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Hidden Markov Models:Inverse Filtering,Belief Estimation and Privacy Protection

Journal of systems Science & Complexity 2021年第5期34卷 1801-1820页

作者： LOURENCO Ines MATTILA Robert ROJAS Cristian R. HU Xiaoming WAHLBERG Bo Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Division of Optimizations and Systems Theory School of Engineering SciencesKTH Royal Institute of TechnologyStockholmSweden

A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy *** paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and privacy enforcement in such a ***,the authors discuss how HMM parameters and sensor measurements can be reconstructed from posterior distributions of an HMM ***,the authors consider a rational decision-maker that forms a private belief(posterior distribution)on the state of the world by filtering private *** authors show how to estimate such posterior distributions from observed optimal actions taken by the *** the setting of adversarial systems,the authors finally show how the decision-maker can protect its private belief by confusing the adversary using slightly sub-optimal *** range from financial portfolio investments to life science decision systems.

关键词： Belief estimation counter-adversarial systems hidden Markov models inverse decision making inverse filtering

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Design and Stability Analysis of control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS) 24

Design and Stability Analysis of Control System in Multiport...

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24th IEEE Workshop on control and Modeling for Power Electronics, COMPEL 2023

作者： Xia, Qianxue Debnath, Suman Marthi, Phani R.V. Saeedifard, Maryam Energy Systems Integration Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798350316186

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system. © 2023 IEEE.

关键词： Energy storage

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Comparing Human Motion Forecasts in Moving Horizon Trajectory Planning of Collaborative Robots

Comparing Human Motion Forecasts in Moving Horizon Trajector...

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IEEE International Conference on Robotics and Biomimetics

作者： Heiko Renz Maximilian Krämer Torsten Bertram Institute of Control Theory and Systems Engineering TU Dortmund University Dortmund Germany

Shared workspaces between humans and robots are a significant part of Industry 4.0. Modern planning algorithms aim to increase human safety in collaborative scenarios and dynamic environments by considering motion forecasts of obstacles. While highly accurate approaches exist, the question arises whether their often greater complexity justifies the benefits that motion planners can draw from them. The paper at hand compares two motion forecasters and evaluates their effectiveness in a robot motion planner to generate collision-free trajectories online. One motion forecaster uses neural networks for predicting, while the other estimates and extrapolates the state of a customized human skeleton. The evaluation shows that simple and online capable motion extrapolations constitute towards human safety in varying situations more than highly accurate neural network predictions.

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Consensus of Uncertain Euler-Lagrange Multi-agent systems under Switching Digraphs and Disturbances 8

Consensus of Uncertain Euler-Lagrange Multi-agent Systems un...

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8th International Conference on Automation, control and Robotics engineering, CACRE 2023

作者： Ju, Lei Wang, Zhiwei Wang, Anqing Zhou, Xiaotong Dalian Maritime University College of Marine Electrical Engineering Dalian China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin China

ISBN: (纸本)9798350302776

This paper focuses on exploring the problem of achieving leader-follower consensus in uncertain Euler-Lagrange multi-agent systems, which are subjected to disturbance, and operate on switched digraph. To be more precise, the system dynamics are characterized by uncertainties that can be linearly parameterized, and the disturbances that are considered are those originating from the leader system. A novel adaptive distributed control strategy is proposed to overcome the difficulties of suppressing disturbances and achieving consistency under the condition of joint connectivity of switched digraphs. By using Lyapunov stability theory and the generalized Barbalat's lemma, it is proved that the uncertain Euler-Lagrange multi-agent systems achieves state consensus asymptotically with the proposed distributed adaptive control protocol. The efficacy of the adaptive distributed control strategy proposed in the paper is validated by presenting a case study involving a system composed of four double-link manipulators. © 2023 IEEE.

关键词： Multi agent systems

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Uncertainty Estimation for Predictive Collision Avoidance in Human-Robot Collaboration

Uncertainty Estimation for Predictive Collision Avoidance in...

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IEEE International Conference on Robotics and Biomimetics

作者： Heiko Renz Maximilian Krämer Torsten Bertram Institute of Control Theory and Systems Engineering TU Dortmund University Dortmund Germany

Predictive motion planning enables robots to consider workspace obstacles and proactively avoid them. Estimating future poses of various obstacles is complex and exhibits results with uncertainties. Especially in the case of human-robot collaboration, the forecast process has to deal with sudden motion changes and varying human intentions. This paper proposes an uncertainty estimation for human motion extrapolations in the joint space of a human skeleton. The approach utilizes Gaussian Mixture Models that allow an online determination of the accuracy of current extrapolations based on preceding extrapolation errors. Multiple human model representations enable consideration of estimated uncertainties and are presented along with evaluating the resulting robot trajectories. The results show that considering estimated uncertainties leads to a robot trajectory with increased distances between humans and robots and decreased danger index from the literature, which is desirable for collaborative scenarios.

