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IEEE Conference on Decision and control

作者： Patricia Pauli Ruigang Wang Ian R. Manchester Frank Allgöwer Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Australia

We establish a layer-wise parameterization for 1D convolutional neural networks (CNNs) with built-in end-to-end robustness guarantees. In doing so, we use the Lipschitz constant of the input-output mapping characterized by a CNN as a robustness measure. We base our parameterization on the Cayley transform that parameterizes orthogonal matrices and the controllability Gramian of the state space representation of the convolutional layers. The proposed parameterization by design fulfills linear matrix inequalities that are sufficient for Lipschitz continuity of the CNN, which further enables unconstrained training of Lipschitz-bounded 1D CNNs. Finally, we train Lipschitz-bounded 1D CNNs for the classification of heart arrythmia data and show their improved robustness.

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Time-Optimal Trajectory Planning in Highway Scenarios Using Basis-Spline Parameterization

Time-Optimal Trajectory Planning in Highway Scenarios Using ...

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International Conference on Intelligent Transportation

作者： Philip Dorpmüller Thomas Schmitz Naveen Bejagam Torsten Bertram TU Dortmund University Institute of Control Theory and Systems Engineering Dortmund Germany ZF Group Automated Driving and Integral Cognitive Safety Düsseldorf Germany

Basis splines enable a time-continuous feasibility check with a finite number of constraints. Constraints apply to the whole trajectory for motion planning applications that require a collision-free and dynamically feasible trajectory. Existing motion planners that rely on gradient-based optimization apply time scaling to implement a shrinking planning horizon. They neither guarantee a recursively feasible trajectory nor enable reaching two terminal manifold parts at different time scales. This paper proposes a nonlinear optimization problem that addresses the drawbacks of existing approaches. Therefore, the spline breakpoints are included in the optimization variables. Transformations between spline bases are implemented so a sparse problem formulation is achieved. A strategy for breakpoint removal enables the convergence into a terminal manifold. The evaluation in an overtaking scenario shows the influence of the breakpoint number on the solution quality and the time required for optimization.

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An $\mathcal{L}_{\mathrm{p}}$ -norm framework for event-triggered control

An $\mathcal{L}_{\mathrm{p}}$ -norm framework for event-trig...

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European control Conference (ECC)

作者： Michael Hertneck Alejandro I. Maass Dragan Nešić Frank Allgöwer University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart Germany Department of Electrical Engineering Pontificia Universidad Catolica de Chile Santiago Chile Department of Electrical and Electronic Engineering The University of Melbourne Parkville Victoria Australia

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel event-triggered control (ETC) design framework based on measured $\mathcal{L}_{p}$ norms. We consider a class of systems with finite $\mathcal{L}_{p}$ gain from the network-induced error to a chosen output. The $\mathcal{L}_{p}$ norms of the network-induced error and the chosen output since the last sampling time are used to formulate a class of triggering rules. Based on a small-gain condition, we derive an explicit expression for the $\mathcal{L}_{p}$ gain of the resulting closed-loop systems and present a time-regularization, which can be used to guarantee a lower bound on the inter-sampling times. The proposed framework is based on a different stability- and triggering concept compared to ETC approaches from the literature, and thus may yield new types of dynamical properties for the closed-loop system. However, for specific output choices it can lead to similar triggering rules as “standard” static and dynamic ETC approaches based on input-to-state stability and yields therefore a novel interpretation for some of the existing triggering rules. We illustrate the proposed framework with a numerical example from the literature.

关键词： Europe Closed loop systems Numerical stability Standards

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Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian

arXiv

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arXiv 2023年

作者： Pauli, Patricia Wang, Ruigang Manchester, Ian R. Allgöwer, Frank The Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70569 Germany The Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Australia

关键词： State space methods

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Simple controller Tuning for Unmanned Aerial Vehicles using Governors

Simple Controller Tuning for Unmanned Aerial Vehicles using ...

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International Conference on Process control (PC)

作者： Raphael Dyrska Jens Müller Miroslav Fikar Martin Mönnigmann Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava Slovakia

A simple governor-based controller tuning is implemented and tested for the application to unmanned aerial vehicles (UAVs). We show that the governor-based tuning approach enables the high-level tuning of the existing controller of the UAVs by using a single tuning parameter only. We implement the approach on two UAV platforms of different sizes and controller frameworks to prove the versatility of the given approach. The validation is performed with experimental data collected for the UAVs flying in the supervised environment of an indoor flight laboratory.

