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Confidence Regions for Predictions of Online Learning-Based control

IFAC-PapersOnLine 2020年第2期53卷 1007-1012页

作者： Alexandre Capone Armin Lederer Sandra Hirche Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich Germany

Although machine learning techniques are increasingly employed in control tasks, few methods exist to predict the behavior of closed-loop learning-based systems. In this paper, we introduce a method for computing confidence regions of closed-loop system trajectories under an online learning-based control law. We employ a sampling-based approximation and exploit system properties to prove that the computed confidence regions are correct with high probability. In a numerical simulation, we show that the proposed approach accurately predicts correct confidence regions.

关键词： Gaussian processes system identification nonlinear systems stochastic systems Monte Carlo simulation error estimation

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Day-ahead Scheduling of Thermal Storage Systems Using Bayesian Neural Networks

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IFAC-PapersOnLine 2020年第2期53卷 13281-13286页

作者： Alexandre Capone Conrad Helminger Sandra Hirche Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of MunichGermany

The increased need for energy efficiency in buildings requires sophisticated scheduling strategies. A considerable challenge when developing such strategies is to address the stochasticity of demand appropriately. In this paper, we propose a day-ahead scheduling technique, which aims to minimize electricity costs, as well as power grid congestion. Our method considers energy storage systems with heat pumps and backup resistance heaters under uncertain heat demand. We employ a Bayesian neural network to model the stochastic consumer demand, which takes historical measurements as training inputs, and is able to model complex stochastic patterns. The model is then employed to generate sample demands, which are used to approximate the expected costs. The minimization of the resulting cost function corresponds to a stochastic optimal control problem with quadratic costs and mixed integer constraints. In a numerical simulation of a single-family building, the proposed approach is shown to perform better than a standard neural network-based scheduling scheme.

关键词： Load forecasting optimal control stochastic modelling machine learning district heating Non-Gaussian processes

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GP3: A Sampling-based Analysis Framework for Gaussian Processes

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IFAC-PapersOnLine 2020年第2期53卷 983-988页

作者： Armin Lederer Markus Kessler Sandra Hirche Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich Germany

Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approaches typically rely on well-understood learning algorithms, which allow formal theoretical analysis. Gaussian process regression is a prominent example among those methods, which attracts growing attention due to its strong Bayesian foundations. Even though many problems regarding the analysis of Gaussian processes have a similar structure, specific approaches are typically tailored for them individually, without strong focus on computational efficiency. Thereby, the practical applicability and performance of these approaches is limited. In order to overcome this issue, we propose a novel framework called GP3, general purpose computation on graphics processing units for Gaussian processes, which allows to solve many of the existing problems efficiently. By employing interval analysis, local Lipschitz constants are computed in order to extend properties verified on a grid to continuous state spaces. Since the computation is completely parallelizable, the computational benefits of GPU processing are exploited in combination with multi-resolution sampling in order to allow high resolution analysis.

关键词： Machine learning Learning for control Bayesian methods Gaussian processes Stability of nonlinear systems Sampling-based analysis Learning systems

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Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression

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IFAC-PapersOnLine 2020年第2期53卷 1194-1199页

作者： Wenxin Xiao Armin Lederer Sandra Hirche Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich Germany

Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems. Due to the high nonlinearity of problems in this area, data-driven approaches gain increasing attention for identifying nonparametric models. In order to increase the prediction performance of these models, abstract prior knowledge such as stability should be included in the learning approach. One of the key challenges is to ensure sufficient flexibility of the models, which is typically limited by the usage of parametric Lyapunov functions to guarantee stability. Therefore, we derive an approach to learn a nonparametric Lyapunov function based on Gaussian process regression from data. Furthermore, we learn a nonparametric Gaussian process state space model from the data and show that it is capable of reproducing observed data exactly. We prove that stabilization of the nominal model based on the nonparametric control Lyapunov function does not modify the behavior of the nominal model at training samples. The flexibility and efficiency of our approach is demonstrated on the benchmark problem of learning handwriting motions from a real world dataset, where our approach achieves almost exact reproduction of the training data.

