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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Adaptive sliding mode back-stepping control for hypersonic flight vehicle with state constraints and unmodeled disturbances

Adaptive sliding mode back-stepping control for hypersonic f...

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International Conference on control and automation (ICCA)

作者： Fei Ma Yunjie Wu Xiaocen Liu Bohao Li Xiaodong Liu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China and Technology on Aircraft Control Laboratory Beijing China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This study presents a finite-time adaptive sliding mode back-stepping controller (FASMBC) for hypersonic flight vehicle (HFV), where the state constraints and unmodeled disturbances are considered. Firstly, the longitudinal model of HFV is decomposed into altitude subsystem and velocity subsystem. Then, sliding mode control and back-stepping logic based on barrier Lyapunov function are merged to design the robust controller for each subsystem. Besides, the adaptive strategy is applied to estimate the unmodeled disturbances. A tracking differentiator is introduced to avoid the "explosion of terms" problem involved in traditional back-stepping control. Finally, the simulation results is provided to illustrate the effectiveness of the proposed control scheme.

关键词： Solid modeling Adaptation models Lyapunov methods Estimation Atmospheric modeling Two dimensional displays Three-dimensional displays

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Youla Parameterization Based control Performance Degradation Online Recovery of Single Inverter control Systems in Microgrids: a PnP Approach

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IFAC-PapersOnLine 2020年第2期53卷 12080-12085页

作者： Y. Liu C. Hu H. Lu S.X. Ding L. Li X. Peng Y. Wang Institute for Automatic Control and Complex Systems University of Duisburg-Essen Bismarkstr. 81 BB 47057 Duisburg Germany College of Electrical and Control Engineering North China University of Technology 100144 Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology200237 Shanghai China

In an inverter control system of smart microgrids, the control performance degradation due to load fluctuations, unbalanced loads and other factors, is one of the considerable factors affecting the output power quality. In this paper, a plug-and-play (PnP) technology based on Youla parameterization is proposed to achieve system performance recovery without modifying the existing controller. By ‘plugging-in’ a residual generator and a compensator, a dynamic feed-back of residual signals is embedded into the original inverter control system. In order to realize PnP, an online parameter estimation scheme of the compensator is developed. Finally, an inverter control system is applied to show the effectiveness of the proposed approach.

关键词： Youla parameterization performance degradation online recovery power quality microgrids

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Graphical Minimax Game and On-Policy Reinforcement Learning for Consensus of Leaderless Multi-Agent Systems *

Graphical Minimax Game and On-Policy Reinforcement Learning ...

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International Conference on control and automation (ICCA)

作者： Wei Dong Chunyan Wang Jinna Li Jianan Wang Key Laboratory of Dynamics and Control of Flight Vehicle within Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing China School of Information and Control Engineering Liaoning Shihua University Fushun China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

In this paper, we study the adaptive optimal consensus control of leaderless multi-agent systems (MASs) with heterogeneous dynamics. First, the consensus control problem is converted into a graphical minimax game problem and the corresponding algebraic Riccati equation (ARE) for each agent is obtained. Then, an on-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without requiring the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed online learning algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation.

关键词： Games Heuristic algorithms Reinforcement learning Mathematical model Vehicle dynamics Performance analysis Optimal control

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Self-Optimizing Grinding Machines using Gaussian process Models and Constrained Bayesian Optimization

arXiv

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arXiv 2020年

作者： Maier, Markus Rupenyan, Alisa Bobst, Christian Wegener, Konrad AG Zurich Switzerland Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Switzerland Agathon AG Bellach Switzerland Institute for Machine Tools Department of Mechanical and Process Engineering ETH Zurich Switzerland

In this study, self-optimization of a grinding machine is demonstrated with respect to production costs, while fulfilling quality and safety constraints. The quality requirements of the final workpiece are defined with respect to grinding burn and surface roughness, and the safety constrains are defined with respect to the temperature at the grinding surface. Grinding temperature is measured at the contact zone between grinding wheel and workpiece using a pyrometer and an optical fiber, which is embedded inside the rotating grinding wheel. Constrained Bayesian optimization combined with Gaussian process models is applied to determine the optimal feed rate and cutting speed of a cup wheel grinding machine manufacturing tungsten carbide cutting inserts. The approach results in the determination of optimal parameters for unknown workpiece and tool combinations after only a few grinding trials. It also incorporates the uncertainty of the constraints in the prediction of optimal parameters by using stochastic process models1. Copyright © 2020, The Authors. All rights reserved.

