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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization

arXiv

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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Submodularity-based sensor scheduling for state estimation among multiple linear dynamical systems

Submodularity-based sensor scheduling for state estimation a...

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Jiangsu Annual Conference on automation (JACA 2022)

作者： Y. Ni C. Yang X. Liu X. Luan F. Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi People's Republic of China The Key Laboratory of Smart Manufacturing in Energy Chemical Process (Ministry of Education) Department of Automation East China University of Science and Technology Shanghai People's Republic of China Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital/Harvard Medical School Boston USA

In this paper we focus on the sensor scheduling for dynamic state estimation among multiple processes. Multiple sensors transmit their measurements to the remote estimator via a shared wireless channel. Only a subset of sensors are allowed to transmit at each time. The objective of the scheduler is to maximize a linear combination of the terminal time estimation error covariances in a log-determinant sense. We develop conditions under which the objective is monotone and submodular. A submodularity-based greedy scheduling algorithm is proposed, and its performance bound is provided. Numerical examples are presented to illustrate the theoretical findings.

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A self-triggered control scheme for Markov jump systems under multiple range performance restrictions

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IFAC-PapersOnLine 2020年第2期53卷 2783-2788页

作者： Haiying Wan Hamid Reza Karimi Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi214122 China Department of Mechanical Engineering Politecnico di Milano Milan Italy

This paper proposes a multi-frequency controller design scheme for Markov jump systems (MJSs) based on the self-triggered strategy in a resource-aware way. Firstly, a derandomization technique is introduced to make sure the transition probability information is included in the finite frequency specification analysis. Then, a self-triggered policy is developed to update the control input of the system via the history measurement. Finally, sufficient conditions are deduced that guarantee the multiple range frequency performances and the reduction of computation and communication occupation for the controlled MJSs, simultanously. The cart- spring system is employed to illustrate the effectiveness of the proposed approach.

关键词： Self-triggered control finite frequency performance Markov jump systems derandomization disturbance rejection

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Zeroth-order algorithms for stochastic distributed nonconvex optimization

arXiv

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arXiv 2021年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(√p=(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p=(nT)) convergence rate when the global cost function additionally satisfies the Polyak-Lojasiewicz (P-L) condition in addition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms, which enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relative bounded second moment assumptions and the P-L condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

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Internal short circuit fault diagnosis for automotive lithium-ion batteries using an adaptive parameter identification model

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Tao, Qian Wang, Yiming Tian, Engang Tao, Hongfeng Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi Jiangsu214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

This paper develops a model-based diagnostic method for internal short circuit (ISC) faults in lithium-ion batteries. This method utilizes a second-order equivalent circuit model (ECM) combined with a recursive least squares method with an adaptive forgetting factor for online parameter identification, with initial parameters optimized by a particle swarm optimization (PSO) algorithm. An extended Kalman filter (EKF) is then employed for state estimation to generate residuals, which are analyzed using the refined cumulative sum (CUSUM) statistical method to detect and diagnose ISC faults. Experimental results show that this method effectively identifies ISC faults in automotive power batteries under various initial state of charge (SOC) levels and fault-free conditions. At 100% initial SOC, fault detection times are 104 seconds for a 15Ω fault, 77 seconds for a 10Ω fault, and 27 seconds for a 1Ω fault;at 90% initial SOC, detection for a 10Ω fault takes 97 seconds, demonstrating high accuracy and robustness. © 2025 Elsevier Ltd

关键词： Lithium-ion batteries

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Soft-Sensing Estimation of Optical Density for PHA Production Using Multilayer Perceptron Neural Network 4

Soft-Sensing Estimation of Optical Density for PHA Productio...

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4th International Conference on engineering Technology 2019, ICET 2019

作者： Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Saleena Taip, Farah Rashid, Ahmad Hazri Ab. Jufika Ahmad, Nur Liyana Yusof, Ishak Mohd Mohamad, Adida Zuraida Control System and Signal Processing Research Centre Department of Electrical and Electronic Engineering Faculty of Engineering Universiti Putra Malaysia UPM Serdang Selangor43400 Malaysia Food and Process Department Faculty of Engineering Universiti Putra Malaysia UPM Serdang Selangor43400 Malaysia Industrial Biotechnology Research Centre SIRIM Berhad Persiaran Dato' Menteri Seksyen 2 Selangor Shah Alam40700 Malaysia Faculty of Electrical and Automation Engineering Technology TATI University College Teluk Kalong Kemaman Terengganu24000 Malaysia

Biomass concentration is an important indicator of production rate in polyhydroxyalkanoates (PHA) fermentation process. In current practice, measurement of biomass concentration is done off-line by laboratory analysis that is unsuitable for online process monitoring and control. Soft-sensor is often used as an alternative that provides an estimate of hard to measure parameters from easy to measure process data. However, most of these studies use simulated data or data generated from mathematical model that was developed without full consideration of process and measurement uncertainty. In this study, a soft-sensor is developed from real production data for PHA fermentation in pilot-scale bioreactor with the appropriate data pre-processing techniques applied to process data that was obtained from this system. Multilayer perceptron (MLP) neural network is used to estimate biomass concentration using secondary process parameters such as dissolved oxygen (DO), temperature, pH and agitation speed as inputs. Different models are developed based on different batches of production data and various network architecture in order to study the appropriate integration of process data and network topology that gives the best model accuracy. Results indicate that the biomass soft-sensor developed using MLP-ANN provides a better estimate of biomass in comparison to radial basis function (RBF) neural network and support vector regression (SVR) methods. The developed soft-sensor can be further used in monitoring and control of production output. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Multilayer neural networks

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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