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Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification

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arXiv 2024年

作者： Kon, Johan van de Wijdeven, Jeroen Bruijnen, Dennis Tóth, Roland Heertjes, Marcel Oomen, Tom Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands Philips Engineering Solutions Eindhoven Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology Netherlands The HUN-REN Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft Netherlands

Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solving Linear Matrix Inequalities. In this paper, an unconstrained DT-LPV-IO parameterization is developed which gives a stable model for any choice of model parameters. To achieve this, it is shown that all quadratically stable DT-LPV-IO models can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball, i.e., a small-gain condition. The unit ball is then reparameterized through a Cayley transformation, resulting in an unconstrained parameterization of all quadratically stable DT-LPV-IO models. As a special case, an unconstrained parameterization of all stable DT linear time-invariant transfer functions is obtained. Identification using the stable DT-LPV-IO model with neural network coefficient functions is demonstrated on a simulation example of a position-varying mass-damper-spring system. © 2024, CC BY-NC-ND.

关键词： Parameterization

Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification

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Unconstrained Parameterization of Stable LPV Input-Output Mo...

European control Conference (ECC)

作者： Johan Kon Jeroen Van De Wijdeven Dennis Bruijnen Roland Tóth Marcel Heertjes Tom Oomen Departement of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology The Netherlands HUN-REN Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft The Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

关键词： Computational modeling Neural networks Riccati equations Transfer functions Mathematical models Stability analysis Numerical models

Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

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arXiv 2023年

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila Automatic Control and Systems Engineering The University of Sheffield United Kingdom Department of Electrical and Computer Engineering Rowan University United States Department of Computer Science University of York United Kingdom

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

CONCENTRATION IN GOSSIP OPINION DYNAMICS OVER RANDOM GRAPHS

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arXiv 2023年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden

We study concentration inequalities in gossip opinion dynamics over random graphs. In the model, a network is generated from a random graph model with independent edges, and agents interact pairwise randomly over the network. During the process, regular agents average neighbors’ opinions and then update, whereas stubborn agents do not change opinions. To approximate the original process, we introduce a gossip model over an expected graph, obtained by averaging all possible networks generated from the random graph model. Using concentration inequalities, we derive high-probability bounds for the distance between the expected final opinion vectors over the random graph and over the expected graph. Leveraging matrix perturbation results, we show how such concentration can help study the effect of network structure on the expected final opinions in two cases: (i) When the influence of stubborn agents is large, the expected final opinions polarize and are close to stubborn agents’ opinions. (ii) When the influence of stubborn agents is small, the expected final opinions are close to each other. With the help of concentration inequalities for Markov chains, we obtain high-probability bounds for the distance between time-averaged opinions and the expected final opinions over the expected graph. In simulation, we validate the theoretical findings, and study a gossip model over a stochastic block model that has community structure. © 2023, CC BY.

关键词： Dynamics

DMCNET: Data-Driven Multi-Pilot Convolution Neural Network for Mimo-Ofdm Receiver 8

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DMCNET: Data-Driven Multi-Pilot Convolution Neural Network f...

8th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Xin, Yutong Peng, Jingyi Lu, Zejian Lee, Yong Yang, Yang China Industrial Control Systems Cyber Emergency Response Team 100029 China China Academic of Electronics and Information Technology 100041 China School of Artificial Intelligence Beijing University of Posts & Telecommunications 100876 China Collage of Electrical and Computer Engineering Sungkyunkwan University 16419 Korea Republic of

ISBN: (纸本)9798350317923

This paper focuses on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMOOFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose the data-driven multi-pilot convolution neural network for MIMO-OFDM receiver (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. Experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches. © 2023 IEEE.

关键词： MIMO systems

Abstracting Linear Stochastic systems via Knowledge Filtering

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arXiv 2023年

作者： Engelaar, Maico Hendrikus Wilhelmus Romao, Licio Gao, Yulong Lazar, Mircea Abate, Alessandro Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Department of Computer Science University of Oxford United Kingdom

In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimension of the noise disturbance and will allow any controller designed for the reduced model to be refined into a controller for the original stochastic system, while preserving any specification on the output. Although initially the reduced model will be time-varying, a method will be provided with which the reduced model can become time-invariant if it satisfies some minor technical conditions. We present our theoretical findings with an example that supports the proposed framework and illustrates how model reduction and controller refinement of stochastic systems can be achieved. We finish the paper by considering specific examples to analyze both completeness with respect to controller synthesis and model order reduction with respect to the state. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

What is the Expected Transient Behavior of Opinion Evolution for Two Communities?

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arXiv 2023年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden

We study the transient behavior of a gossip model, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within each community have identical weights, different from the weights between communities. It is shown that, at the early stage of the opinion evolution, the expected agent states in the same community have identical sign, despite influence of stubborn agents. Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between. In contrast, if the edge weights between communities are larger, then the expected states of all agents concentrate around everyone’s initial average opinion. Different from the traditional asymptotic analysis in the opinion dynamics literature, these results focus on the initial phase of opinion evolution and establish a correspondence between community structure and transient behavior of the gossip model. The results are illustrated by numerical examples. © 2023, CC BY.

关键词： Asymptotic analysis

Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

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Adaptive Sampling of Algal Blooms Using an Autonomous Underw...

control Technology and Applications (CCTA),

作者： Joana Fonseca Alexandre Rocha Miguel Aguiar Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden

This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a concentration indicates the presence of algal blooms. The proposed method learns the kernel parameters of a Gaussian process model using satellite images of chlorophyll a from previous days. The AUV estimates the chlorophyll a concentration online using locally collected data. The algal bloom front estimate is fed to the motion control algorithm. The performance of this method is evaluated through simulations using a real dataset of an algal bloom front in the Baltic. We consider a real-world scenario with sensor and localization noise and with a detailed AUV model.

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

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On the Approximation of the Quotient of Two Gaussian Densiti...

26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions