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arXiv 2023年

作者： Zagorowska, Marta König, Christopher Yu, Hanlin Balta, Efe C. Rupenyan, Alisa Lygeros, John Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway Control and Automation Group Inspire AG Switzerland Automatic Control Laboratory ETH Zürich Switzerland ZHAW Centre for Artificial Intelligence ZHAW Zürich University of Applied Sciences Switzerland

Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationally inefficient. In this paper, we propose a novel approach to safe learning by formulating a series of optimization problems instead of a grid search. We also develop a method for initializing the optimization problems to guarantee feasibility while using numerical solvers. The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision. Experimental validation on an industrial-grade precision motion system confirms that the proposed algorithm achieves 30% better tracking at sub-micrometer precision as a state-of-the-art safe learning algorithm, improves the default auto-tuning solution, and reduces the computational cost seven times compared to learning algorithms based on exhaustive search. © 2023, CC BY.

关键词： Learning algorithms

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Modeling and Optimal control of a Hydrogen Storage System for Wind Farm Output Power Smoothing 59

Modeling and Optimal Control of a Hydrogen Storage System fo...

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59th IEEE Conference on Decision and control, CDC 2020

作者： Abdelghany, Muhammad Bakr Faisal Shehzad, Muhammad Liuzza, Davide Mariani, Valerio Glielmo, Luigi University of Sannio Grace Group for Research on Automatic Control Engineering Department of Engineering Benevento82100 Italy

ISBN: (纸本)9781728174471

This paper proposes an output power smoothing strategy for a grid-connected wind-hydrogen plant. An Energy Storage System (ESS) composed of an electrolyzer and a fuel cell is used to smooth the fluctuating output power of the wind plant. The aim of this study is to propose a multi-objective optimization model for joint wind farm and energy storage operation, to smooth the wind power output, and to track a load demand subject to a variety of constraints on the system model. Based on a modeling adopting mixed-integer constraints and dynamics, the problem of output power fluctuations and smooth tracking of load demand is solved through the implementation of an optimal controller which follows a sequential optimization technique. We illustrate the effectiveness of the proposed controller with a simulation example, employing real equipment data and wind profiles. © 2020 IEEE.

关键词： Wind power

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MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection

MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Na...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Olaya Álvarez-Tuñón Hemanth Kanner Luiza Ribeiro Marnet Huy Xuan Pham Jonas le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiRLab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

This paper presents MIMIR-UW, a multipurpose underwater synthetic dataset for SLAM, depth estimation, and object segmentation to bridge the gap between theory and application in underwater environments. MIMIR-UW integrates three camera sensors, inertial measurements, and ground truth for robot pose, image depth, and object segmentation. The underwater robot is deployed within a pipe exploration scenario, carrying artificial lights that create uneven lighting, in addition to natural artefacts such as reflections from natural light and backscattering effects. Four environments totalling eleven tracks are provided, with various difficulties regarding light conditions or dynamic elements. Two metrics for dataset evaluation are proposed, allowing MIMIR-UW to be compared with other datasets. State-of-art methods on SLAM, segmentation and depth estimation are deployed and benchmarked on MIMIR-UW. Moreover, the dataset's potential for sim-to-real transfer is demonstrated by leveraging the segmentation and depth estimation models trained on MIMIR-UW in a real pipeline inspection scenario. To the best of the authors' knowledge, this is the first underwater dataset targeted for such a variety of methods. The dataset is publicly available online. https://***/remaro-network/MIMIR-UW/

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Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

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Construction Robotics 2021年第2期5卷 131-146页

作者： Meng, Yiping Sun, Yiming Chang, Wen-shao People Environments and Performance Group School of Architecture The University of Sheffield Sheffield UK Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

In this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

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Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection

Visual Tracking Nonlinear Model Predictive Control Method fo...

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International Conference on Advanced Robotics (ICAR)

作者： Abdelhakim Amer Mohit Mehndiratta Jonas le Fevre Sejersen Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark GIM Robotics Espoo Finland Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes 1 1 https://***/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced.

