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Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE controllers Under External Uncertainties

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

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Rule-Based Energy Management Strategy of Fuel Cell/Ultracapacitor/Battery Vehicles: winner of the IEEE VTS Motor Vehicles Challenge 2020

Rule-Based Energy Management Strategy of Fuel Cell/Ultracapa...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Alessandro Ferrara Christoph Hametner Division of Process Control and Automation TU Wien Institute of Mechanics and Mechatronics Vienna Austria Christian Doppler Laboratory for Innovative Control and Monitoring of Automotive Powertrain Systems TU Wien Vienna Austria

ISBN: (数字)9781728189598

ISBN: (纸本)9781728189604

This paper focuses on the energy management of fuel cell/ultracapacitor/battery hybrid vehicles. A robust rule-based strategy is proposed to effectively reduce hydrogen consumption, increase vehicle lifetime, and handle multiple constraints. This strategy won the IEEE VTS Motor Vehicles Challenge 2020. The formulation of the control rules is heavily based on the vehicle model and the analysis of the assigned cost function. A stochastic generation of driving scenarios is proposed to deal with the limited information provided by the challenge, guarantying a robust design of the energy management strategy. The results are analyzed on a large set of synthetic driving cycles.

关键词： Hydrogen Stochastic processes Fuel cells Supercapacitors Propulsion Batteries Fuels

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Graph Optimization based Visual SLAM fusing KeyPoints and Markers

Graph Optimization based Visual SLAM fusing KeyPoints and Ma...

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Chinese control Conference (CCC)

作者： Zhen Chen Yang Zhou Fengdi Zhang Min Xu Xiangdong Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing P. R. China Beijing Aerospace Automatic Control Institute P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Simultaneous localization and mapping (SLAM) plays an important role in autonomous navigation for mobile robots. Most of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.

关键词： Cameras Jacobian matrices Simultaneous localization and mapping Optimization Visualization Trajectory

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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Evacuation control of Crowds of Pedestrians: Distributed or Decentralized? ⁎

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IFAC-PapersOnLine 2020年第5期53卷 318-323页

作者： Kecai Cao YangQuan Chen Chenglin Liu Nanjing Institute of Technology Nanjing 211167 China Mechatronics Embedded Systems and Automation Lab School of Engineering University of California Merced CA USA 95343 Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 China

Evacuation of crowds in a corridor with only one exit has been considered in this paper where distributed controllers have been constructed to regulate the crowd’s evacuation speed so that stampede tragedy can be avoided. Macroscopic model for crowds of pedestrians has been firstly derived based on inspiration of LWR (Lighthill-Whitham and Richards) model and Greenshields model from traffic system where finite volume method has been introduced to simplify the obtained PDEs into finite ODEs. Distributed evacuation controllers have been constructed based on inspiration of multi-agent system and stability analysis of obtained closed-loop system has been simplified to linear system with the delicate controllers proposed. Comparisons between distributed evacuation controllers and previous evacuation controllers have also been presented to show the advantages and disadvantages of different controllers under different initial density of crowds so that tragedy of crowd stampede can be effectively avoided with appropriate selection of different controllers.

关键词： Distributed evacuation controller Decentralized evacuation controller Stampede

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Zeroth-order algorithms for stochastic distributed nonconvex optimization

arXiv

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arXiv 2021年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(√p=(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p=(nT)) convergence rate when the global cost function additionally satisfies the Polyak-Lojasiewicz (P-L) condition in addition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms, which enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relative bounded second moment assumptions and the P-L condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

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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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Fault-tolerant control based on Back-stepping and Fixed-time Disturbance Estimation for Hypersonic Reentry Vehicles

Fault-tolerant Control based on Back-stepping and Fixed-time...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Tiancai Wu Honglun Wang Yue Yu Na Li Yiheng Liu Shenyuan Honors College School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China Unmanned Systems Research Institute Beihang University Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

In this paper, a novel robust back-stepping fault- tolerant control scheme is proposed, which enables hypersonic reentry vehicles with model uncertainties to accurately track the attitude commands under the condition of both unknown actuator faults and external disturbances. Firstly, a control-oriented hypersonic reentry vehicle model is established on the basis of regarding three factors mentioned above as lumped disturbance. Then, a fixed-time observer is used to achieve the fixed-time lumped disturbance estimation. Based on the disturbance observer and back-stepping technology, the proposed fault-tolerant control laws are designed to ensure control performance. Meanwhile, the “differential explosion” problem is solved by the sigmoid function-based tracking differentiator. Finally, the stability and effectiveness of the proposed controller are verified from theoretical analysis and extensive numerical simulations respectively.

关键词： automation Field-flow fractionation Electrical engineering Attitude control Heart rate variability Fault tolerant systems Fault tolerance

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Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

arXiv

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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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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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