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Discrete-time inverse linear quadratic optimal control over fnite time-horizon under noisy output measurements

control Theory and Technology 2021年第4期19卷 563-572页

作者： Han Zhang Yibei Li Xiaoming Hu Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Mathematics KTH Royal Institute of Technology10044 StockholmSweden

In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is *** goal is to recover the corresponding quadratic objective function using noisy ***,the identifability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally ***,we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown,yet the exact observations on the initial states are *** formulate the problem as a risk minimization problem and approximate the problem using empirical *** is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative *** then study the case where the exact observations on the initial states are not available,yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian(with unknown mean and covariance).EM-algorithm is used to estimate the parameters in the objective *** efectiveness of our results are demonstrated by numerical examples.

关键词： Inverse optimal control Linear quadratic regulator Statistical consistency EM-algorithm

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A three-dimensional force estimation method for the cable-driven soft robot based on monocular images

arXiv

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arXiv 2024年

作者： Zhu, Xiaohan Bu, Ran Li, Zhen Xu, Fan Wang, Hesheng School of Electronic Information and Electrical Engineering The Department of Automation Shanghai Jiao Tong University China Artificial Intelligence Research Institute School of Information and Control Engineering China University of Mining and Technology China

Soft manipulators are known for their superiority in coping with high-safety-demanding interaction tasks, e.g., robot-assisted surgeries, elderly caring, etc. Yet the challenges residing in real-time contact feedback have hindered further applications in precise manipulation. This paper proposes an end-to-end network to estimate the 3D contact force of the soft robot, with the aim of enhancing its capabilities in interactive tasks. The presented method features directly utilizing monocular images fused with multidimensional actuation information as the network inputs. This approach simplifies the preprocessing of raw data compared to related studies that utilize 3D shape information for network inputs, consequently reducing configuration reconstruction errors. The unified feature representation module is devised to elevate low-dimensional features from the system’s actuation signals to the same level as image features, facilitating smoother integration of multimodal information. The proposed method has been experimentally validated in the soft robot testbed, achieving satisfying accuracy in 3D force estimation (with a mean relative error of 0.84% compared to the best-reported result of 2.2% in the related works). Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic surgery

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Consensus of Linear Multi-agent systems with Pure Relative Output Through Fully Distributed Reduced-Order Protocols

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IFAC-PapersOnLine 2023年第2期56卷 10162-10167页

作者： Yuwen Ma Xianwei Li Shaoyuan Li Department of Automation Shanghai JiaoTong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

This paper considers fully distributed adaptive control for linear multi-agent systems with pure relative output information only. Two reduced-order protocols, namely, an edge-based protocol and a node-based protocol, are derived. For the edge-based protocol, each edge is adapted by a coupling weight which depends only on relative output information of the associated two agents, while the coupling weight in the node-based protocol is based on the relative output information of all neighboring agents. Sufficient conditions for the solvability of the consensus problem under the two protocols are derived. Compared with most of the existing related protocols, the main merits of the protocols are that only relative output information is needed, which helps reduce the communication burdens and protect the multi-agent systems from network attacks, and that the protocols are fully distributed. A simulation example is finally presented to illustrate the effectiveness of the proposed protocols.

关键词： Multi-agent systems consensus pure relative output fully distributed design

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Derivative-Free Data-Driven control of Continuous-Time Linear Time-Invariant systems

arXiv

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arXiv 2024年

作者： Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Department of Electrical Electronic and Information Engineering University of Bologna Italy Institute for System Theory and Automatic Control University of Stuttgart Germany Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. © 2024, CC BY.

关键词： Continuous time systems

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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A Parkinson's Bradykinesia Recognition system Based on Deep Learning Method 12

A Parkinson's Bradykinesia Recognition System Based on Deep ...