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An Intelligent Ovarian Ultrasound Image Generation Algorithm based on Generative Adversarial Networks

An Intelligent Ovarian Ultrasound Image Generation Algorithm...

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IEEE Symposium (IUS) Ultrasonics

作者： Hongbei Xiang Yue Zhao Hao Huang Kuo Miao Xiaoqiu Dong Control Theory and Engineering School of Astronautics Harbin Institute of Technology Harbin China Department of Ultrasound Fourth Affiliated Hospital of Harbin Medical University Harbin Heilongjiang China

A deep learning framework based on generative adversarial networks (GAN) for ovarian ultrasound (US) images synthesis is investigated. This method offers an effective solution for addressing the issue of insufficient and unbalanced data in ovarian disease research. The proposed network, built on the Triple-GAN model, can synthesize a large number of medical ovarian US images which are difficult to distinguish from the real ones. This approach effectively increases the available volume of the ultrasound images of ovarian diseases and facilitates deep learning applications in ovarian ultrasound images. It provides reliable training data replacements. The generated data were validated through professional appraisal, classification method and image metrics. The results demonstrated high credibility and quality. The feasibility of using the generated data in the intelligent classification algorithm of ovarian diseases is verified, which has important practical significance for the research and development of artificial intelligence diagnosis algorithms for various diseases in the future.

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A Duality-Based Approach to Inverse Kalman Filtering

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IFAC-PapersOnLine 2023年第2期56卷 10258-10263页

作者： Yibei Li Bo Wahlberg Lihua Xie Xiaoming Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Optimization and Systems Theory Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.

关键词： Inverse filtering Kalman filters duality covariance estimation identifiability

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Performance Optimization of Angle-Based Network Localization

Performance Optimization of Angle-Based Network Localization

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IEEE Conference on Decision and control

作者： Chenyang Liang Liangming Chen Yibei Li Jie Mei Lihua Xie Center for Control Science and Technology and Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China

Recent advances in sensor network localization have enabled sensor nodes to localize themselves by using the measurements of inter-node angles. According to our earlier work, the proposed angle-based localization algorithms' performance, particularly, the convergence rate, is relatively poor, which, however, has not been adequately addressed in the existing literature. Motivated by this, this paper aims to improve the performance of angle-based localization algorithms, specifically, the stability margin, convergence rate and robustness against measurement noises. Firstly, we show that the stability margin, convergence rate and robustness of angle-based localization algorithms are commonly determined by one parameter, namely, the minimum eigenvalue of the network's localization matrix. Secondly, we formulate the performance optimization problem as an eigenvalue optimization problem, and show the non-differentiability of the eigenvalue optimization problem. By carefully choosing the decision variable, we utilize interior-point methods to obtain an optimal solution to the eigenvalue optimization problem. Finally, simulation examples validate the improvement of the algorithms' performance.

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Simple Tuning of Arbitrary controllers using Governors

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IFAC-PapersOnLine 2023年第2期56卷 8439-8444页

作者： Miroslav Fikar Karol Kiš Martin Klaučo Martin Mönnigmann Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinskeho 9 SK-812 37 Bratislava Automatic Control and Systems Theory Ruhr-Universität Bochum Germany

The paper proposes a preliminary idea to an intuitive and straightforward mechanism for tuning arbitrary controllers and changing the closed-loop performance. While the structure and parameters of the original controller are kept unchanged, the inputs to the nominal controller are modified such that the closed-loop response becomes slower or faster. Such a governor setup implementation is advantageous, especially when re-tuning the original controller is impractical or impossible. The practicability and versatility of this approach is presented using several examples spanning from simple loops with PID controllers to complex nonlinear closed-loop systems with optimal and approximated explicit MPC.

关键词： Tuning constrained control predictive control controller structure governor

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Time-Optimal Trajectory Planning in Highway Scenarios using Basis-Spline Parameterization

arXiv

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arXiv 2023年

作者： Dorpmüller, Philip Schmitz, Thomas Bejagam, Naveen Bertram, Torsten TU Dortmund University Institute of Control Theory and Systems Engineering Dortmund44227 Germany ZF Group Automated Driving and Integral Cognitive Safety Düsseldorf40547 Germany

关键词： Trajectories

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