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State-Feedback control Design for Polynomial Discrete-Time Systems obtained via Second-Order Runge-Kutta Discretization

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IFAC-PapersOnLine 2024年第5期58卷 13-19页

作者： Priscila F.S. Guedes Márcio J. Lacerda Erivelton Nepomuceno Control and Modelling Group (GCOM) Department of Electrical Engineering Federal University of São João del-Rei - UFSJ São João del-Rei MG Brazil Centre for Communications Technology School of Computing and Digital Media London Metropolitan University UK Centre for Ocean Energy Research Department of Electronic Engineering Maynooth University Ireland

This paper addresses the state-feedback control problem for the class of state-polynomial discrete-time systems. The continuous-time polynomial nonlinear model is discretized by the second-order Runge-Kutta method. The Lyapunov theory and the exponential stability were employed to derive the conditions. The sum of squares formulation was used to check the constraints. Two approaches are presented, the first makes use of the Lyapunov function to recover the gain matrices. While the second formulation allows the design of rational state feedback control gains. We evaluated the impact of the step size used in the discretization process in the results. Numerical experiments were used to illustrate the potential of the proposed technique.

关键词： State-feedback control State polynomial systems Second-order Runge-Kutta method Computer arithmetic

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Fractional calculus in modelling hereditariness and nonlocality in transmission lines 11

Fractional calculus in modelling hereditariness and nonlocal...

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11th International Conference of the Balkan Physical Union, BPU 2022

作者： Zorica, Dušan Cvetićanin, Stevan M. Rapaić, Milan R. Department of Physics Faculty of Sciences University of Novi Sad Trg D. Obradovića 4 Novi Sad Serbia Mathematical Institute Serbian Academy of Arts and Sciences Kneza Mihaila 36 Beograd Serbia Department of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences University of Novi Sad Trg D. Obradovića 6 Novi Sad Serbia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg D. Obradovića 6 Novi Sad Serbia

Transmission lines are traditionally modelled by considering Heaviside's elementary circuit that contains a resistor and inductor in the series branch, accounting for the energy losses and magnetic effects, while the shunt branch contains a resistor and a capacitor, accounting for the energy losses and capacitive phenomena. Classical telegrapher's equations, modelling the signal propagation in a transmission line, are obtained by assuming the infinitesimal length of the elementary circuit and by passing to a continuum. The generalization of elementary circuit is two-fold: topological by adding the capacitor in the series branch in order to account for the charge accumulation effects along the line and constitutive in order to account for the memory effects that transmission line may display. The constitutive generalization is performed by changing the constitutive relation describing the capacitive and inductive material properties using the fractional calculus approach accounting for the short-tail memory. On the other hand, the inclusion of nonlocal material properties of a transmission line is performed by considering the magnetic coupling of inductors in the series branch of Heaviside's elementary circuit, so that the magnetic flux is obtained as a superposition of local and constitutively given nonlocal magnetic flux through the cross-inductivity kernel. Signal propagation is studied in the case of power, exponential, and Gauss type cross-inductivity kernels. The presented results are published in [1-3]. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Electric lines

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Data-Driven Predictive control for Robust Exoskeleton Locomotion

Data-Driven Predictive Control for Robust Exoskeleton Locomo...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kejun Li Jeeseop Kim Xiaobin Xiong Kaveh Akbari Hamed Yisong Yue Aaron D. Ames Department of Computation and Neural Systems Caltech Pasadena CA USA Department of Mechanical and Civil Engineering Caltech Pasadena CA USA Department of Mechanical Engineering University of Wisconsin-Madison Madison WI USA Department of Mechanical Engineering Virginia Tech VA USA Department of Computing and Mathematical Sciences Caltech Pasadena CA USA Department of Control and Dynamical Systems Caltech Pasadena CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

关键词： Adaptation models Analytical models Perturbation methods Exoskeletons Predictive models Hardware Trajectory Numerical models Predictive control Payloads

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation 4

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Classification of Software Developer Skills Using Analysis of Russian Job Ads

Classification of Software Developer Skills Using Analysis o...

