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Online Learning Algorithm Design for Adaptive Output Regulation with Initial Excitation

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IEEE Transactions on automatic control 2025年

作者： Xu, Yong Wu, Zheng-Guang School of Automation Beijing Institute of Technology Beijing100081 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

This paper investigates the adaptive optimal output regulation of completely unknown linear time-invariant systems. First, a dynamic state feedback control policy with assured convergence rate requirement is developed such that the output regulation problem is transformed into a tractable optimization problem by incorporating the internal model. Then, an online-verifiable initial excitation-based dual-integrator-based learning algorithm is first proposed for establishing data-driven learning algorithm. The optimal data-driven control policy is learned by solving the linear regression equation (LRE) based on online system data, where the uniqueness of the LRE solution is established by verifying an invertible matrix under an online-verifiable initial excitation condition, rather than requiring the full-rank condition. Compared with existing iterative learning algorithms, our proposed algorithm relaxes those limitations, including the persistent exciting condition, the computation of memory-expensive delayed-window integral, the full-rank condition, an intelligent data-storage, and an initial stabilizing control policy. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed algorithms, and detailed comparisons with existing algorithms are provided. © 1963-2012 IEEE.

关键词： Time varying control systems

Port-Hamiltonian Neural Networks with Output Error Noise Models

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arXiv 2025年

作者： Moradi, Sarvin Beintema, Gerben I. Jaensson, Nick Tóth, Roland Schoukens, Maarten Control System group Eindhoven University of Technology Eindhoven Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural networks. However, their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements. This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors. We integrate port-Hamiltonian theory into the neural network structure, allowing for the inclusion of external inputs and dissipation, while mitigating the impact of measurement noise through an output-error (OE) model structure. The resulting output error port-Hamiltonian neural networks (OE-pHNNs) can be adapted to tackle modeling complex engineering systems with noisy measurements. Furthermore, we propose the identification of OE-pHNNs based on the subspace encoder approach (SUBNET), which efficiently approximates the complete simulation loss using subsections of the data and uses an encoder function to predict initial states. By integrating SUBNET with OE-pHNNs, we achieve consistent models of complex engineering systems under noisy measurements. In addition, we perform a consistency analysis to ensure the reliability of the proposed data-driven model learning method. We demonstrate the effectiveness of our approach on system identification benchmarks, showing its potential as a powerful tool for modeling dynamic systems in real-world applications. © 2025, CC BY-SA.

关键词： Hamiltonians

Layered Nonlinear Model Predictive control for Robust Stabilization of Hybrid systems

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arXiv 2025年

作者： Olkin, Zachary Ames, Aaron D. Department of Control and Dynamical Systems California Institute of Technology PasadenaCA91125 United States

Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation. To address this challenge, we propose a layered Model Predictive control (MPC) architecture for robust stabilization of hybrid systems. A high level "hybrid" MPC is solved at a slow rate to produce a stabilizing hybrid trajectory, potentially sub-optimally, including a domain and guard sequence. This domain and guard sequence is passed to a low level "fixed mode" MPC which is a traditional, time-varying, state-constrained MPC that can be solved rapidly, e.g., using nonlinear programming (NLP) tools. A robust version of the fixed mode MPC is constructed by using tracking error tubes that are not guaranteed to have finite size for all time. Using these tubes, we demonstrate that the speed at which the fixed mode MPC is re-calculated is directly tied to the robustness of the system, thereby justifying the layered approach. Finally, simulation examples of a five link bipedal robot and a controlled nonlinear bouncing ball are used to illustrate the formal results. © 2025, CC BY.

关键词： Predictive control systems

Impact of Leg Stiffness on Energy Efficiency in One Legged Hopping

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arXiv 2025年

作者： Khemakhem, Iskandar Tschemernjak, Dominik Raff, Maximilian Remy, C. David Institute for Nonlinear Mechanics University of Stuttgart StuttgartD-70569 Germany Institute for Systems Theory and Automatic Control University of Stuttgart StuttgartD-70569 Germany

In the fields of robotics and biomechanics, the integration of elastic elements such as springs and tendons in legged systems has long been recognized for enabling energyefficient locomotion. Yet, a significant challenge persists: designing a robotic leg that perform consistently across diverse operating conditions, especially varying average forward speeds. It remains unclear whether, for such a range of operating conditions, the stiffness of the elastic elements needs to be varied or if a similar performance can be obtained by changing the motion and actuation while keeping the stiffness fixed. This work explores the influence of the leg stiffness on the energy efficiency of a monopedal robot through an extensive parametric study of its periodic hopping motion. To this end, we formulate an optimal control problem parameterized by average forward speed and leg stiffness, solving it numerically using direct collocation. Our findings indicate that, compared to the use of a fixed stiffness, employing variable stiffness in legged systems improves energy efficiency by 20% maximally and by 6.8% on average across a range of speeds. Copyright © 2025, The Authors. All rights reserved.

