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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Han Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai China

ISBN: (纸本)9798350316339

We develop a safe, efficient, and flexible motion control framework for collision-free navigation of robots in uncertain and dynamic environments. With the gathered real-time data at each control time, the motion distribution of obstacles is estimated from data streams by leveraging the Dirichlet process mixture model. We propose a novel uncertainty propagation method, which theoretically admits the derivation of obstacle position distributions over the entire prediction horizon only based on the current position and motion distribution of obstacles. To enhance the robustness against estimated distribution errors, an ambiguity set utilizing local-moment information of position distributions is constructed. Additionally, we introduce a compression scheme to automatically adjust the complexity (granularity) of the ambiguity set by aggregating distributions that are close in Wasserstein space, thereby flexibly striking a trade-off between control performance and computation time. The distributionally robust collision avoidance constraint based on the compressed ambiguity set is reformulated into separating hyperplanes, which are applied in the model predictive controller to obtain the optimal collision-free trajectory. To resolve the infinite-dimensionality issue inherent in the computation of the separating hyperplanes, we equivalently derive a tractable semi-definite programming formulation exploiting distributionally robust optimization techniques. The efficacy of the proposed framework is demonstrated through simulation. © 2024 IEEE.

关键词： Predictive control systems

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Multi-camera Registration with Small FOV Based on Pose Graph Optimization 1

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13th International Conference on Green Intelligent Transportation systems and Safety, GITSS 2022

作者： Chen, Long He, Xing He, Yuesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9789819730520

ISBN: (纸本)9789819730513

Multiple roadside cameras sense and localize autonomous vehicles for Automated Valet Parking. The accurate multi-camera registration into a shared coordinate system is of great importance. Owing to the sparse and distributed placement of multiple cameras, there is a very small field of view (FOV) between the cameras. The multi-camera registration is complex and dependent on human expertise. Therefore, a multi-camera registration method with small FOV based on pose graph optimization is proposed in this paper to automatically generate the extrinsic. The moving checkerboard in the public FOV is utilized as a reference for calibration. The optimization method constructs a pose graph to store camera and checkerboard pose and solves the pose graph by calculating checkerboard reprojection errors. To increase the overall flatness of the ground, ground plane assumptions can be utilized to further optimize the parameters. The experimental results show that the proposed method has greater calibration accuracy on the constructed multiple RGB-D camera system, as well as being simple and efficient in the real calibration procedure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automated valet parking Automatic calibration Multi-camera registration Pose graph optimization Small FOV

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Research on High-Precision Contouring control of Gantry Dual-Drive H-Type Planar Motion Platform

Research on High-Precision Contouring Control of Gantry Dual...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Chen, Chao Li, Haoyu Gu, Chaochen Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai China

ISBN: (纸本)9798350372601

The H-type two-axis linear plane motion platform with dual gantry drive is one of the most crucial components in contour processing equipment. The dual gantry structure employs independently controlled dual-side motors to collectively drive the motion of the gantry crossbeam axis. While the introduction of dual gantry drive brings higher single-axis positioning accuracy and greater operational speed to contour processing *** these issues, this paper starts with the dynamic modeling of the dual gantry H-type plane motion platform. It modifies and extends the task coordinates within the framework of the Global Task Coordinate Frame (GTCF). Subsequently, it effectively addresses the coordination between axes and the synchronization of dual drive in the task space, obtaining both motion coordination and dual-axis synchronization capabilities, leading to more accurate contour error calculation and evaluation. The paper includes a comprehensive design of experiments to assess the dual gantry synchronization performance in contour control. The proposed control method is compared with the traditional GTCF-based contour control method, demonstrating the effectiveness and superiority of the integrated coordination and synchronization contour control method. © 2024 IEEE.

关键词： Contour followers

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Hierarchical Data-driven Scheduling-control Framework for Energy-efficient Industrial Flexible Production 24

Hierarchical Data-driven Scheduling-Control Framework for En...

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2nd International Conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Jiang, Yuliang Zhu, Shanying Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400710681

This paper proposes a hierarchical data-driven scheduling-control framework for a multi-machine/mode production system to operate in an energy-efficient manner. At the scheduling layer, the scheduling problem incorporating process dynamics is formulated into a quadratic programming to arrange candidate processing machines/modes and corresponding production objectives. Based on the dynamic linearization model, scheduling results are executed by model-free adaptive control at the control layer. The real-time process output is feedback to the scheduling layer for the next decision-making. The practicality of the proposed framework is validated in a thread-tapping process featuring four distinct operational modes. On average, operating in a single mode maintains output accuracy while saving 14.2% on energy. The multi-mode operation further optimizes energy usage through its capability for dynamic mode switching. © 2024 Copyright held by the owner/author(s).

