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American control Conference (ACC)

作者： Maurice Poot Jim Portegies Noud Mooren Max van Haren Max van Meer Tom Oomen Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Eindhoven The Netherlands Department of Mathematics and Computer Science CASA Eindhoven University of Technology Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781665451963

ISBN: (纸本)9781665494809

Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.

关键词： Mechatronics Gaussian processes Machine learning Printers Feedforward systems Motion control Substrates

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Synchronous Online Abstract Dynamic Programming

Synchronous Online Abstract Dynamic Programming

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Xiuxian Li Min Meng Lihua Xie Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper addresses the abstract dynamic programming (DP) in the online scenario, where the abstract DP mapping is time-varying, instead of static. In this case, optimal costs and policies at different time instants are not the same in general, and the problem amounts to tracking time-varying optimal costs and policies, which is of interest to many practical problems. It is thus necessary to analyze the performance of classical value iteration (VI) and policy iteration (PI) algorithms in the online case. In doing so, this paper develops and provides the theoretical analysis for several online algorithms, including approximate online VI, online PI, approximate online PI, online optimistic PI, and approximate online optimistic PI algorithms. It is proved that the tracking error bounds for all algorithms critically depend upon the largest difference between any two consecutive abstract mappings. Meanwhile, examples are presented to illustrate the theoretical results.

关键词： Costs Automation Law enforcement Heuristic algorithms Approximation algorithms Dynamic programming Robots

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Accelerating Soft-Constrained MPC for Linear systems Through Online Constraint Removal

Accelerating Soft-Constrained MPC for Linear Systems Through...

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IEEE Conference on Decision and control

作者： S.A.N. Nouwens M.M. Paulides W.P.M.H. Heemels Department of Mechanical Engineering Control Systems Technology section Eindhoven University of Technology Eindhoven The Netherlands Department of Electrical Engineering Electromagnetics for Care & Cure Eindhoven University of Technology Eindhoven The Netherlands Department of Radiotherapy Erasmus University Medical Center Cancer Institute Rotterdam The Netherlands

Optimization-based controllers, such as Model Predictive control (MPC), have attracted significant research interest due to their intuitive concept, constraint handling capabilities, and natural application to multi-input multi-output systems. However, the computational complexity of solving a receding horizon problem at each time step remains a challenge for the deployment of MPC. This is particularly the case for systems constrained by many inequalities. Recently, we introduced the concept of constraint-adaptive MPC (ca-MPC) to address this challenge for linear systems with hard constraints. In ca-MPC, at each time step, a subset of the constraints is removed from the optimization problem, thereby accelerating the optimization procedure, while resulting in identical closed-loop behavior. The present paper extends this framework to soft-constrained MPC by detecting and removing constraints based on sub-optimal predicted input sequences, which is rather easy for soft-constrained MPC due to the receding horizon principle and the inclusion of slack variables. We will translate these new ideas explicitly to an offset-free output tracking problem. The effectiveness of these ideas is demonstrated on a two-dimensional thermal transport model, showing a three order of magnitude improvement in online computational time of the MPC scheme.

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Energy-Efficient Path-Planning for UAV Swarm Based Missions: A Genetic Algorithm Approach

Energy-Efficient Path-Planning for UAV Swarm Based Missions:...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Georgios P. Kladis Lefteris Doitsidis Nikos C. Tsourveloudis Department of Aeronautical Science Automatic Control Aerospace Technology Defence Systems and Operations Academic Section Hellenic Air Force Academy Dekeleia Greece School of Production Engineering and Management Technical University of Crete Chania Crete Greece

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In all applications involving swarms, it's crucial for the group to achieve its objectives safely and with efficient energy utilisation, while adhering to constraints and meeting mission requirements. This article focuses on addressing the offline path planning problem for Unmanned Aerial Vehicles (UAVs), with a specific emphasis on enhancing energy efficiency. Each UAV in the swarm is guided along a candidate path represented by a Bézier curve, which evolves through a two-step procedure. Firstly, a genetic algorithm (GA) normalises the fitness function to ensure fair comparison of traits. Secondly, a multi-objective swarm-based path planning approach is employed to find the most energy-efficient and safe route for the swarm, meeting predefined criteria. The designed solution paths accommodate the functional and physical limitations of aerial vehicles, while also considering factors such as vessel traffic and weather conditions in the operational area. Simulation examples demonstrate the effectiveness of this approach.