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Tracking control in Presence of Obstacles and Uncertainties for Bioinspired Spherical Underwater Robots

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Journal of Bionic engineering 2023年第1期20卷 323-337页

作者： Chunying Li Shuxiang Guo Jian Guo Graduate School of Engineering Kagawa UniversityTakamatsuKagawa761-0396Japan Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Information TechnologySchool of Life Science and TechnologyBeijing Institute of TechnologyBeijing100081China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of TechnologyBinshui Xidao Extension 391Tianjin300384China

During marine missions,AUVs are susceptible to external disturbances,such as obstacles,ocean currents,etc.,which can easily cause mission failure or *** this paper,considering the strong nonlinearities,external disturbances and obstacles,the kinematic and dynamic model of bioinspired Spherical Underwater Robot(SUR)was ***,the waypoints-based trajectory tracking with obstacles and uncertainties was proposed for SUR to guarantee its safety and ***,the Lyapunov theory was adopted to verify the stability and the Slide Mode control(SMC)method is used to verify the robustness of the control *** addition,a series of simulations were conducted to evaluate the effectiveness of proposed control *** tests,including path-following,static and moving obstacle avoidance were performed which verified the feasibility,robustness and effectiveness of the designed control ***,a series of experiments in real environment were performed to verify the performance of the control *** simulation and experimental results of the study supplied clues to the improvement of the path following capability and multi-obstacle avoidance of AUVs.

关键词： Path following Obstacle avoidance Bioinspired.Spherical underwater robot(SUR) Lyapunov theory

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Analysis of Depth Estimation and Semantic Segmentation Algorithms for the Environment Perception of Automated Vehicles 1

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6. Internationalen ATZ-Fachtagung Automatisiertes Fahren, 2020

作者： Schmidt, Manuel Stannartz, Niklas Bertram, Torsten TU Dortmund Institute of Control Theory and Systems Engineering Germany

ISBN: (数字)9783658347529

ISBN: (纸本)9783658347512

This paper evaluates different deep learning based depth estimation algorithms. We propose improvements for a state-of-the-art unguided depth completion method where the number of necessary parameters can be more than halved at unvarying accuracy. Based on the results of the depth estimation evaluation, we consider the performance of semantic segmentation methods. We investigate if the completion improves the accuracy of point cloud based segmentation. The results are compared to the segmentation accuracy using only measured sensor data. Moreover, we give a comparison to the segmentation based solely on predicted depths of a monocular camera. Here, we depict the differences in accuracy when a costly lidar sensor is economized. The results are further validated on a self-provided dataset recorded with the institute’s own test vehicle. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2021.

关键词： Semantic Segmentation

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Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots

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Science China(Information Sciences) 2024年第5期67卷 197-214页

作者： Yefeng YANG Tao HUANG Tianqi WANG Wenyu YANG Han CHEN Boyang LI Chih-yung WEN Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Center for Control Theory and Guidance Technology Harbin Institute of Technology School of Engineering The University of Newcastle Research Center for Unmanned Autonomous Systems The Hong Kong Polytechnic University

Autonomous robots have garnered extensive utilization in diverse fields. Among the critical concerns for autonomous systems, path planning holds paramount importance. Notwithstanding considerable efforts in its development over the years, path planning for autonomous systems continues to grapple with challenges related to low planning efficiency and inadequate obstacle avoidance response in a timely *** study proposes a novel and systematic solution to the path planning problem within intricate office buildings. The solution consists of a global planner and a local planner. To handle the global planning aspect, an adaptive clustering-based dynamic programming rapidly exploring random tree(ACDP-RRT) algorithm is proposed. ACDP-RRT effectively identifies obstacles on the map by leveraging geometric *** obstacles are then represented as a collection of sequentially arranged convex polygons, optimizing the sampling region and significantly enhancing sampling efficiency. For local planning, a network decoupling actor-critic(ND-AC) algorithm is employed. The proposed ND-AC simplifies the local planner design process by integrating planning and control loops into a neural network(NN) trained via an end-to-end model-free deep reinforcement learning(DRL) framework. Moreover, the adoption of network decoupling(ND) techniques leads to an improved obstacle avoidance success rate when compared to conventional actor-critic(AC)-based methods. Extensive simulations and experiments are conducted to demonstrate the effectiveness and robustness of the proposed approach.

关键词： rapidly exploring random tree (RRT) adaptive clustering network decoupling actor critic (AC) path planning

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Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency

arXiv

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arXiv 2024年

作者： González, Rodrigo A. van Haren, Max Oomen, Tom Rojas, Cristian R. The Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect signal reconstructability, careful consideration is required when identifying parametric and nonparametric models. In this letter, a comprehensive statistical analysis of estimators under slow sampling is performed. Necessary and sufficient conditions are obtained for unbiased estimates of the frequency response function beyond the Nyquist frequency, and it is shown that consistency of parametric estimators can be achieved even if input frequencies overlap after aliasing. Monte Carlo simulations confirm the theoretical properties. © 2024, CC BY-NC-ND.

关键词： Continuous time systems

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A Linear Parameter-Varying Approach to Data Predictive control

arXiv

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arXiv 2023年

作者： Verhoek, Chris Berberich, Julian Haesaert, Sofie Tóth, Roland Abbas, Hossam S. The Control Systems Group Eindhoven University of Technology Netherlands The University of Stuttgart Institute for Systems Theory and Automatic Control Germany The Institute for Electrical Engineering in Medicine Universität zu Lübeck Germany The Systems and Control Lab Institute for Computer Science and Control Hungary

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system. © 2023, CC BY-NC-SA.

关键词： Predictive control systems

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