引用

12th International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2022

作者： Tong, Lina Liu, Daisong Zhang, Mingjia Peng, Liang Department of State Key Laboratory of Management and Control for Complex Systems Institute of Automation China Academy of Science Beijing China School of Mechanical Electronic and Information Engineering Chinese University of Mining and Technology-Beijing Beijing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Parkinson's disease is a neurodegenerative disease common in middle-aged and elderly people, and bradykinesia is one of the most obvious symptoms. In this study, A deep learning-based Parkinson's bradykinesia recognition system is designed which has an inertial sensor-based data acquisition system. It can wirelessly collect bradykinesia kinematic information by wearing it on the wrist and ankle of Parkinson's patients. In addition, a method for identifying the symptom of Parkinson's bradykinesia based on Long Short Term Memory (LSTM) Network is proposed, with an accuracy of 98.6%. It provides a feasible method to identify Parkinson bradykinesia to assist the diagnosis and treatment. © 2022 IEEE.

关键词： Neurodegenerative diseases

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Distributed Fault-Tolerant Formation Game for Quadrotor UAVs With Hybrid Attacks

引用

IEEE Transactions on Aerospace and electronic systems 2025年

作者： Zhang, Rui-Ming Yao, Xiang-Yu Wang, Leimin Park, Ju H. Ge, Ming-Feng China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China China University of Geosciences School of Automation Wuhan430074 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of

This manuscript studies the distributed faulttolerant formation for quadrotor unmanned aerial vehicles (QUAVs) subject to actuator failures and hybrid cyber attacks via a game-theoretical approach. The desired formation, which aligns with optimal self-interests, is achieved by seeking the Nash equilibrium (NE) of the game under a strongly connected directed graph. First, a secure fault-tolerant NE seeking algorithm is presented to handle actuator failures and false data injection (FDI) attacks, with the false data being measured through extended state observers (ESOs). The proposed algorithm is established on pseudo-gradient dynamics, in which a local estimate law is used to anticipate the actions of other QUAVs. Then, an ESOsbased algorithm is developed to mitigate the influence of FDI and denial-of-service (DoS) attacks, guaranteeing that all QUAVs exponentially converge to an adjustable small domain of NE by Lyapunov stability analysis. This work is distinct from existing studies by addressing the practical scenario, where physical failures and hybrid cyber attacks occur simultaneously in the game framework of QUAVs. Eventually, numerical experiments are conducted to demonstrate the efficacy of theoretical findings. © 1965-2011 IEEE.

关键词： Drones

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Sliding Mode-based Two-Dimensional Iterative Learning control for systems with Uncertainties and External Disturbances

Sliding Mode-based Two-Dimensional Iterative Learning Contro...

引用

International Conference on control and automation (ICCA)

作者： Y. Yaru L. Dewei M. Aoyun Furong Gao Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai Department of Chemical and Biological Engineering Hong Kong University of Science and Technology HongKong

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper proposes a sliding mode-based two-dimensional (2D) iterative learning control (ILC) strategy for discrete- time batch process systems challenged by uncertainties and external disturbances. Considering the repetitive nature of batch process systems, the ILC method shows significant advantages. Given the presence of non-repetitive uncertainties and external disturbances in practical systems, this paper integrates the sliding mode control (SMC) scheme into the ILC design framework to ensure system robustness. A novel aspect of this study is the development of an augmented 2D model that captures the dynamic evolution in both batch and sampling time directions. Based on this 2D model, a corresponding 2D sliding mode surface and a 2D iterative learning sliding mode controller are designed, preserving the learning capabilities of ILC and the robustness of SMC against uncertainties and external disturbances. The method proposed in this paper enables the controlled system to maintain a high tracking capability in the face of non-repetitive uncertainties and external disturbances. Particularly, the stability conditions derived from this 2D model provide a broad feasible solution space, which improves the applicability. Furthermore, the correctness of the proposed method is proven and its effectiveness is validated through a simulation example.

关键词： Uncertainty system dynamics Two-dimensional displays Simulation Batch production systems Robustness Stability analysis

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SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Tasks

SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Task...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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