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Computational Intelligence on engineering/Cyber Physical systems (CIES), IEEE Symposium on

作者： Halil Ibrahim Ugurlu Adrian Redder Erdal Kayacan Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Automatic Control Paderborn University Paderborn Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

The inherent black-box nature of deep reinforcement learning (DRL) poses challenges in ensuring safety constraints. This paper, therefore, introduces a DRL reward design inspired by Lyapunov stability theory for safe robot navigation in the presence of obstacles. The navigation problem is formulated as a state-space control problem with close obstacle locations integrated into the state representation. To ensure safe obstacle avoidance, we introduce a novel reward-shaping strategy utilizing a Lyapunov function that discourages fast movement toward obstacles. Our numerical experiments demonstrate the effectiveness of the reward design strategy compared to baselines in achieving consistent superior learning with higher mission completion rates while maintaining speeds closer to a desired target speed. In addition, we show that our reward design enables a generally smaller choice for the discount factor for value-function-based DRL algorithms, which can lead to faster convergence. This is possible since the reward design merely penalizes the one-step decay of the Lyapunov function. Furthermore, policy training simulations employ an early episode termination method to constrain exploration and add more valuable samples to the DRL training replay memory. Finally, real-world experiments with a quadrotor validate the ability of our method to safely navigate around varying densities of obstacles. The proposed method consistently takes cautious maneuvers near obstacles by slowing down, achieving greater obstacle clearance compared to baseline, although with an increase in mission completion time.

关键词： Training Navigation Deep reinforcement learning Safety Planning Collision avoidance Robots Lyapunov methods Quadrotors Convergence

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An Interacting Multiple Model Correntropy Kalman Filter Approach for Unmanned Aerial Vehicle Localisation 16

An Interacting Multiple Model Correntropy Kalman Filter Appr...

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16th International Conference on INnovations in Intelligent systems and Applications, INISTA 2022

作者： Candan, Fethi Beke, Aykut Shen, Chen Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Aselsan Department of Control Systems Design Ankara06750 Turkey 310018 China

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

This paper compares a conventional interacting multiple model Kalman filter (IMM-KF) filter and an interacting multiple models with maximum correntropy Kalman filter (IMM-MCKF). A nonlinear UAV dynamics model was used to compare these two methods. The compared filters estimated the position of the UAV under the noise distribution. Although KF has reliable accuracy, MCKF has got better results under non-Gaussian or mixed distributions. At this point, these filters have been investigated under maneuver and non-maneuver motion, and it is known that better advantages will be shown when both filters are used in the IMM. These filters have been compared under non-Gaussian distributions, and the Student's-T distribution has been selected as a non-Gaussian type. The performance validation and testing stages are carried out with variable degrees of freedom, and scaling matrix factors for the Student's-T distributions have been used. Results from simulation tests from 3000 independent Monte-Carlo runs are presented. In these experiments, UAV models and UAV trajectory results have been used. © 2022 IEEE.

关键词： Kalman filters

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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A Unifying Complexity-Certification Framework for Branch-and-Bound Algorithms for Mixed-Integer Linear and Quadratic Programming

arXiv

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arXiv 2025年

作者： Shoja, Shamisa Arnström, Daniel Axehill, Daniel Division of Automatic Control Department of Electrical Engineering Linköping University Sweden Division of Systems and Control Department of Information Technology Uppsala University Sweden

In model predictive control (MPC) for hybrid systems, solving optimization problems efficiently and with guarantees on worst-case computational complexity is critical to satisfy the real-time constraints in these applications. These optimization problems often take the form of mixed-integer linear programs (MILPs) or mixed-integer quadratic programs (MIQPs) that depend on system parameters. A common approach for solving such problems is the branch-and-bound (B&B) method. This paper extends existing complexity certification methods by presenting a unified complexity-certification framework for B&B-based MILP and MIQP solvers, specifically for the family of multi-parametric MILP and MIQP problems that arise in, e.g., hybrid MPC applications. The framework provides guarantees on worst-case computational measures, including the maximum number of iterations or relaxations B&B algorithms require to reach optimality. It systematically accounts for different branching and node selection strategies, as well as heuristics integrated into B&B, ensuring a comprehensive certification framework. By offering theoretical guarantees and practical insights for solver customization, the proposed framework enhances the reliability of B&B for real-time application. The usefulness of the proposed framework is demonstrated through numerical experiments on both random MILPs and MIQPs, as well as on MIQPs arising from a hybrid MPC problem. © 2025, CC BY.

关键词： Branch and bound method

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MOVING HIGHER-ORDER TAYLOR APPROXIMATIONS METHOD FOR SMOOTH CONSTRAINED MINIMIZATION PROBLEMS

arXiv

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arXiv 2024年

作者： Nabou, Yassine Necoara, Ion Automatic Control and Systems Engineering Department University Politehnica Bucharest Romania Automatic Control and Systems Engineering Department University Politehnica Bucharest Gheorghe Mihoc - Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest Romania

In this paper, we develop a higher-order method for solving composite (non)convex minimization problems with smooth (non)convex functional constraints. At each iteration, our method approximates the smooth part of the objective function and of the constraints by higher-order Taylor approximations, leading to a moving Taylor approximation method (MTA). We present convergence guarantees for the MTA algorithm for both, nonconvex and convex problems. In particular, when the objective and the constraints are nonconvex functions, we prove that the sequence generated by MTA algorithm converges globally to a KKT point. Moreover, we derive convergence rates in the iterates when the problem’s data satisfy the Kurdyka-Lojasiewicz (KL) property. Further, when the objective function is (uniformly) convex and the constraints are also convex, we provide (linear/superlinear) sublinear convergence rates for our algorithm. Finally, we present an efficient implementation of the proposed algorithm and compare it with existing methods from the literature. © 2024, CC BY.

