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Machine learning-Based Resource Allocation in 6G Integrated Space and Terrestrial Networks-Aided Intelligent Autonomous Transportation

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Prabhashana, Sasinda C. Huynh, Dang Van Singh, Keshav Zepernick, Hans-Jurgn Dobre, Octavia A. Shin, Hyundong Duong, Trung Q. Mem Univ St John NF A1C 5S7 Canada Natl Sun Yat Sen Univ Kaohsiung 804 Taiwan Blekinge Inst Technol S-37179 Karlskrona Sweden Kyung Hee Univ Seoul South Korea Queens Univ Belfast Belfast BT7 1NN Antrim North Ireland

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments.

关键词： Resource management Satellites Heuristic algorithms 6G mobile communication Reconfigurable intelligent surfaces Energy consumption Low earth orbit satellites Delays Real-time systems Servers 6G networks deep reinforcement learning mobile edge computing orbital edge computing intelligent autonomous transportation systems

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Neural Network adaptive Output-Feedback Optimal Control for Active Suspension Systems

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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第6期52卷 4021-4032页

作者： Li, Yongming Wang, Tiechao Liu, Wei Tong, Shaocheng Liaoning Univ Technol Dept Basic Math Jinzhou 121001 Peoples R China Liaoning Univ Technol Coll Elect Engn Jinzhou 121001 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

The adaptive neural network (NN) output-feedback optimal control issue has been investigated for a quarter-car active electric suspension systems, where the suspension stiffness is unknown and partial state variables are unavailable for measurement. NNs are utilized to identify unknown nonlinearities, and an NN state observer is devised to estimate the unmeasurable states. For each backstepping step, via reinforcement learning (RL), a critic-actor architecture is designed to get the approximation solution of Hamilton-Jacobi-Bellman (HJB) equations and actual and virtual optimization controllers are designed, in which the input saturation constraint and road interference are considered. It is analytically proved that all controlled system signals remain bounded, while the power of the control input signal, as well as the amplitude of the vertical displacement, has been minimized. A comparative simulation is eventually given to elaborate the feasibility of the developed control algorithm.

关键词： Optimal control Artificial neural networks Suspensions (mechanical systems) Observers Vehicle dynamics Mathematical model Safety adaptive dynamic programming (ADP) output-feedback optimal control state observer suspension dynamics

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reinforcement learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

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ieee ACCESS 2021年 9卷 860-871页

作者： Lee, Heeyun Cha, Suk Won Seoul Natl Univ Dept Mech Engn Seoul 08826 South Korea Seoul Natl Univ Inst Adv Machines & Design Seoul 08826 South Korea

Hybrid electric vehicles, operated by engines and motors, require an energy management strategy to achieve competitive fuel economy performance. The equivalent consumption minimization strategy is a well-known algorithm that can be employed for the energy management of hybrid electric vehicles, based on the concept of the equivalent cost of fossil fuels and electric battery energy. However, in the equivalent consumption minimization strategy approach, a parameter called the equivalent factor should be determined to obtain the optimal control policy. In this study, reinforcement learning based approaches are proposed to determine the equivalent factor. First, we show that the equivalent factor can be indirectly extracted from the reinforcement learning results, using the control action from reinforcement learning for the specific driving cycle. In addition, a novel approach that combines reinforcement learning and the equivalent consumption minimization strategy is proposed, where the equivalent factor is determined based on the interaction between the reinforcement learning agent and driving environment, while the control input is decided by the equivalent consumption minimization strategy based on the determined equivalent factor. A model-based reinforcement learning method is used, and the proposed method is validated for vehicle simulation using a parallel hybrid electric vehicle. The simulation results show that the proposed method can achieve a near-optimal solution, which is close to the global solution obtained with the dynamic programming approach (96.7% compared to dynamic programming result in average), and improved performance of 4.3% in average compared with the existing adaptive equivalent consumption minimization strategy.

关键词： Equivalent consumption minimization strategy (ECMS) hybrid Electric vehicle model-based reinforcement learning optimal control power management reinforcement learning

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Robust Policy Iteration for Continuous-Time Linear Quadratic Regulation

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ieee TRANSACTIONS ON AUTOMATIC CONTROL 2022年第1期67卷 504-511页

作者： Pang, Bo Bian, Tao Jiang, Zhong-Ping NYU Tandon Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA Bank Amer Merrill Lynch New York NY 10036 USA

This article studies the robustness of policy iteration in the context of continuous-time infinite-horizon linear quadratic regulator (LQR) problem. It is shown that Kleinman's policy iteration algorithm is small-disturbance input-to-state stable, a property that is stronger than Sontag's local input-to-state stability but weaker than global input-to-state stability. More precisely, whenever the error in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded and enter a small neighborhood of the optimal solution of the LQR problem. Based on this result, an off-policy data-driven policy iteration algorithm for the LQR problem is shown to be robust when the system dynamics are subject to small additive unknown bounded disturbances. The theoretical results are validated by a numerical example.

