their performances is rigorously evaluated using key metrics, including mean square error, standard deviation, and mean reciprocal rank (MRR). The proposed method gives 12.78%, 29.85% and 17.45% lower mean square error and 23.67%, 28.86% and 16.45% higher MRR with existing techniques like segmentation of E-commerce users depend on cart abandonment with product recommendation using collaborative filtering: moderating effect of exorbitant pricing (SECU-CAPR-CF), new top-n recommendation technique for multi-criteria collaborative filtering (PR-MCCF-EC) and reinforcement learning E-commerce cart targeting to reduce cart abandonment in E-commerce (RL-EC-RCA) methods, respectively.
double transformer residual super‐ resolution network
polar coordinate bald eagle search algorithm
Development of a Distributed Outlier Detection Method based on the Alternating Direction Method of Multipliers
In data mining, one of the most studied problems is outlier detection, which involves identifying "unusual" data points within a dataset that are suspected to be generated by a different mechanism than the rest of the dataset. Outlier detection has applications in discovering novel information, detecting bank fraud, and identifying system intrusions, and others. However, handling large volumes of data, known as big data, poses a challenge to outlier detection algorithms because the resources of a single computer may not be sufficient to achieve efficient performance. Furthermore, datasets are often stored in distributed environments. The goal of this work is to develop a new distributed outlier detection algorithm based on the solution of the support vector data description using the alternating direction method of multipliers. Mathematical optimization methods and Python language libraries are mainly used for the implementation. As a result, the design and distributed implementation of the proposed algorithm are achieved, which are validated using several test datasets, yielding satisfactory and competitive results compared to existing methods.
A Novel Model-Free Control Technique for Angular Motion of Single Ducted-Fan Unmanned Aerial Vehicle
This paper reveals the proposed method to operate landing angular motion of ducted-fan unmanned aerial vehicle (DUAV). The angular motion frequently varies during the landing stage. Additionally, the DUAV system is a complex system with uncertain parameters or incorrectly identified parameters, and the yaw angle has to be controlled in the proper position before grounding. Because of issues with structure of the system and identification in the real model of DUAV, a model-free control technique is approached by combining time-delay estimation (TDE) and integral sliding mode law (ISMC). Thanks to the TDE technique provides a model-free method for the complex system as DUAV. Hence, a novel control method is designed to achieve the desired angular motion. Besides, the ISMC method is a good solution for tracking performance. The stability of the whole system is guaranteed by the Lyapunov theory. The comparison between the TDE-ISMC and sliding mode control (SMC) is conducted in several cases to verify the effectiveness of the proposed TDE-ISMC control.
Model-Based Development of Autopilot for a Gasodynamically Controlled High-Speed Unmanned Aerial Vehicle
In recent years, model-based design and automatic code generation have gained popularity in various applications. However, using this approach in some specialized safety-critical applications is still challenging because the code must fulfill rigorous requirements. In this paper, the methodology of development of the software autopilot for the surface-to-surface guided projectile is presented in detail. The missile is actuated only with 32 solid propellant lateral motors, which makes the control task challenging. MATLAB and Simulink were used to develop the detailed simulation of the missile together with the control software. A Model-in-the-Loop testing was evaluated to achieve appropriate autopilot performance. Embedded Coder was applied to generate production-ready C code from the model. A custom test framework was created to accelerate the design process. The numerical equivalency of the Simulink model and C code was investigated extensively using Software-in-the-Loop and Processor-in-the-Loop simulations. The developed control algorithm was implemented on real hardware with ARM Cortex M4 microcontroller. The integrated prototype of the projectile control system was successfully tested in laboratory conditions by Hardware-in-the-Loop simulation. The scientific significance of this paper lies in a comprehensive description of the methodology that might be used in the external ballistics area.
Atlantic Blue Marlin, Boops, Chironex Fleckeri, and General Practitioner – Sick Person Optimization Algorithms mization Algorithms
In this paper Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri Search Optimization (CSO) algorithm, General practitioner -Sick person (PS) optimization algorithm are applied for solving factual power loss reduction problem. The regular actions of Atlantic blue marlin are emulated to design the Atlantic blue marlin (ABM) optimization algorithm and populace in the examination space is capriciously stimulated. Boops optimization (BO) algorithm is designed by imitating the actions of Boops. Boops possess the obliging stalking physiognomies. As a cluster they stalk the quarry by forming the key and subordinate clusters CSO is based on the drive and search behaviour of Chironex fleckeri. Obviously Chironex fleckeri will exploit the limbs to paralyze the pray by injecting the rancour. In real time world a General practitioner will treat the Sick person with various procedures and it has been imitated to model the Projected PS algorithm. In general people will be inoculated and then with respect to disorder and disease- medical treatment will be given by medicines. Inoculation, medicine and operation are the procedures have been considered as the phases of the projected PS algorithm. Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri Search Optimization (CSO) algorithm, General practitioner -Sick person (PS) optimization algorithm validated in IEEE 57, 300 systems and 220 KV network. Factual power loss lessening, power divergence restraining, and power constancy index amplification has been attained.