关键词： Quadratic programming

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Robust H∞ Analysis and control of Fractional-Order systems With Ellipsoidal Parametric Uncertainty

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Mathematical Methods in the Applied Sciences 2025年第8期48卷 9058-9070页

作者： Zhu, Long-Jun Lu, Jun-Guo Zhu, Zhen Zhang, Qing-Hao Department of Automation Shanghai Jiao Tong University Shanghai China School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

In the domain of systems control, the robust control of fractional-order systems (FOSs) with ellipsoidal parameter uncertainty has received scant attention, even though ellipsoidal uncertainty is significant for quantifying and modeling parameter uncertainties. This paper addresses the gap by focusing on (Formula presented.) analysis and control of FOSs with ellipsoidal parametric uncertainty, where the fractional order is delimited to the range (Formula presented.). Two distinct categories of ellipsoidal parameter uncertainty models are considered: one corresponding to FOSs with uncertain zero positions and the other to FOSs with uncertain pole and zero positions. First, sufficient conditions for the (Formula presented.) performance analysis of FOSs with ellipsoidal parametric uncertainty are established by extending the (Formula presented.) bounded real lemma for FOSs. Next, the problems of (Formula presented.) controller synthesis are analyzed, and the state feedback (Formula presented.) controllers are designed, along with the establishment of sufficient conditions for stabilization. These conditions are given in the form of linear matrix inequalities (LMIs). Lastly, several numerical examples are provided to verify the effectiveness of the proposed results. © 2025 John Wiley & Sons Ltd.

关键词： State feedback

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A joint sparse Bayesian learning for wideband DOA estimation

A joint sparse Bayesian learning for wideband DOA estimation

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2024 Conference on Spectral Technology and Applications, CSTA 2024

作者： Zhao, Yonghong Liu, Jisong Wang, Junlong Dong, Shuxin Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Department of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510683082

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method. © 2024 SPIE.

关键词： Direction of arrival

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A Timeslot Clustering-Based Hybrid Traffic Scheduling in Time-Sensitive Networking 22

A Timeslot Clustering-Based Hybrid Traffic Scheduling in Tim...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yang, Zhihao Wang, Shouliang Xu, Qimin Li, Xin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9798331527471

The development of Industrial Internet of Things necessitates deterministic transmission of hybrid traffic with varying real-time requirements. Time-Sensitive Networking offers schemes combining scheduling mechanisms like the Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). Current methods utilize the combination of TAS&CQF to schedule Time-Triggered (TT) and Audio-Video Bridging (AVB) traffic under corresponding mechanisms, respectively. However, the prior TT traffic orchestration restricts the scheduling solution space of AVB traffic, decreasing the effectiveness of combination and finally resulting in low schedulability. In this paper, we focus on enhancing the schedulability of TT&AVB scheduling by integrating TAS and Cyclic Specified Queuing and Forwarding (CSQF), which brings larger scheduling solution space to AVB traffic. The integration of TAS&CSQF is proposed by analyzing the impact of TAS on CSQF to expand the allocatable resource for AVB traffic. Then TAS&CSQF scheduling model is formulated to ensure deterministic requirements. Within this model, Timeslot Clustering Optimization (TCO) is proposed to optimize the scheduling solution space of AVB traffic and slice timeslot by refining the scheduling of TAS. Based on TCO, Flow Congestion Metric (FCM) is designed to improve schedulability by the congestion level of each link. An FCM-based algorithm is further presented to generate TT&AVB solutions incrementally. Simulation results demonstrate that our method reduces scheduling time cost by over 168 times and improves schedulability by 32% for 3000 flows compared to existing methods. © 2024 IEEE.

关键词： Traffic congestion

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OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical systems 63

OIDM: An Observability-based Intelligent Distributed Edge Se...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350316339

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. © 2024 IEEE.

关键词： Stochastic systems

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Robust Stability of Linear Time-Invariant systems with Hybrid Uncertainties

Robust Stability of Linear Time-Invariant Systems with Hybri...

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2023 International Conference on automation, control and Electronics Engineering, CACEE 2023

作者： Li, Yangxuan Lu, Junguo Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Department of Automation Shanghai China

ISBN: (纸本)9798350309201

This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and sufficient conditions for robust stability are derived. Based on the stability condition, the stability margin of the uncertain system is obtained from the skewed structured singular value. Additionally, numerical simulation results are provided to validate the effectiveness of the proposed methods. © 2023 IEEE.

关键词： Linear time-invariant system

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Preserving Topology of Network systems: Metric, Analysis, and Optimal Design

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IEEE Transactions on Automatic control 2024年第6期70卷 3540-3555页

作者： Li, Yushan Wang, Zitong He, Jianping Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings. © 1963-2012 IEEE.

关键词： Network topology

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