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2022 Ural Symposium on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2022

作者： Shadrin, Denis Sozykin, Andrey Kuklin, Evgeniy Bersenev, Alexander Ural Federal University Engineering School of IT Telecom. and Control Systems Ekaterinburg Russia N.N. Krasovskii Institute of Mathematics and Mechanics Department of Computing Systems Ekaterinburg Russia Ural Federal University Institute of Natural Sciences and Mathematics Ekaterinburg Russia

ISBN: (数字)9781665460927

ISBN: (纸本)9781665460927

In this paper we present our findings in IT job market analysis and share our ideas of how they could be applied for designing new software development educational programs. This is relevant the current situation, when university programs cannot keep up with the changes in technologies and skills that employers require, also be constrained by Russian professional standards. To solve the problem of compatibility of educational programs with labor market requirements, we propose to create flexible educational programs based on the most common competencies in the most popular professions found in IT job advertisements. In total, we analyzed about 380000 IT vacancies for 2021-2022 downloaded from HeadHunter, the top Russian job website. The paper describes our results in classification of software engineering skills extracted from the job ads, and how one can apply these results for teaching new professions based on these skills. © 2022 IEEE.

关键词： Data mining

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Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode control

Robust Voltage Regulation for DC Microgrids via Passivity-Ba...

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International Workshop on Variable Structure Systems, VSS

作者： Edoardo Vacchini Michele Cucuzzella Pablo Borja Antonella Ferrara Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen (ENTEG) Faculty of Science and Engineering University of Groningen Groningen The Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth U.K.

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a decentralized sliding mode control approach to the voltage regulation problem in a DC microgrid consisting of distributed generation units interconnected with each other through resistive-inductive power lines and supplying unknown nonlinear loads. In particular, the port-Hamiltonian structure of the system suggests the design of a suitable sliding manifold such that the system on this manifold exhibits desired passivity properties. This approach simplifies the control design and relaxes some restrictive assumptions required by other controllers proposed in the literature, while offering satisfactory performance.

关键词： Manifolds Variable structure systems Power transmission lines Microgrids Power system stability Stability analysis Robustness Reduced order systems Voltage control Sliding mode control

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Distributed Load Shedding via Differentially Private Average Consensus Algorithm

Distributed Load Shedding via Differentially Private Average...

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第34届中国控制与决策会议

作者： Zixin Qiao Fanghong Guo Xiping Pan Yue Sun Lei Wang Shanghai Institute of Computing Technology Department of Automation Zhejiang University of Technology College of Control Science and Engineering Zhejiang University

This paper presents a distributed load shedding strategy for islanded active distribution power system,where the average consensus algorithm acts as a key tool to discover the required global information for load *** privacy concerns,we propose a new differentially private average consensus algorithm,where a distributed shuffling mechanism based on the Pailler cryptosystem is established to initialize the initial states for the subsequent stage of running standard average *** the proposed algorithm,both rigorous provable differential privacy and accuracy guarantee are established,with an improved privacy/accuracy trade-off than the existing relevant ***,it is shown that given any expected differential privacy levels,the achievable mean-square computation error is inversely proportional to the network size,in contrast with the optimal error achieved in the literature that is independent of the network size.A numerical example and an application to the distributed load shedding for an IEEE 33-bus power system are used to validate the effectiveness of the proposed approach.

关键词： Distributed load shedding average consensus differential privacy power system

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

arXiv

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arXiv 2023年

作者： Chen, Xinyi Wu, Jibin Tang, Huajin Ren, Qinyuan Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong College of Computer Science and Technology Zhejiang University China College of Control Science and Engineering Zhejiang University China

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Improving finite-difference time-domain memory bandwidth by using block floating-point arithmetic

Improving finite-difference time-domain memory bandwidth by ...

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Telecommunications Forum (TELFOR)

作者： Stefan Pijetlovic Ivan Kastelan Milos Subotic Nebojsa Pjevalica RT-RK Institute for computer based systems Novi Sad Serbia Department of Computing and control engineering (Faculty of Technical Sciences) University of Novi Sad Novi Sad Serbia

Finite-difference time-domain is a numerical method used for modelling of computational electrodynamics. The method is resource intensive, especially regarding memory usage since multiple memory accesses are required per single computation. In other words, memory, or more precisely its speed, is a limiting factor for the overall performance. Existing solutions focus either on structures with great parallelism such as graphical processing units or custom designs. The former have issues with underutilized resources, while the latter require significant time and effort to achieve similar or slightly better performances. The approach taken during this research was to target the memory itself and find a way to improve the efficiency of memory usage across multiple platforms. The main contribution of the paper is using block floating-point in a novel way regarding the finite-difference time-domain method. The presented solution is a verification of earlier research, proving that the performance of this algorithm can indeed be improved using the suggested methods.

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