关键词： Robustness Biological system modeling Optimal control Numerical stability Heuristic algorithms Approximation algorithms Symmetric matrices adaptive dynamic programming adaptive optimal control data-driven control policy iteration reinforcement learning robustness

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Online adaptive Compensation for Model Uncertainty Using Extreme learning Machine-based Control Barrier Functions

Online Adaptive Compensation for Model Uncertainty Using Ext...

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

作者： Munoz, Emanuel Kalaria, Dvij Lin, Qin Dolan, John M. Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA IIT Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Cleveland State Univ Elect Engn & Comp Sci Dept Cleveland OH 44115 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

A control barrier functions-based quadratic programming (CBF-QP) method has emerged as a controller synthesis tool to assure safety of autonomous systems owing to the appealing safe forward invariant set. However, the provable safety relies on a precisely described dynamic model, which is not always available in practice. Recent works leverage learning to compensate model uncertainty for a CBF controller. However, these approaches based on reinforcement learning or episodic learning are limited to dealing with time-invariant uncertainty. Also, the reinforcement learning approach learns the uncertainty offline, while episodic learning only updates the controller after a batch of data is available by the end of an episode. Instead, we propose a novel tuning extreme learning machine (tELM)-based CBF controller that can compensate time-variant and time-invariant model uncertainty adaptively in an online manner. We validate our approach's effectiveness in a simulation of an adaptive Cruise Control (ACC) system.

关键词： Control barrier function online learning adaptive cruise control safety model uncertainty extreme learning machine

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A Hybrid adaptive dynamic programming for Optimal Tracking Control of USVs

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ieee Transactions on Neural Networks and learning Systems 2025年第6期36卷 9961-9969页

作者： Shan Xue Ning Zhao Weidong Zhang Biao Luo Derong Liu School of Information and Communication Engineering Hainan University Haikou China Department of Automation Shanghai Jiao Tong University Shanghai China School of Automation Central South University Changsha China School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago IL USA

This article presents an efficient method for solving the optimal tracking control policy of unmanned surface vehicles (USVs) using a hybrid adaptive dynamic programming (ADP) approach. This approach integrates data-driven integral reinforcement learning (IRL) and dynamic event-driven (DED) mechanisms into the solution of the control policy of the established augmented system while obtaining both the feedforward and feedback components of the tracking controller. For the USV model and the reference trajectory, an augmented system is established, and the tracking Hamilton-Jacobi–Bellman (HJB) equation is derived based on IRL, aiming to fully utilize system data information and reduce model dependency. For the solution of the tracking HJB equation, the DED-based controller update rule is used to further reduce the burden of network transmission. In implementing the ADP method, the DED experience replay-based weight update rule is utilized to recycle data resources. Experiments show that compared with the static event-driven (SED) approach, the DED approach reduces the sample size by 78% and increases the average interval by about four times.

关键词： Mathematical models Data models Trajectory dynamic programming Performance analysis Feedforward systems reinforcement learning learning systems Electronic mail Adaptation models

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dynamic VNF Deployment and Resource Allocation in Mobile Edge Computing 22

Dynamic VNF Deployment and Resource Allocation in Mobile Edg...

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22nd ieee International symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhai, Xiaolong He, Zhenli Xiao, Yuanfei Wu, Junjie Yu, Xuejie Yunnan University Yunnan Key Laboratory of Software Engineering Kunming China Yunnan University School of Software Kunming650504 China

ISBN: (纸本)9798331509712

The explosive growth of terminal devices at the network edge, coupled with advancements in communication technology, poses significant challenges to traditional cloud computing models. Despite Mobile Edge Computing (MEC) mitigating some issues by enabling real-time data processing closer to the source, it faces challenges with linear growth in computational resources insufficient to meet the exponential growth in service demand. Existing research utilizing Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Service Function Chain (SFC) technologies has made progress but still faces critical issues, such as the inability to respond in real-time to dynamic demands and inefficiencies in resource management strategies. This paper addresses these challenges by optimizing Virtual Network Function (VNF) deployment strategies in MEC environments. We propose PPO-ERA, a novel algorithm leveraging deep reinforcement learning and the Karush-Kuhn-Tucker (KKT) method. This approach provides real-time, adaptive, and dynamic deployment policies for VNFs, significantly improving both the average response delay of tasks and resource utilization. Key contributions include rigorous SFC-based application modeling, dynamic VNF deployment algorithms, elastic resource allocation, uniform state representation, and extensive performance validation. These advancements enhance the adaptability, efficiency, and performance of VNF deployment strategies, addressing critical challenges in dynamic MEC environments. © 2024 ieee.

