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Equilibrium-Independent control of Continuous-Time Nonlinear Systems via the LPV Framework - Extended Version

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arXiv 2023年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

A Linear Parameter-Varying Approach to Data Predictive control

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arXiv 2023年

作者： Verhoek, Chris Berberich, Julian Haesaert, Sofie Tóth, Roland Abbas, Hossam S. The Control Systems Group Eindhoven University of Technology Netherlands The University of Stuttgart Institute for Systems Theory and Automatic Control Germany The Institute for Electrical Engineering in Medicine Universität zu Lübeck Germany The Systems and Control Lab Institute for Computer Science and Control Hungary

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system. © 2023, CC BY-NC-SA.

关键词： Predictive control systems

Autonomous driving of trucks in off-road environment

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arXiv 2023年

作者： Caldas, Kenny A.Q. Barbosa, Filipe M. Silva, Junior A.R. Santos, Tiago C. Gomes, Iago P. Rosero, Luis A. Wolf, Denis F. Grassi, Valdir Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil

Off-road driving operations can be a challenging environment for human conductors as they are subject to accidents, repetitive and tedious tasks, strong vibrations, which may affect their health in the long term. Therefore, they can benefit from a successful implementation of autonomous vehicle technology, improving safety, reducing labor costs and fuel consumption, and increasing operational efficiency. The main contribution of this paper is the experimental validation of a path tracking control strategy, composed of longitudinal and lateral controllers, on an off-road scenario with a fully-loaded heavy-duty truck. The longitudinal control strategy relies on a Non-Linear Model Predictive controller (NMPC), which considers the path geometry and simplified vehicle dynamics to compute a smooth and comfortable input velocity, without violating the imposed constraints. The lateral controller is based on a Robust Linear Quadratic Regulator (RLQR), which considers a vehicle model subject to parametric uncertainties to minimize its lateral displacement and heading error, as well as ensure stability. Experiments were carried out using a fully-loaded vehicle on unpaved roads in an open-pit mine. The truck followed the reference path within the imposed constraints, showing robustness and driving smoothness. © 2023, CC BY.

关键词： controllers

Adaptive-Grained Label Distribution Learning 39

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Adaptive-Grained Label Distribution Learning

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yunan Li, Weiwei Liu, Dun Li, Huaxiong Jia, Xiuyi School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Economics and Management Southwest Jiaotong University Chengdu China Department of Control Science and Intelligence Engineering Nanjing University Nanjing China

ISBN: (纸本)157735897X

Label polysemy, where an instance can be associated with multiple labels, is common in real-world tasks. LDL (label distribution learning) is an effective learning paradigm for handling label polysemy, where each instance is associated with a label distribution. Although numerous LDL algorithms have been proposed and achieved satisfactory performance on most existing datasets, they are typically trained directly on the collected label distributions which often lack quality guarantees in real-world tasks due to annotator subjectivity and algorithm assumptions. Consequently, direct learning from such uncertain label distributions can lead to unpredictable generalization performance. To address this problem, we propose an adaptive-grained label distribution learning framework whose main idea is to extract relatively reliable supervision information from unreliable label distributions, and thus the label distribution learning task can be decomposed into three subtasks: coarsening label distributions, learning coarse-grained labels and refining coarse-grained labels. In this framework, we design an adaptive label coarsening algorithm to extract an optimal coarsen-grained labels and a label refining function to enhance the coarse-grained label into the final label distributions. Finally, we conduct extensive experiments on real-world datasets to demonstrate the advantages of our proposal. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

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Sensors 2024年第10期24卷 3098页

作者： Chi, Tsun-Kuang Chen, Tsung-Yi Lin, Yu-Chen Lin, Ting-Lan Zhang, Jun-Ting Lu, Cheng-Lin Chen, Shih-Lun Li, Kuo-Chen Abu, Patricia Angela R. Department of Electronic Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information Management Chung Yuan Christian University Taoyuan320317 Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Quezon1108 Philippines

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. © 2024 by the authors.

关键词： Deep learning

AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

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arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： Nonlinear programming

Method for Measuring Leaf Area of Brassica Napus Using Image Analysis 21

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Method for Measuring Leaf Area of Brassica Napus Using Image...

5th International Conference on computer science and Application engineering, CSAE 2021

作者： Liu, Zhenqi Liu, Changhua Chen, Jiaxi University of Sheffield Automatic Control and Systems Engineering South Yorkshire Sheffield United Kingdom School of Mathematics and Computer Science Wuhan Polytechnic University Hubei Wuhan China

ISBN: (纸本)9781450389853

Leaf is an important part of plants. A rapid and accurate detection of plant leaf area is an important guidance to reasonable fertilizer application and accurate sprinkler irrigation. In order to solve the problem of large error and incomplete target region in segmenting leaves when people using Otus's threshold method, a method based on watershed algorithm was proposed to segment the leaves of Brassica napus. Firstly, the images of relevant crop leaves are collected by the digital camera on site. Then these images are preprocessed by Sobel operator and morphological operation. Thirdly, labeled images are segmented by watershed algorithm to get target images. Finally the leaf area is calculated by counting the number of pixels in target area. The experimental results show that this method can solve the problem of incomplete segmentation of the Otus's threshold method and effectively reduce the measurement error to less than 2.2%.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Mathematical morphology

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

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arXiv 2023年

作者： Amer, Abdelhakim Mehndiratta, Mohit le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark GIM Robotics Espoo Finland The Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

Automated visual inspection of on-and offshore wind 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 are open-sourced. © 2023, CC BY.

关键词： Inspection