关键词： Autonomous aerial vehicles Energy efficiency Path planning Air traffic control Aircraft Genetic algorithms Meteorology

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Designing a Trajectory control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

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Periodica Polytechnica Transportation engineering 2024年第4期52卷 412-422页

作者： Vu, Van Tan Nguyen, Quang Thinh Nakagawa, Chihiro Nguyen, Trung Nguyen Le, Thanh Long Nguyen, Van Vinh Hoang, Thi Lan Anh Do, Trong Tu Mihály, András Gáspár, Péter Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications Building A6 no. 3 Cau Giay Street Lang Thuong Ward Dong Da District Hanoi100000 Viet Nam Japan Technology Solution Joint Stock Company 9 Dau Ma Street Section 2 Thi Cau Ward Bac Ninh Bac Ninh222000 Viet Nam School of Engineering Osaka Metropolitan University 1-1 Gakuen-cho Nakaku Sakai Osaka599-8531 Japan Emergency abdominal surgery Department Viet Duc University Hospital no. 40 Trang Thi Street Hanoi100000 Viet Nam Mechanical and Power Engineering Faculty Electric Power University no. 235 Hoang Quoc Viet Street Co Nhue Ward Bac Tu Liem District Hanoi100000 Viet Nam Systems and Control Laboratory Institute for Computer Science and Control Hungarian Research Network Kende u. 13-17 Budapest1111 Hungary

A wheelchair is a specialized form of Personal Mobility Vehicle (PMV) designed to help people with disabilities move safely to their desired locations. Unlike conventional PMVs, wheelchairs for people with disabilities are challenging to manually control in environments with heavy traffic. This study focuses on designing and feasibility testing of control systems for PMVs to meet safety and efficiency standards, even in complex environments. First, a control structure model for PMVs is proposed. This is followed by the application of a combination of First In First Out (FIFO) and Dijkstra traffic control methods to determine effective trajectories for PMVs. The proposed control system is then tested on the campus of the University of Transport and Communications. Simulation results show that when comparing the travel time of 10 PMVs, the order execution time for each PMV remains stable (within 10%) for up to 20 PMVs. However, when the number of PMVs increases to 25, 30, 35, and 40 vehicles, the order execution time increases by 35%, 55%, 103%, and 119%, respectively. These results demonstrate that the system can control a large number of PMVs, ensuring safety, flexibility, and efficiency. © 2024 Budapest University of technology and Economics. All rights reserved.

关键词： Travel time

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An Aperiodic Prescribed Performance control Scheme for Uncertain Nonlinear systems

An Aperiodic Prescribed Performance Control Scheme for Uncer...

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Mediterranean Conference on control and Automation (MED)

作者： Alexandros Nikou Christos K. Verginis Shahab Heshmati-Alamdari Ericsson Research Stockholm Sweden Department of Electrical Engineering Division of Signals and Systems Uppsala University Sweden Department of Electronic Systems Section of Automation &amp Control Aalborg University Denmark

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper proposes a low-complexity feedback control law that is updated aperiodically, in an event-triggered manner, and guarantees prescribed transient and steady state performance for uncertain nonlinear systems affine in the control. By prescribed performance, we mean that the closed-loop error trajectory converges to a predefined arbitrarily small residual set, with convergence rate no less than a certain prespecified value, having maximum overshoot less than a preassigned level. The proposed novel control design is performed in the transformed normalized error, and the triggering mechanism is extracted by guaranteed that these errors always lead to bounded closed loop signals. Moreover, the approach provides a scheme of designing and tuning the control parameters in order to achieve stabilization in a desire state in a pre-defined time T >0. The efficiency of the proposed approach is verified with numerical simulations in MATLAB.

关键词： control design Numerical simulation Trajectory Steady-state Feedback control Nonlinear systems Transient analysis

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A survey on secure decentralized optimization and learning

arXiv

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arXiv 2024年

作者： Liu, Changxin Bastianello, Nicola Huo, Wei Shi, Yang Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Mechanical Engineering University of Victoria VictoriaBC Canada

Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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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

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Feedback Stability Under Mixed Gain and Phase Uncertainty

arXiv

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arXiv 2024年

作者： Liang, Jiajin Zhao, Di Qiu, Li The Googol Technology Guangdong Shenzhen China The Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

In this study, we investigate the robust feedback stability problem for multiple-input-multiple-output linear time-invariant systems involving sectored-disk uncertainty, namely, dynamic uncertainty subject to simultaneous gain and phase constraints. This problem is thereby called a sectored-disk problem. Employing a frequency-wise analysis approach, we derive a fundamental static matrix problem that serves as a key component in addressing the feedback stability. The study of this matrix problem heavily relies on the Davis-Wielandt (DW) shells of matrices, providing a profound insight into matrices subjected to simultaneous gain and phase constraints. This understanding is pivotal for establishing a less conservative sufficient condition for the matrix sectored-disk problem, from which we formulate several robust feedback stability conditions against sectored-disk uncertainty. Finally, several conditions based on linear matrix inequalities are developed for efficient computation and verification of feedback robust stability against sectored-disk uncertainty. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear time-invariant system

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