关键词： Grinding (machining)

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Output Feedback Based Iterative Learning control with Finite Frequency Range Specifications via a Heuristic Approach for Batch processes with Polytopic Uncertainties ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1397-1402页

作者： Shoulin Hao Tao Liu Wojciech Paszke Hongfeng Tao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China School of Control Science and Engineering Dalian University of Technology Dalian 116024 China Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 China

For robust control and iterative optimization of industrial batch processes with polytopic uncertainties, this paper proposes a robust output feedback based iterative learning control (ILC) design in terms of finite frequency range stability specifications. Robust stability conditions for the closed-loop ILC system along both time and batch directions are first established based on the generalized Kalman-Yakubovich-Popov lemma and linear repetitive system theory. To facilitate the ILC controller design with respect to process uncertainties described in a polytopic form, extended sufficient conditions for the system stability are then derived in terms of matrix inequalities. Correspondingly, a two-stage heuristic approach is developed to iteratively compute feasible ILC controller gains for implementation. An illustrative example is given to demonstrate the effectiveness of the proposed control design.

关键词： Batch processes polytopic uncertainties output feedback iterative learning control finite frequency range design

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Nonlinearity Measures for Distributed Parameter and Descriptor Systems

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IFAC-PapersOnLine 2020年第2期53卷 7545-7550页

作者： Pedro Reyero-Santiago Carlos Ocampo-Martinez Rolf Findeisen Richard D. Braatz Automatic Control Department Universitat Politècnica de Catalunya Institut de Robòtica i Informàtica Industrial (CSIC-UPC) 08028 Barcelona Spain Institute for Automation Engineering (IFAT) Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Universität Magdeburg 39106 Magdeburg Germany Massachusetts Institute of Technology Room E19-551 77 Massachusetts Avenue Cambridge MA 02139 USA

control design and state estimation are usually more straightforward for linear than for nonlinear dynamical systems, which has motivated the development of methods for quantifying the extent of nonlinearity in dynamical systems. Although many well-defined methods have been proposed for systems described by ordinary differential equations, such methods are not as well explored for dynamical systems described by PDEs and descriptor systems that represent most chemical processes. This paper reviews, discusses, and compares methods for the definition and computation of nonlinearity measures. The measures are categorized in terms of open- vs. closed-loop control topologies, theoretical vs. numerically computed, state transformation dependency, input scaling dependency, linearization vs. optimized linear modeling vs. average linear modeling, applicability to unstable dynamical systems, and applicability to the right-hand side of the state equation or to input-output relationships. Then extensions of the nonlinearity measures are discussed for hybrid systems and those described by coupled differential, integral, and algebraic equations, often referred to as descriptor/singular systems.

关键词： nonlinear systems process control nonlinear measures descriptor systems distributed parameter systems

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Learning References with Gaussian processes in Model Predictive control applied to Robot Assisted Surgery

Learning References with Gaussian Processes in Model Predict...

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European control Conference (ECC)

作者： Janine Matschek Tim Gonschorek Magnus Hanses Norbert Elkmann Frank Ortmeier Rolf Findeisen Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Univ. Magdeburg Chair of Software Engineering Otto-von-Guericke Univ. Magdeburg Fraunhofer-Institute for Factory Operation and Automation Magdeburg

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive and learning based prediction models can provide suitable predictions to the controller and therefor can be applied to overcome this issue. We propose to learn references for model predictive controllers via Gaussian processes. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator must follow a learned reference position based on optical tracking measurements.

关键词： Predictive models Gaussian processes Robots Predictive control Task analysis control systems Tracking

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Time-delay assisted mechanism and adaptive LSTM hybrid train braking model of heavy haul trains

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control engineering Practice 2025年 163卷

作者： Qiang Liu Kexuan Xu Yating Fu Jiang Liu Ling Liu State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) Shenyang 110819 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China Frontiers Science Center for Smart High-speed Railway System Beijing 100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 China Beijing National Railway Research & Design Institute of Signal Communication Group Company Beijing 100070 China

The train braking model (TBM) that describes the dynamic relations of operation speed, mileage, and control force is essential for achieving stable operation and precise stopping of heavy haul trains (HHTs). However, it is difficult to establish the TBM of HHTs due to complex characteristics: (i) the long body and air braking process of the HHTs may lead to unexpected time-delays of control force; and (ii) there are significant unmodeled dynamics caused by rough tracks and external poor environment. Traditional TBM does not take into account the unmodeled dynamics and time-delays caused by air transmission during braking. To address these issues, this study proposes a data mechanism hybrid modeling strategy, which incorporates a braking time-delay assisted mechanism model and an adaptive long and short-term memory (LSTM) model. A new Bayesian optimization based time-delay estimation method is first proposed to determine unknown time-delays of each carriage and the estimated time-delays are incorporated to generate the multi-point-mass kinetic mechanism model. Moreover, the error of the mechanism-driven model is adaptively compensated by a sliding window LSTM model to conduct the unmodeled dynamics. The effectiveness of the proposed method is demonstrated using the field data.

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