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arXiv 2023年第8期8卷 4094页

作者： Ampountolas, Konstantinos The Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionality of stock ACC systems, including their proprietary control logic and parameters, is not publicly available, this work considers a car-following scenario with a human-driven vehicle (leader) and an ACC engaged ego vehicle (follower) that employs a constant timeheadway policy (CTHP). The objective of the DUKF is to determine the CTHP parameters of the ACC by using real-time observations of space-gap and relative velocity from the vehicle's onboard sensors. Real-time parameter identification of stock ACC systems is essential for assessing their string stability, large-scale deployment on motorways, and impact on traffic flow and throughput. In this regard, L2and L∞string stability conditions are considered. The observability rank condition for nonlinear systems is adopted to evaluate the ability of the proposed estimation scheme to estimate stock ACC system parameters using empirical data. The proposed filter is evaluated using empirical data collected from the onboard sensors of two 2019 SUV vehicles, namely Hyundai Nexo and SsangYong Rexton, equipped with stock ACC systems;and is compared with batch and recursive least-squares optimization. The set of ACC model parameters obtained from the proposed filter revealed that the commercially implemented ACC system of the considered vehicle (Hyundai Nexo) is neither L2nor L∞string stable. Copyright © 2023, The Authors. All rights reserved.

关键词： Adaptive cruise control

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Data-Driven control of Distributed Event-Triggered Network systems

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IEEE/CAA Journal of automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Seek-and-Take Games of Heterogeneous Agent Teams With Large Language Model

Seek-and-Take Games of Heterogeneous Agent Teams With Large ...

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2024 China Automation Congress, CAC 2024

作者： Li, Haolin Yi, Peng Wei, Dixiao Bai, Wenyan The Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201203 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9798350368604

The problem of confrontation games between multi-agent teams has attracted considerable interest, and the question of how to ensure effective coordination of heterogeneous agents in dynamic and adversarial environments remains a significant challenge. In this paper, we present a decision-making methodology for a confrontation game between two teams, which is based on large language models (LLMs). The game is designed as a competitive Seek-and-Take task comprising three distinct robot types: reconnaissance, grabbing, and blocking robots. The LLMs enhance the robot team’s decision-making efficiency, delivering high-level commands that significantly improve coordination and adversarial performance. The game is initially formalized as a partially observable Markov decision process. Subsequently, a systematic construction of the LLM agents is provided, enabling real-time decisions based on the state information of a heterogeneous robot team (HRT). Detailed prompt design and the "Zero-shot-CoT" procedure are provided, and a cue word iteration method with reflections is proposed as a means of enhancing the decision-making efficiency of the LLMs and generating optimal collaboration strategies. Finally, the efficacy of the methodology is validated through experimentation with multiple LLMs in a multi-robot simulation environment. The results demonstrate that LLMs improve the efficiency and adaptability of decision-making in dynamic environments by serving as a core decision module, generating optimal strategies through real-time analysis. © 2024 IEEE.

关键词： Efficiency

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Physics-Informed Neural Networks for Learning the Parameters of Commercial Adaptive Cruise control systems

Physics-Informed Neural Networks for Learning the Parameters...

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IEEE Conference on Decision and control

作者： Theocharis Apostolakis Konstantinos Ampountolas Department of Mechanical Engineering Automatic Control and Autonomous Systems Laboratory University of Thessaly Volos Greece

This paper develops a physics-informed neural network (PINN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems. The constant time-headway policy (CTHP) is adopted to emulate the core functionality of stock ACC systems (proprietary control logic and its parameters) which is not publicly available. Multi-layer artificial neural networks is a class of universal approximators, and thus the developed PINN can serve as a surrogate approximator to capture the longitudinal dynamics of ACC-engaged vehicles and efficiently learn the unknown parameters of the CTHP. The ability of the PINN to infer the unknown ACC parameters is tested on both synthetic and empirical data of space-gap and relative velocity involved ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PINN to learn the unknown design parameters of stock ACC systems of different vehicle makes. The set of ACC model parameters obtained from the PINN revealed that the stock ACC system of the considered vehicles in three experimental campaigns is neither $\mathcal{L}_{2}$ nor $\mathcal{L}_{\infty}$ string stable.

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Model Predictive control with Gaussian-Process-Supported Dynamical Constraints for autonomous Vehicles

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IFAC-PapersOnLine 2023年第2期56卷 507-512页

作者： Johanna Bethge Maik Pfefferkorn Alexander Rose Jan Peters Rolf Findeisen Control and Cyber-Physical Systems Laboratory Technical University of Darmstadt Darmstadt Germany Systems Theory and Automatic Control Laboratory Otto-von-Guericke University Magdeburg Magdeburg Germany Intelligent Autonomous Systems Laboratory Technical University of Darmstadt Darmstadt Germany

We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. The multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with Gaussian process regression support enables probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes.

