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ieee 26th International conference on Intelligent Transportation systems (ITSC)

作者： Guo, Jiaying Ghanadbashi, Saeedeh Wang, Shen Golpayegani, Fatemeh Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9798350399462

Static or traditional rule-based urban traffic signal control approaches are inadequate to handle ever-changing and stochastic urban traffic. Recent works in Intelligent Traffic Signal control (ITSC) have extensively explored Deep Reinforcement learning (DRL) to enhance traffic signal adjustment based on real-time traffic. However, DRL-based ITSC is data-driven, and with current communication technologies, especially in dense urban traffic networks, it is unrealistic to assume perfect data transmission. Partial detection of vehicles, noisy sensor data, and unexpected traffic events should be addressed promptly. Ontology-based Intelligent Traffic Signal control (OITSC) was proposed to address these challenges and validated under synthetic grid maps. In this paper, we have improved the original OITSC to handle more complicated urban scenarios using DRL. A road network in Dublin city center is selected to simulate realistic urban traffic scenarios. The reduction in the average vehicle waiting time demonstrates the efficiency of OITSC in improving urban traffic under partially observable and noisy traffic information, with the inclusion of prioritized vehicles in the face of unexpected road events. We have also provided an edge-computing deployment strategy, which is expected to provide robust and fast responses for complex urban traffic, especially for unexpected cases.

关键词： Reinforcement learning

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Local Directional Centrality Clustering Based on K-nearest Neighbor Outlier Detection and Shared Neighborhood Strategy 13

Local Directional Centrality Clustering Based on K-nearest N...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Qing Feng, Zihang Yan, Liping Xia, Yuanqing School of Automation Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China

ISBN: (纸本)9798350361674

Clustering, as an important prerequisite method in data analysis, can uncover potential information in the data and then proceed to the next step of data analysis and processing. The recently proposed boundary-seeking Clustering algorithm using the local Direction Centrality (CDC) is a very effective method for clustering data with heterogeneous density and weak connectivity. However, it still has some shortcomings. On the one hand, the reachable distance is not enough to comprehensively distinguish weak connection situations, which can easily lead to cross cluster connection errors. On the other hand, K-nearest neighbor search is prone to cross cluster search, leading to misjudgment of boundary points and resulting in connection errors. This paper proposes a local direction centrality clustering algorithm based on K-nearest neighbor outlier detection and shared neighborhood strategy (SODCDC) for sparse and weakly connected data. This algorithm uses a K-nearest neighbor outlier detection strategy to relieve K-nearest neighbor cross cluster search and reduces the probability of boundary point misjudgment. At the same time, it uses a shared neighborhood strategy to further prevent cross cluster connections of weakly connected data. Experiments on some datasets have shown that compared to the original algorithm, the proposed algorithm performs better under the commonly used evaluation metrics. © 2024 ieee.

关键词： Nearest neighbor search

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Multi-Gradient-Descent Federated learning With Parity for Cooperative Short-Term Load Forecasting

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ieee TRANSACTIONS ON POWER systems 2025年第2期40卷 1199-1213页

作者： Wang, Haijin Xing, Shuangshuang Si, Caomingzhe Pan, Zibin Zhao, Junhua Qiu, Jing Dong, Zhaoyang China Southern Power Grid Co Ltd Energy Dev Res Inst CSG Guangzhou 510663 Guangdong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China State Grid Zhejiang Elect Power Co Ltd Tonglu Elect Power Co Hangzhou 310007 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia City Univ Hong Kong Dept Elect Engn Hong Kong 518057 Peoples R China

Short-term load forecasting (STLF) is essential in power system research. In scenarios where multiple distribution system operators are involved in cooperative forecasting, it is important to uphold the fairness of multi-party cooperation and drive the parity of model gains among parties under the premise of privacy protection. In this context, this paper proposes a short-term load forecasting method namely Multi-Gradient-Descent Federated learning with Parity Long-short Term Memory (MGD-FLP-LSTM) under a federated learning framework. First, MGD-FLP-LSTM transforms cooperative STLF into a Multi-Objective Optimization (MOO) problem with parity-driven objective. The designed algorithm optimizes multiple objectives simultaneously and incorporates Karush-Kuhn-Tucker (KKT) conditions in solving the dual form of the problem, thus accelerating the global model convergence. Second, MGD-FLP-LSTM constitutes a parity-driven objective with cosine similarity in the MOO iteration. The objective allows all local loss functions to have similar descents on the common gradient descending direction. Third, MGD-FLP-LSTM constructs a Performance Parity control (PPC) scheme that enables an active moderate trade-off between forecasting accuracy and parity. Experiments on real-measured load dataset highlight the method's enhanced accuracy, efficiency, and performance while maintaining consumer privacy.