关键词： Approximation algorithms

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Aspects of Using Rotating Modes of Hydraulic Units in Pump Complexes 5

Aspects of Using Rotating Modes of Hydraulic Units in Pump C...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Korenkova, Tetyana Kovalchuk, Viktoriya Qawaqzeh, Mohamed Zaidan Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk39600 Ukraine Al-Balqa' Applied University Department of Electrical and Electronics Engineering Al-Salt66110 Jordan

ISBN: (纸本)9798350359787

It is shown that pumping complexes of industrial and communal water supply are complex energy-intensive objects with low controllability in both normal and emergency modes of operation. It is proposed to use reversible modes of operation of turbomechanisms to increase the controllability of pump complex equipment. The structure of the system of active regulation of pump complex parameters with various options for including the adjustable hydroturbine unit has been developed. It is shown that active devices can perform both regulatory and protective functions. A device for controlling and protecting the pumping unit against water hammer based on an active water flow energy quencher is proposed. The algorithm of operation of the software control device of the electrohydraulic equipment protection system against excess pressure in the hydraulic network has been developed. © 2023 IEEE.

关键词： controllability

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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A Machine-Learning Approach for Prognosis of Oscillating Water Column Wave Generators

A Machine-Learning Approach for Prognosis of Oscillating Wat...

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Circuits, systems, Communications and computers (CSCC), International Conference on

作者： Izaskun Garrido Jon Lecube Fares Mzoughi Pavam Aboutalebi Irfan Ahmad Salvador Cayuela Ailtor Garrido Department of Automatic Control and Systems Engineering Automatic Control Group ACG University of the Basque Country-UPV/EHU Bilbao Spain

Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the generated power and reducing the life expectancy. The problem of generator deterioration has been considered for the Mutriku MOWC plant and a machine learning-based approach for prognosis and fault characterization has been proposed. In particular, the use of k-Nearest Neighbors (kNN) models for predicting the time to failure of OWC generators has been proposed. The analysis is based on data collected from sensors that measure various operational parameters of the turbines. The results demonstrate that the proposed kNN model is an excellent choice for reducing maintenance costs by enabling scheduling months in advance. The model's high accuracy in predicting generator failures allows for timely and cost-effective maintenance, preventing costly breakdowns and improving turbine efficiency. These results highlight the potential of machine learning-based approaches for addressing maintenance challenges in the energy sector and underscore the importance of proactive strategies in reducing operational costs and maximizing energy production.

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Parallel Domain-Decomposition Algorithms for Complexity Certification of Branch-and-Bound Algorithms for Mixed-Integer Linear and Quadratic Programming

arXiv

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arXiv 2025年

When implementing model predictive control (MPC) for hybrid systems with a linear or a quadratic performance measure, a mixed-integer linear program (MILP) or a mixed-integer quadratic program (MIQP) needs to be solved, respectively, at each sampling instant. Recent work has introduced the possibility to certify the computational complexity of branch-and-bound (B&B) algorithms when solving MILP and MIQP problems formulated as multi-parametric MILPs (mp-MILPs) and mp-MIQPs. Such a framework allows for computing the worst-case computational complexity of standard B&B-based MILP and MIQP solvers, quantified by metrics such as the total number of LP/QP iterations and B&B nodes. These results are highly relevant for real-time hybrid MPC applications. In this paper, we extend this framework by developing parallel, domain-decomposition versions of the previously proposed algorithm, allowing it to scale to larger problem sizes and enable the use of high-performance computing (HPC) resources. Furthermore, to reduce peak memory consumption, we introduce two novel modifications to the existing (serial) complexity certification framework, integrating them into the proposed parallel algorithms. Numerical experiments show that the parallel algorithms significantly reduce computation time while maintaining the correctness of the original framework. © 2025, CC BY.

关键词： Integer linear programming

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Learning-to-control relaxation systems with the step response

Learning-to-control relaxation systems with the step respons...

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American control Conference (ACC)

作者： Ross Drummond Hamed Taghavian Pablo R. Baldivieso-Monasterios The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The problem of learning-to-control relaxation systems from data is considered. It is shown that the equi-librium of the relaxation system's step response defines the solution of a class of robust control problems and provides a good suboptimal solution to a class of linear quadratic regulator problems. These results demonstrate the potential to efficiently learn policies for these control problems from a single, easy-to-implement trajectory data point, being the step response. More broadly, these results highlight how the system structure and problem definition of the control problem can be exploited to generate data efficient learning- to-control methods.

关键词： Robust control Linear systems Regulators System dynamics Optimal control Trajectory

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