关键词： Gaussian Process based identification and control Nonlinear Model Predictive control Gaussian Processes Robust control Intelligent autonomous Vehicles

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Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise control systems

arXiv

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arXiv 2023年

作者： Apostolakis, Theocharis Ampountolas, Konstantinos Automatic Control and Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotive industry. To emulate the core functionality of stock ACC systems, which have proprietary control logic and undisclosed parameters, the constant time-headway policy (CTHP) is adopted. Leveraging the multi-layer artificial neural networks as universal approximators, the developed PiNN serves as a surrogate model for the longitudinal dynamics of ACC-engaged vehicles, efficiently learning the unknown parameters of the CTHP. The PiNNs allow the integration of physical laws directly into the learning process. The ability of the PiNN to infer the unknown ACC parameters is meticulously assessed using both synthetic and high-fidelity empirical data of space-gap and relative velocity involving ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PiNN in learning the unknown design parameters of stock ACC systems from different car manufacturers. The set of ACC model parameters obtained from the PiNN revealed that the stock ACC systems of the considered vehicles in three experimental campaigns are neither L2 nor L∞ string stable. © 2023, CC BY-NC-ND.

关键词： Adaptive cruise control

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Seek-and-Take Games of Heterogeneous Agent Teams with Large Language Model

Seek-and-Take Games of Heterogeneous Agent Teams with Large ...

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Chinese Automation Congress (CAC)

作者： Haolin Li Peng Yi Dixiao Wei Wenyan Bai Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The problem of confrontation games between multi-agent teams has attracted considerable interest, and the question of how to ensure effective coordination of heterogeneous agents in dynamic and adversarial environments remains a significant challenge. In this paper, we present a decision-making methodology for a confrontation game between two teams, which is based on large language models (LLMs). The game is designed as a competitive Seek-and-Take task comprising three distinct robot types: reconnaissance, grabbing, and blocking robots. The LLMs enhance the robot team's decision-making efficiency, delivering high-level commands that significantly improve coordination and adversarial performance. The game is initially formalized as a partially observable Markov decision process. Subsequently, a systematic construction of the LLM agents is provided, enabling real-time decisions based on the state information of a heterogeneous robot team (HRT). Detailed prompt design and the “Zero-shot-CoT” procedure are provided, and a cue word iteration method with reflections is proposed as a means of enhancing the decision-making efficiency of the LLMs and generating optimal collaboration strategies. Finally, the efficacy of the methodology is validated through experimentation with multiple LLMs in a multi-robot simulation environment. The results demonstrate that LLMs improve the efficiency and adaptability of decision-making in dynamic environments by serving as a core decision module, generating optimal strategies through real-time analysis.

关键词： Systematics Robot kinematics Large language models Markov decision processes Decision making Collaboration Games Reconnaissance Real-time systems Reflection

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Learning Energy-Efficient Trajectory Planning for Robotic Manipulators Using Bayesian Optimization

Learning Energy-Efficient Trajectory Planning for Robotic Ma...

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European control Conference (ECC)

作者： Philipp Holzmann Maik Pfefferkorn Jan Peters Rolf Findeisen Control and Cyber-Physical Systems Laboratory (CCPS) TU Darmstadt Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Intelligent Autonomous Systems Laboratory TU Darmstadt Research Department SAIROL German Research Center for AI (DFKI) Hessian Centre for Artificial Intelligence

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Energy-optimal operation of robotic systems has gained high interest in both industry and science. We propose to fuse model predictive control and Bayesian optimization to plan minimum-energy trajectories for industrial robots that guarantee successful executions of the primary task. Particularly, parts of the predictive planner are learned using Bayesian optimization to account for the secondary, higher-level objective - here energy minimization. The effectiveness of the proposed approach is underlined in simulation, where a reduction in energy consumption is observed while maintaining a high quality of task executions.

关键词： Energy consumption Uncertainty Trajectory planning Service robots Welding Manipulators Bayes methods

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Dynamic Hard Shoulder Running Lane control

Dynamic Hard Shoulder Running Lane Control

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Rodrigo Castelan Carlson Eduardo Rosa de Lima Eduardo Rauh Müller Felipe Augusto de Souza Konstantinos Ampountolas Department of Automation and Systems Federal University of Santa Catarina Florianópolis Brazil Energy Systems Argonne National Laboratory Lemont IL USA Department of Mechanical Engineering Automatic Control & Autonomous Systems Laboratory University of Thessaly Volos Greece

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This work presents a novel and practical real-time switching control policy for the dynamic hard shoulder running lane control on motorways. Using the hard shoulder as a running lane in peak traffic periods increases the motorway capacity at bottlenecks, increasing throughput and avoiding the onset of congestion. The proposed policy enables a smooth switching on of the hard shoulder lane at peak periods. It is based on properties of the fundamental diagram of motorway traffic with easy and unambiguous interpretation and implementation by traffic operators. Simulation analysis on a 5 km-long motorway stretch with three lanes and one hard shoulder lane using the AIMSUN microscopic simulator is demonstrated. The results have showed that the proposed switching control improves the motorway throughput compared to rival control schemes.

关键词： State feedback Sensitivity System performance Roads Switches control systems Throughput Real-time systems Safety Vehicle dynamics

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Model Predictive control with Gaussian-Process-Supported Dynamical Constraints for autonomous Vehicles

arXiv

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arXiv 2023年

作者： Bethge, Johanna Pfefferkorn, Maik Rose, Alexander Peters, Jan Findeisen, Rolf Control and Cyber-Physical Systems Laboratory Technical University of Darmstadt Darmstadt Germany Systems Theory and Automatic Control Laboratory Otto-von-Guericke University Magdeburg Magdeburg Germany Intelligent Autonomous Systems Laboratory Technical University of Darmstadt Darmstadt Germany

We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs the uncertainty about the GP’s prediction to achieve safety. A multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with Gaussian process regression support enables repeated feasibility and probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes. © 2023, CC BY.

关键词： Robust control

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