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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Improving IoT Network Lifetime Through HHO-MPPT optimized Solar Energy Harvesting

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Improving IoT Network Lifetime Through HHO-MPPT optimized So...

Mechatronic systems Supervision (IW_MSS), IEEE International Workshop on

作者： Sirine Rabah Aida Zaier Jaime Lloret Hassen Dahman National Engineering School of Gabes Research Laboratory Modeling Analysis and Control of Systems (MACS- LR16ES22) National Engineering School of Tunis Innov Lab University of Carthage Tunis Tunisia Instituto de Investigacion Para la Gestion Integrada de Zonas Costeras Universitat Politecnica de Valencia Gandia Spain b-Department of Automatic and Electrical Engineering National Engineering School of Gabes a-LaPhyMNE-LR05ES14 Gabes University

The principal contribution of this study lies in creating an effective solar energy harvesting system intended to address the energy limitations experienced by batteries in Internet of Things (IoT) networks. Ideally, IoT network nodes equipped with optimized Solar Energy Harvesting systems should operate for extended periods. To achieve this goal, we propose the utilization of Maximum Power Point Tracking (MPPT) based on Harris Hawks Optimization (HHO) controllers. This strategy aims to optimize solar power extraction, consequently boosting overall system efficiency. We’ve implemented our methodology in MATLAB/Simulink and assessed its performance using a range of performance metrics. The results demonstrate the efficiency of our method, which is further substantiated through a comparative analysis with analysis with two traditional techniques. Our proposed method achieves maximum power tracking performance in photovoltaic (PV) systems in both cases of stable and variable weather conditions.

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Efficiency of higher-order algorithms for minimizing composite functions

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arXiv 2022年

作者： Nabou, Yassine Necoara, Ion Automatic Control and Systems Engineering Department National University of Science and Technology Politehnica Bucharest Spl. Independentei 313 Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

Composite minimization involves a collection of functions which are aggregated in a nonsmooth manner. It covers, as a particular case, smooth approximation of minimax games, minimization of max-type functions, and simple composite minimization problems, where the objective function has a nonsmooth component. We design a higher-order majorization algorithmic framework for fully composite problems (possibly nonconvex). Our framework replaces each component with a higher-order surrogate such that the corresponding error function has a higher-order Lipschitz continuous derivative. We present convergence guarantees for our method for composite optimization problems with (non)convex and (non)smooth objective function. In particular, we prove stationary point convergence guarantees for general nonconvex (possibly nonsmooth) problems and under Kurdyka-Lojasiewicz (KL) property of the objective function we derive improved rates depending on the KL parameter. For convex (possibly nonsmooth) problems we also provide sublinear convergence *** Codes Composite optimization, (non)convex minimization, higher-order methods, Kurdyka-Lojasiewicz property, convergence rates © 2022, CC BY.

关键词： Optimization

Do We Run Large-scale Multi-Robot systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling

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arXiv 2023年

作者： Kuckling, Jonas Luckey, Robin Avrutin, Viktor Vardy, Andrew Reina, Andreagiovanni Hamann, Heiko The Department of Computer and Information Science University of Konstanz Konstanz78457 Germany The Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany The Institute of Computer Engineering University of Lübeck Lübeck Germany The Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany The Department of Computer Science The Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John's Canada IRIDIA Université Libre de Bruxelles Brussels Belgium

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning. © 2023, CC BY.

关键词： Multipurpose robots

Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling control and a High-Fidelity Aircraft Model

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engineering 2021年第4期7卷 495-506页

作者： Olusayo Obajemu Mahdi Mahfouf Lohithaksha M.Maiyar Abrar Al-Hzndi Michal Weiszer Jun Chen Department of Automatic Control and Systems Engineering The University of SheffieldSheffield S14DWUK Business and Management Research Institute University of BedfordshireLuton LU13JUUK School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK

Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground *** novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside ***,autonomous taxiing is envisioned as a key component in future digitalized ***,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning *** majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas *** paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control *** in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational *** proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous *** proposed methodology is realistic and simple to ***,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.