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A Special Issue:"Key Technologies of Virtual Power Plant and Distributed Energy Resources"for Global Energy Interconnection

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全球能源互联网（英文版） 2024年第2期7卷前插1-前插2页

作者： Wei Pei Xiaojun Shen Xin Zhang North China Electric Power University BeijingChina Electrical Engineering Department Shanghai Jiaotong UniversityShanghaiChina University of Sheffield United Kingdom Electrical Machines and Drives Research Group and the Department of Automatic Control and Systems Engineering UK Research and Innovation (UKRI)

The increasing penetration of renewable and distributed energy resources (DERs) is transforming the power grid into a new type of clean and low-carbon power system. However, the uncertainty and volatility of DERs have also brought severe challenges to the secure and reliable operation of the power systems. In order to successfully integrate renewable DERs, virtual power plant (VPP) has emerged as a new technique for coordinating demand-side DERs, which has drawn significant attention from industry and academia.

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Bayesian Optimization-based Nonlinear Adaptive PID controller Design for Robust Mobile Manipulation

arXiv

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arXiv 2022年

作者： Hajieghrary, Hadi Deisenroth, Marc Peter Bekiroglu, Yasemin Chalmers University of Technology Department of Electrical Engineering Automatic Control Research Group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator's joint variables while carrying an unspecified heavy load and an abrupt base movement occurs. © 2022, CC BY-NC-ND.

关键词： Closed loop systems

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Self-consistent equilibrium of a helical magnetic flux rope in a finite-pressure plasma

arXiv

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arXiv 2024年

作者： Cheremnykh, Oleg K. Fedun, Viktor Lashkin, Volodymyr M. Space Research Institute Pr. Glushkova 40 k.4/1 Kyiv03187 Ukraine Plasma Dynamics Group Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Institute for Nuclear Research Pr. Nauki 47 Kyiv03028 Ukraine

We present an analytical model of the self-consistent equilibrium of a magnetic flux rope which is obtained in cylindrical geometry. The equilibrium azimuthal magnetic field and plasma pressure are determined in a self-consistent way through the current density which is derived as a solution of a nonlinear equation. By minimizing the energy functional, it was shown that the constrained equilibrium state is stable. The obtained results are also applicable to the cylindrical tokamak magnetic configurations. It is shown that the analytically predicted radial profiles of equilibrium quantities are in good agreement with the experimental data. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear equations

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Distributed Finite-Time k-means Clustering with Quantized Communucation and Transmission Stopping

Distributed Finite-Time k-means Clustering with Quantized Co...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Gabriele Oliva Christoforos N. Hadjicostis Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Engineering Unit of Automatic Control Università Campus Bio-Medico di Roma Rome Italy Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we present a distributed version of the k-means algorithm for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. Our distributed algorithm allows each node to transmit quantized values in an event-driven fashion, and exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck, whereas distributed stopping preserves available resources. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents in mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.

关键词： Clustering algorithms Bandwidth Partitioning algorithms Distributed algorithms Multi-agent systems

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Benchmarking local motion planners for navigation of mobile manipulators

arXiv

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arXiv 2022年

作者： Tafnakaji, Sevag Hajieghrary, Hadi Teixeira, Quentin Bekiroglu, Yasemin Department of Electrical Engineering Automatic Control Research Group Chalmers University of Technology Göteborg Sweden University College London Statistical Machine Learning Group AI Center London United Kingdom

There are various trajectory planners for mobile manipulators. It is often challenging to compare their performance under similar circumstances due to differences in hardware, dissimilarity of tasks and objectives, as well as uncertainties in measurements and operating environments. In this paper, we propose a simulation framework to evaluate the performance of the local trajectory planners to generate smooth, and dynamically and kinematically feasible trajectories for mobile manipulators in the same environment. We focus on local planners as they are key components that provide smooth trajectories while carrying a load, react to dynamic obstacles, and avoid collisions. We evaluate two prominent local trajectory planners, Dynamic-Window Approach (DWA) and Time Elastic Band (TEB) using the metrics that we introduce. Moreover, our software solution is applicable to any other local planners used in the Robot Operating System (ROS) framework, without additional programming effort. © 2022, CC BY.

关键词： Trajectories

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