关键词： Long short term memory Load modeling Forecasting Accuracy Load forecasting data models Federated learning Cooperative STLF federated learning multi-gradient-descent privacy-preserving performance parity

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data-driven Predictive control for Robust Exoskeleton Locomotion

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

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2024 International conference on Intelligent Robots and systems

作者： Li, Kejun Kim, Jeeseop Xiong, Xiaobin Hamed, Kaveh Akbari Yue, Yisong Ames, Aaron D. CALTECH Dept Computat & Neural Syst Pasadena CA 91125 USA CALTECH Dept Mech & Civil Engn Pasadena CA 91125 USA Univ Wisconsin Madison Dept Mech Engn Madison WI 53706 USA Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA CALTECH Dept Comp & Math Sci Pasadena CA 91125 USA CALTECH Dept Control & Dynam Syst Pasadena CA 91125 USA

ISBN: (纸本)9798350377712;9798350377705

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： Inverse kinematics

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Model-Free Adaptive control for Unknown MIMO Nonaffine Nonlinear Discrete-Time systems With Experimental Validation

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2022年第4期33卷 1727-1739页

作者： Xiong, Shuangshuang Hou, Zhongsheng Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

In this article, a model-free adaptive control (MFAC) algorithm based on full form dynamic linearization (FFDL) data model is presented for a class of unknown multi-input multi-output (MIMO) nonaffine nonlinear discrete-time learning systems. A virtual equivalent data model in the input-output sense to the considered plant is established first by using the FFDL technology. Then, using the obtained data model, a data-driven MFAC algorithm is designed merely using the inputs and outputs data of the closed-loop learning system. The theoretical analysis of the monotonic convergence of the tracking error dynamics, the bounded-input bounded-output (BIBO) stability, and the internal stability of the closed-loop learning system is rigorously proved by the contraction mapping principle. The effectiveness of the proposed control algorithm is verified by a simulation and a quad-rotor aircraft experimental system.

关键词： data models MIMO communication Field-flow fractionation Nonlinear dynamical systems Heuristic algorithms Adaptation models Adaptive control data-driven control full form dynamic linearization (DL) model-free adaptive control (MFAC) multi-input multi-output (MIMO) nonlinear systems stability

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Safe control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and control

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ieee TRANSACTIONS ON ROBOTICS 2023年第3期39卷 1749-1767页

作者： Dawson, Charles Gao, Sicun Fan, Chuchu MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced transparency and lack of guarantees on the safety or stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies-these certificates provide concise data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this article, we provide a comprehensive survey of this rapidly developing field of certificate learning. We hope that this article will serve as an accessible introduction to the theory and practice of certificate learning, both to those who wish to apply these tools to practical robotics problems and to those who wish to dive more deeply into the theory of learning for control.

关键词： Lyapunov methods Safety Robots Asymptotic stability Trajectory Measurement Trajectory tracking Deep learning in robotics and automation formal methods in robotics and automation neural certificates robot safety

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Comparative Analysis of Machine learning Algorithms in Traffic Mainstream control on Freeway Networks 28

Comparative Analysis of Machine Learning Algorithms in Traff...

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28th ieee International conference on Intelligent Engineering systems (INES)

作者： Amini, Mehran Koczy, Laszlo T. Szechenyi Istvan Univ Dept Informat Technol Gyor Hungary

ISBN: (纸本)9798350367607;9798350367591

Efficient management of mainstream traffic flow on freeway networks is a critical challenge in urban transportation, with significant implications for congestion mitigation and environmental sustainability. The purpose of this study is to address the problem of predicting traffic volumes and maintaining flow rates below critical densities, thereby preventing the onset of congestion on interconnected freeway systems. Motivated by the need for real-time traffic control strategies, this research employs machine learning algorithms to forecast traffic volumes, leveraging a comprehensive dataset of traffic patterns on freeways. In our approach, we conducted a comparative analysis of two advanced machine learning algorithms: Long Short-Term Memory (LSTM) networks, which are adept at modeling time-series data with long-range temporal dependencies, and Random Forest regression, known for its robust performance across diverse datasets. We enriched the traffic data through feature engineering, incorporating temporal variables, vehicular counts, and a calculated measure of proximity to critical density for the targeted freeway. Our findings indicate a markedly disparate performance between the algorithms. The LSTM model showed a moderate ability to capture the variance in traffic flow, with an R-2 score of 0.619. In contrast, the Random Forest model demonstrated exceptional predictive accuracy, achieving an R-2 of 0.998, and substantially outperforming the LSTM model in terms of both Mean Squared Error and Root Mean Squared Error.