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Data-Driven Estimation of Structured Singular Values

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arXiv 2025年

作者： Guerrero, Margarita A. Lakshminarayanan, Braghadeesh Rojas, Cristian R. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm100 44 Sweden

Estimating the size of the modeling error is crucial for robust control. Over the years, numerous metrics have been developed to quantify the model error in a control relevant manner. One of the most important such metrics is the structured singular value, as it leads to necessary and sufficient conditions for ensuring stability and robustness in feedback control under structured model uncertainty. Although the computation of the structured singular value is often intractable, lower and upper bounds for it can often be obtained if a model of the system is known. In this paper, we introduce a fully data-driven method to estimate a lower bound for the structured singular value, by conducting experiments on the system and applying power iterations to the collected data. Our numerical simulations demonstrate that this method effectively lower bounds the structured singular value, yielding results comparable to those obtained using the Robust control toolbox of MATLAB. © 2025, CC BY.

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Fine Tuning a Data-Driven Estimator

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arXiv 2025年

作者： Lakshminarayanan, Braghadeesh Rojas, Cristian R. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm100 44 Sweden

In recent years, many industries have developed high-fidelity simulators, such as digital twins, to represent physical systems, although their parameters must be calibrated to accurately reflect the true system. This need has paved the way for the creation of data-driven parameter estimators to calibrate such simulators. These estimators are constructed by generating synthetic observations for various parameter settings of the simulator and then establishing a mapping from these observations to the corresponding parameter settings using supervised learning. However, if the true system’s parameters fall outside the range of the sampled parameter set used to construct the mapping, the resulting predictions will suffer from out-of-distribution (OOD) issues. In this paper, we introduce a fine-tuning approach for a specific data-driven estimator, known as the Two-Stage estimator, designed to mitigate the problems associated with OOD and improve its accuracy. © 2025, CC BY.

关键词： Self-supervised learning

Risk Assessment Based Time-adjusted Optimization for Safe and Fast Flight of Quadrotor UAVs

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IEEE Transactions on Vehicular technology 2025年

作者： Zhu, Hai Li, Baoquan Yin, Haolin Zhang, Xuebo Tiangong University School of Control Science and Engineering The Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin300387 China Nankai University Institute of Robotics and Automatic Information Systems The Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

Generating an executable reference trajectory in realtime is a core of autonomous navigation for a quadrotor unmanned aerial vehicle (UAV). In the paper, a timeadjusted optimization method is proposed for UAV motion planning to achieve safe and fast flight. Firstly, based on parameterization for uniform B-spline trajectories, relationship of knot spans and UAV speeds is analyzed, and an iterative velocity restrict algorithm is introduced to tackle nonexecutable flight. Subsequently, combining start and target points in local planning, an evaluation criterion function is designed with control points of the above trajectory and directions of collision avoidance. On this basis, risk assessment is conducted according to surrounding environments, where four phases of passing obstacles is constructed by considering risk level variation. Then, the weight function in each phase is determined by relationship of obstacle and planned local trajectory. Moreover, time intervals of B-spline trajectories are adjusted to obtain nonuniform trajectories, so as to affect UAV speeds. Simulation and experiment are conducted with comparison to verify performance of the proposed method. © 2025 IEEE.

关键词： Risk assessment

OpenCV-based Recognition Method for Computer Interlocking Host Computer Interface

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OpenCV-based Recognition Method for Computer Interlocking Ho...

IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： Jiapeng Li Wenzhen Kuang Tao He Yajie Li Yanpeng Wu Key Lab of Opt-Electronic Technology and Intelligent Control of Minisitry of Education Lanzhou Jiaotong University Lanzhou China Automatic Control Institute Lanzhou Jiaotong University Lanzhou China Gansu Rail Transit Signal and Control Evaluation Industry Technology Center Lanzhou

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In order to improve the efficiency of identifying the operation of computer interlocking host computer interface and promote the improvement of automation, combined with the display standards and requirements of the host computer interface, this paper proposes an image analysis method for the host computer interface based on Open CV. The method accurately extracts the coordinate information on the screen by quantifying the geometric features of various types of signal devices in the host computer interface. Through this process, it can realise the automatic identification, obtain the whole station's signal layout map from the image input, and make preparations for the subsequent automated test.

关键词： Computer interfaces Image analysis Accuracy Text recognition Software algorithms Layout Feature extraction Software Libraries Data mining

Collision avoidance and routing based on location access (CARLA) of mobile robots

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Neural Computing and Applications 2025年第9期37卷 6401-6430页

作者： ElSayyad, Shimaa Ezzat Saleh, Ahmed I. Ali, Hesham A. Saraya, M.S. Rabie, Asmaa H. Abdelsalam, Mohamed M. Computers Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Round Road Mansoura City35516 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Dean of Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Mansoura Egypt

The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios. © The Author(s) 2025.

关键词： Mobile robots