Design of Fault-tolerant Structures for Underwater Sensor Networks based on Markov Chains
Recently, underwater sensor networks (USN) have gained widespread adoption, proving instrumental in diverse research endeavors within the marine environment and across various scientific and technological domains. Considering the challenging operational conditions, a paramount issue in the present day is the creation of USNs with a predetermined fault tolerance margin. The primary focus of this study is the development and evaluation of a design method for fault-tolerant structures of USNs using Markov chains. The proposed method enables the creation of suitable USNs’ structures, ensuring a guaranteed attainment of the desired number of operational cycles before failure at reasonable costs. Utilizing the mathematical framework of Markov chains, coupled with the strategy of a well-selected sequence of hierarchy levels, enables the precise determination of the network’s actual number of operation cycles before failure as well as perform the efficient reservation without excessive redundancy. To evaluate the efficacy of the proposed method, this study undertakes a specific case – the development of a fault-tolerant structure for a real USN with predetermined parameters. The analysis of the results obtained confirms the high feasibility of utilizing the developed method for crafting USNs for various purposes, ensuring a predefined level of fault tolerance while maintaining acceptable costs.
Design of Small-Phase Time-Variant Low-pass Digital Fractional Differentiators and Integrators
The design method and the time-variant FIR architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.
Integrating Disturbance Handling into Control Strategies for Swing-up And Stabilization of Rotary Inverted Pendulum
The rotary inverted pendulum (RIP) is an underactuated mechanical system with fewer input controls than output controls. The application of the RIP model is to investigate the control of nonlinear systems and in other fields as well, as it is simple to analyze the dynamics and test despite its high nonlinearity. The two fundamental control issues in the RIP are achieving the desired balance position of the pendulum and maintaining stability. The energy-based swing-up controller is used for the model to bring the pendulum to an upright position. Regarding the issue of stability control, the LQR linear controller is well-known for its effectiveness and good stability, but it lacks stability in the presence of disturbance. The sliding mode controller (SMC) is able to resist the impact of disturbances when they affect the model. Therefore, this paper combines both controllers to address the balancing stability problem of the RIP system. The LQR-based SMC controller uses the LQR controller as the basic controller to stabilize the pendulum and employs the SMC controller to resist the impact of disturbance. In addition, it is necessary to accurately estimate the velocity of the pendulum and arm in order to apply them to the real model. This paper designs an observer to solve this problem. The simulation results show that the proposed controller performs well in the presence of input disturbance.
Design of Multidimensional Nonlinear Predictive Controller for 3D Crane
The paper presents a comprehensive approach to design and implementation of a multidimensional nonlinear control system for a 3D crane. For design purposes, a simulation model of the crane is developed and verified. Proposed are two structures of the control system, which are based on PID controller and predictive control system. The synthesis process is presented. The designed systems are verified in terms of their effectiveness, based on the judgement on the obtained waveforms of controlled variables and integral control indicators. Finally, the two systems are compared with each other, and the conclusions regarding their applicability for this type of system are presented.
Gradient Scale Monitoring for Federated Learning Systems
As the computational and communicational capabilities of edge and IoT devices grow, so do the opportunities for novel Machine Learning solutions. This leads to an increase in popularity of Federated Learning (FL), especially in cross-device settings. However, while there is a multitude of ongoing research works analyzing various aspects of the FL process, most of them do not focus on issues of operationalization and monitoring. For instance, there is a noticeable lack of research in the topic of effective problem diagnosis in FL systems. This work begins with a case study, in which we have intended to compare the performance of four selected approaches to the topology of FL systems. For this purpose, we have constructed and executed simulations of their training process in a controlled environment. We have analyzed the obtained results and encountered concerning periodic drops in the accuracy for some of the scenarios. We have performed a successful reexamination of the experiments, which led us to diagnose the problem as caused by exploding gradients. In view of those findings, we have formulated a potential new method for the continuous monitoring of the FL training process. The method would hinge on regular local computation of a handpicked metric - the gradient scale coefficient (GSC). We then extend our prior research to include a preliminary analysis of the effectiveness of GSC and average gradients per layer as potentially suitable for FL diagnostics metrics. In order to perform a more thorough examination of their usefulness in different FL scenarios, we simulate the occurrence of the exploding gradient problem, vanishing gradient problem and stable gradient serving as a baseline. We then evaluate the resulting visualizations based on their clarity and computational requirements. We introduce a gradient monitoring suite for the FL training process based on our results.