关键词： Traffic Mainstream control Freeway Machine learning LSTM Random Forest Traffic Flow Prediction Congestion Management Critical Density

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Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine learning Method

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ieee ACCESS 2025年 13卷 3274-3283页

作者： Tian, Hu Ji, Anping Chongqing Three Gorges Univ Sch Mech Engn Chongqing 404100 Peoples R China Chongqing Municipal Agr Sch Chongqing 401329 Peoples R China

Tractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models, which lack real-time feedback, making them unsuitable for practical field applications. To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine learning (ML) to enhance the driving comfort of agricultural tractors. Our low-cost IoT-enabled solution is compatible with existing tractors, requiring no expensive intelligent upgrades. Using the XGBoost model for ride comfort prediction, we achieved superior performance (R- 2=0.96 , RMSE =0.015) compared to other ML models. Additionally, the Particle Swarm Optimization (PSO) algorithm is employed to recommend optimal operational parameters, reducing the ride comfort value (OVV) by 6.67% in real-time experiments. This study highlights a scalable, data-driven approach for improving tractor comfort and offers a reference for intelligent control strategies in Agriculture 4.0.

关键词： Agricultural machinery Real-time systems Vibrations Predictive models Internet of Things Agriculture Prediction algorithms Machine learning Soil Software Tractor machine learning particle swarm optimization internet of things driving comfort real-time control

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End-to-End Autonomous Driving Algorithm Based on PPO and Its Implementation 13

End-to-End Autonomous Driving Algorithm Based on PPO and Its...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhao, Jianfeng Zhao, Yang Li, Weixia Zeng, Chaoqun School of Automative and Transportation Engineering Shenzhen Politechnic University Shenzhen518000 China

ISBN: (纸本)9798350361674

With the commercialization of vehicles equipped with partial autonomous driving capabilities, achieving fully autonomous driving remains a hot topic among researchers. In contrast to modular autonomous vehicle control, end-to-end autonomous driving aims to replicate human driving by directly translating sensor inputs into vehicle control outputs, making it the ultimate goal in autonomous driving. In this study, we propose an end-to-end autonomous driving algorithm based on the Proximal Policy Optimization (PPO) reinforcement learning algorithm and verify its feasibility. Firstly, we construct an autonomous driving algorithm based on the PPO algorithm, where pre-encoded images and vehicle state information are input to a policy based on deep neural networks, directly generating speed and steering controls for the vehicle, thus achieving true end-to-end autonomous driving. Furthermore, using the CARLA simulation environment for autonomous vehicles, we define a reward mechanism for driving behavior and establish a reinforcement learning environment to train and validate the proposed algorithm. Finally, we analyze the algorithm's feasibility based on rewards, cumulative average rewards, average episode length, and average driving distance. While the algorithm still has a long way to go before practical application, it lays the foundation for further implementation of end-to-end autonomous driving algorithms. © 2024 ieee.

关键词： Deep reinforcement learning

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Distributed data-driven control of Network systems

IEEE OPEN JOURNAL OF CONTROL SYSTEMS

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ieee OPEN JOURNAL OF control systems 2023年 2卷 93-107页

作者： Celi, Federico Baggio, Giacomo Pasqualetti, Fabio Univ Calif Riverside Dept Mech Engn Riverside CA 92521 USA Univ Padua Dept Informat Engn I-35131 Padua Italy

Imperfect models lead to imperfect controllers and deriving accurate models from first principles or system identification is especially challenging in networked systems. Instead, data can be used to directly compute controllers, without requiring any system identification or modeling. In this paper we propose a strategy to directly learn control actions when data from past system trajectories is distributed among multiple agents in a network. The approach we develop provably converges to a suboptimal solution in a finite number of steps, bounded by the diameter of the network, and with a sub-optimality gap that can be characterized as a function of data, and that can be made arbitrarily small. We further characterize the robustness properties of our approach and give provable guarantees on its performance when data are affected by noise or by a class of attacks.

关键词： Trajectory Network systems control systems Robustness Optimal control data models Minimization Distributed control and optimization learning for control network analysis and control optimal control

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