关键词： Nonparametric methods Machine learning Nonlinear system identification Learning systems Lyapunov methods Human centered automation Gaussian processes

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Data-driven output synchronization of heterogeneous leader-follower multi-agent systems

arXiv

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arXiv 2021年

作者： Jiao, Junjie van Waarde, Henk J. Trentelman, Harry L. Camlibel, M. Kanat Hirche, Sandra The Chair of Information-oriented Control Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany The Control Group Department of Engineering University of Cambridge Cambridge United Kingdom The Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands

This paper deals with data-driven output synchronization for heterogeneous leader-follower linear multiagent systems. Given a multi-agent system that consists of one autonomous leader and a number of heterogeneous followers with external disturbances, we provide necessary and sufficient data-based conditions for output synchronization. We also provide a design method for obtaining such output synchronizing protocols directly from data. The results are then extended to the special case that the followers are disturbance-free. Finally, a simulation example is provided to illustrate our results. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Lifecycle traceability towards sustainable and circular value chains: analysis framework and state of the art in the fashion industry

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IFAC-PapersOnLine 2022年第10期55卷 1705-1710页

作者： Riemens Joséphine Lemieux Andrée-Anne Lamouri Samir IFM-KERING Sustainability Chair Institut Français de la Mode 34 Quai d'Austerlitz 75013 Paris Laboratory of Industrial and Human Automation control Mechanical engineering and Computer Science (LAMIH – UMR CNRS 8201) Arts et Métiers Institute of Technology 151 Boulevard de l'Hôpital 75013 Paris

Tremendous environmental and social challenges have been raising traceability as a critical priority in the fashion industry. However, this traditional quality concept inherited from the food sector is prompting major confusions when it comes to sustainability and circularity transition, hindering its implementation. Based on three interrelated dimensions ( Objectives, Scope, and Action levers ) the paper proposes a novel analysis framework to conduct a literature review on lifecycle traceability towards sustainable and circular value chains in the fashion industry. Given the early stage of the topic, the paper opens significant research avenues as well as potential support to practitioners to build-up appropriate traceability action plans.

关键词： traceability transparency sustainability lifecycle supply chain fashion apparel textile

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Learning-based control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

Learning-based Control for PMSM Using Distributed Gaussian P...

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Annual Conference of Industrial Electronics Society

作者： Zhenxiao Yin Xiaobing Dai Zewen Yang Yang Shen Georges Hattab Hang Zhao Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Chair of Information-oriented Control Technical University of Munich Munich Germany Center for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong China

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

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Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime

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Scientific reports 2025年第1期15卷 19140页

作者： Cedric Donié Neha Das Satoshi Endo Sandra Hirche TUM School of Computation Information and Technology Department of Computer Engineering Chair of Information-oriented Control Technical University of Munich Munich Germany. cedric.donie@tum.de. TUM School of Computation Information and Technology Department of Computer Engineering Chair of Information-oriented Control Technical University of Munich Munich Germany. Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany. Munich Data Science Institute (MDSI) Munich Germany.

Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.

关键词： Accelerometer Deep learning IMU InceptionTime Machine learning Parkinson’s disease ROCKET Time series classification

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Transmission power control for remote state estimation in industrial wireless sensor networks

arXiv

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arXiv 2019年

作者： Zoppi, Samuele Soleymani, Touraj Klugel, Markus Vilgelm, Mikhail Hirche, Sandra Kellerer, Wolfgang Chair of Communication Networks Department of Electrical and Computer Engineering TUM Germany Chair of Information-Oriented Control Department of Electrical and Computer Engineering TUM Germany

Novel low-power wireless technologies and IoT applications open the door to the Industrial Internet of Things (IIoT). In this new paradigm, Wireless Sensor Networks (WSNs) must fulfil, despite energy and transmission power limitations, the challenging communication requirements of advanced manufacturing processes and technologies. In industrial networks, this is possible thanks to the availability of network infrastructure and the presence of a network coordinator that efficiently allocates the available radio resources. In this work, we consider a WSN that simultaneously transmits measurements of Networked control Systems' (NCSs) dynamics to remote state estimators over a shared packet-erasure channel. We develop a minimum transmission power control (TPC) policy for the coordination of the wireless medium by formulating an infinite horizon Markov decision process (MDP) optimization problem. We compute the policy using an approximate value iteration algorithm and provide an extensive evaluation of its parameters in different interference scenarios and NCSs dynamics. The evaluation results present a comprehensive characterization of the algorithm's performance, proving that it can flexibly adapt to arbitrary use cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

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Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization

Closed-loop Model Selection for Kernel-based Models using Ba...

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IEEE Annual Conference on Decision and control

作者： Thomas Beckers Somil Bansal Claire J. Tomlin Sandra Hirche Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Sciences UC Berkeley USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Kernel-based nonparametric models have become very attractive for model-based control approaches for nonlinear systems. However, the selection of the kernel and its hyperparameters strongly influences the quality of the learned model. Classically, these hyperparameters are optimized to minimize the prediction error of the model but this process totally neglects its later usage in the control loop. In this work, we present a framework to optimize the kernel and hyperparameters of a kernel-based model directly with respect to the closed-loop performance of the model. Our framework uses Bayesian optimization to iteratively refine the kernel-based model using the observed performance on the actual system until a desired performance is achieved. We demonstrate the proposed approach in a simulation and on a 3-DoF robotic arm.

关键词： Kernel Optimization Predictive models Data models Bayes methods Linear programming Mathematical model

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