关键词： Aircraft model Intelligent taxiing Optimization Four-dimensional trajectory

Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator

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arXiv 2022年

作者： Ju, Yue Mu, Biqiang Ljung, Lennart Chen, Tianshi The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China The Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

Regularized techniques, also named as kernel-based techniques, are the major advances in system identification in the last decade. Although many promising results have been achieved, their theoretical analysis is far from complete and there are still many key problems to be solved. One of them is the asymptotic theory, which is about convergence properties of the model estimators as the sample size goes to infinity. The existing related results for regularized system identification are about the almost sure convergence of various hyper-parameter estimators. A common problem of those results is that they do not contain information on the factors that affect the convergence properties of those hyper-parameter estimators, e.g., the regression matrix. In this paper, we tackle problems of this kind for the regularized finite impulse response model estimation with the empirical Bayes (EB) hyper-parameter estimator and filtered white noise input. In order to expose and find those factors, we study the convergence in distribution of the EB hyper-parameter estimator, and the asymptotic distribution of its corresponding model estimator. For illustration, we run Monte Carlo simulations to show the efficacy of our obtained theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint

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arXiv 2022年

作者： Fontanesi, Gianluca Zhu, Anding Arvaneh, Mahnaz Ahmadi, Hamed The School of Electrical and Electronic Engineering University College Dublin Dublin 4D04 V1W8 Ireland The Department of Electronic Engineering University of York Heslington YorkYO10 5DD United Kingdom The Automatic Control and Systems Engineering University of Sheffield United Kingdom

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Proximal gradient methods with inexact oracle of degree q for composite optimization

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arXiv 2024年

作者： Nabou, Yassine Glineur, François Necoara, Ion Automatic Control and Systems Engineering Department National University of Science and Technology Politehnica Bucharest Spl. Independentei 313 Bucharest060042 Romania ICTEAM Institute Université catholique de Louvain Louvain-la-Neuve1348 Belgium CORE Université catholique de Louvain 1348 Belgium Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest610101 Romania

We introduce the concept of inexact first-order oracle of degree q for a possibly nonconvex and nonsmooth function, which naturally appears in the context of approximate gradient, weak level of smoothness and other situations. Our definition is less conservative than those found in the existing literature, and it can be viewed as an interpolation between fully exact and the existing inexact first-order oracle definitions. We analyze the convergence behavior of a (fast) inexact proximal gradient method using such an oracle for solving (non)convex composite minimization problems. We derive complexity estimates and study the dependence between the accuracy of the oracle and the desired accuracy of the gradient or of the objective function. Our results show that better rates can be obtained both theoretically and in numerical simulations when q is large. © 2024, CC BY.

关键词： Gradient methods

An UWB Bandpass Filter with Large Upper Stop Band Suppression

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An UWB Bandpass Filter with Large Upper Stop Band Suppressio...

Advanced Technology in Electronic and Electrical engineering (ICATEEE), International Conference of

作者： Hassiba Louazene Mouloud Challal M’hamed Boulakroune Department of Electronics and Communication Faculty of New Technologies of Information and Communication University of Kasdi Merbah of Ouargla Ouargla Algeria Department of Electronics Institute of Electrical and Electronic Engineering. Signals and Systems Laboratory University of M’Hamed Bougara of Boumerdes Boumerdes Algeria Department of Electrical and Automatic National Polytechnic School of Constantine Constantine Algeria

An new bandpass filter (BPF) with wide stopband performance for ultra-wideband (UWB) applications is presented in this paper. An equivalent RLC circuit model is also shown, and its relevant parameters are derived using parametric relationships. The proposed filter operates from 2.4 GHz to 10.4 GHz. The key advantage of this filter is the large upper stopband without adding any component. Based on good agreement between the results of simulations (using an ECM and a full-wave electromagnetic simulator) and measurements, the proposed filter is appropriate for UWB systems.

关键词： Band-pass filters Fabrication Electrical engineering Costs RLC circuits Ultra wideband communication Predictive models