关键词： Network function virtualization

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An Online Model-Free reinforcement learning Approach for 6-DOF Robot Manipulators

An Online Model-Free Reinforcement Learning Approach for 6-D...

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2023 ieee International symposium on Robotic and Sensors Environments, ROSE 2023

作者： Hosny, Zeyad Nassar, Abdullah Aboelyazeed, Ahmed Mohamed, Mahmoud Abouheaf, Mohammed Gueaieb, Wail University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N6N5 Canada Bowling Green State University Robotics Engineering Bowling GreenOH43402 United States

ISBN: (纸本)9798350308044

Controlling 6 Degrees-of-Freedom (DoF) robotic manipulators in an online, model-free manner poses significant challenges due to their complex coupling, non-linearities, and the need to account for unmodeled dynamics. This paper introduces a model-free adaptive approach for real-time control of a 6 DoF 'EPSON' robotic manipulator, without requiring any prior knowledge of the manipulator's dynamics. Initially, we lay out the framework for an optimal control solution. A performance index is introduced, leveraging error dynamics and correction control signals, offering the capability to incorporate high-order error dynamics without the need to explicitly derive error trajectories. The order of error dynamics is determined by the chosen number of error samples. We assume a kernel-based solution structure aligning with the performance index, resulting in a temporal difference equation. This equation can be optimized to formulate a model-free control strategy. Subsequently, a reinforcement learning approach is adopted to approximate the underlying strategy. Infeasible exact solutions are overcome by employing a value iteration mechanism to adapt the actor-critic structures within an adaptive critics framework. To validate the proposed approach, it is compared against a conventional proportional-integral controller. A Unified Robot Description Format file is generated to facilitate the import of the robotic manipulator into the MATLAB Simulink environment, enabling its control. Ultimately, the proposed method yields superior results in terms of the dynamic characteristics of the response, demonstrating its effectiveness over the conventional approach. © 2023 ieee.

关键词： Real time control

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Model-Free Force Control of Cable-Driven Parallel Manipulators for Weight-Shift Aircraft Actuation

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Mailhot, Nathaniel Abouheaf, Mohammed Spinello, Davide Univ Ottawa Dept Mech Engn Ottawa ON K1N 6N5 Canada Bowling Green State Univ Sch Engn Bowling Green OH 43403 USA

We present a novel approach to flight control of weight-shift aircraft by employing a cable-driven parallel robot (CDPR) integrated with adaptive force control based on reinforcement learning. Development of weight-shift aircraft control has been sparse. Despite limited but notable efforts, modeling is hindered by parameter uncertainty stemming from the system's nonlinear dynamics. The model-free control method introduced in this work operates without relying on the knowledge of the complex dynamics inherent to weight-shift aircraft flight control. An online reinforcement learning technique known as action dependent heuristic dynamic programming (ADHDP) is applied to the problem of coordinating the tension forces across parallel cable-driven actuators. Two adaptive learning agents perform demanded weight-shift maneuvers by coordinating torque commands, without an inverse kinematics model. The online reinforcement learning control is implemented on flight controller hardware with limited computational resources and strict timing constraints, performing real-time experiments on a kinematically equivalent surrogate two-body weight-shift mechanism. After online training in the presence of sustained disturbance events, the adaptive learning agents optimally balance against competing trajectory tracking objectives. The CDPR capably reproduces standard S-turn maneuvers, coordinating simultaneous banking and pitching speed actions. The encouraging experimental results inform future integration of the weight-shift CDPRs toward automatic flight control that is unprecedented for this class of aircraft.

关键词： Aerospace control Aircraft Atmospheric modeling Vehicle dynamics Adaptation models Bars Trajectory adaptive flight control cable-driven parallel robot (CDPR) hardware implementation reinforcement learning weight-shift aircraft

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adaptive dynamic programming for Control: A Survey and Recent Advances

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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第1期51卷 142-160页

作者： Liu, Derong Xue, Shan Zhao, Bo Luo, Biao Wei, Qinglai Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control. First, its applications in optimal regulation are introduced, and some skilled and efficient algorithms are presented. Next, the use of ADP to solve game problems, mainly nonzero-sum game problems, is elaborated. It is followed by applications in large-scale systems. Note that although the functions presented in this article are based on continuous-time systems, various applications of ADP in discrete-time systems are also analyzed. Moreover, in each section, not only some existing techniques are discussed, but also possible directions for future work are pointed out. Finally, some overall prospects for the future are given, followed by conclusions of this article. Through a comprehensive and complete investigation of its applications in many existing fields, this article fully demonstrates that the ADP intelligent control method is promising in today's artificial intelligence era. Furthermore, it also plays a significant role in promoting economic and social development.

关键词： adaptive critic designs (ACDs) adaptive dynamic programming approximate dynamic programming intelligent control learning control neural dynamic programming neuro-dynamic programming optimal control reinforcement learning (RL)

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