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Higher-order properties and extensions for indirect MRAC and APPC of linear systems

Science China(Information Sciences) 2024年第4期67卷 218-238页

作者： Yanjun ZHANG Zhipeng ZHANG Jian SUN Lei WANG Kangkang ZHANG National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center College of Control Science and Engineering Zhejiang University Department of Electrical and Electronic Engineering Imperial College London KIOS Research and Innovation Center of Excellence University of Cyprus

Recently, a reference derived some new higher-order output tracking properties for direct model reference adaptive control（MRAC） of linear time-invariant（LTI） systems: limt→∞ e（i）（t） = 0, i = 1,..., n*-1, where n*and e（i）（t） denote the relative degree of the system and the i-th derivative of the output tracking error, respectively. However, a naturally arising question involves whether indirect adaptive control（including indirect MRAC and indirect adaptive pole placement control） of LTI systems still has higher-order tracking properties. Such properties have not been reported in the literature. Therefore, this paper provides an affirmative answer to this question. Such higher-order tracking properties are new discoveries since they hold without any additional design conditions and, in particular, without the persistent excitation condition. Given the higher-order properties, a new adaptive control system is developed with stronger tracking features.（1） It can track a reference signal with any order derivatives being unknown.（2） It has higher-order exponential or practical output tracking properties.（3） Finally, it is different from the usual MRAC system, whose reference signal's derivatives up to the n*order are assumed to be known. Finally, two simulation examples are provided to verify the theoretical results obtained in this paper.

关键词： higher-order tracking indirect adaptive control model reference adaptive control adaptive pole placement control

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Cooperative Constrained Enclosing control of Multirobot systems in Obstacle Environments

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IEEE TRANSACTIONS ON control OF NETWORK systems 2024年第2期11卷 718-730页

作者： Lu, Ke Dai, Shi-Lu Jin, Xu South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Guangdong Peoples R China Univ Kentucky Dept Mech & Aerosp Engn Lexington KY 40506 USA

This article develops a novel cooperative constrained control framework to solve the moving-target enclosing problem for multirobot systems, where safe navigation and formation precision are concerned in obstacle environments. When moving obstacles block the encircling motion, the enclosing formation contracts toward the target with a precise spacing pattern. Without a priori knowledge of environmental information, the mobile robot group can track and enclose a moving target while guaranteeing limited sensing requirements and collision avoidance with the target, neighboring robots, and obstacles. Under the precision constraint, with an encircling speed assigned to only one robot, the mobile robots are driven by their neighbors until all robots enclose the target as a whole. With the help of velocity observers, the robot group can converge to a small neighborhood of the desired enclosing formation without knowing the target's velocity. Simulation results demonstrate the effectiveness of the proposed constrained control strategy.

关键词： Collision avoidance Robot sensing systems Robots Safety Robot kinematics Sensors Network systems Barrier function collision avoidance limited sensing obstacle avoidance target enclosing

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Sampled-Data Semi-Global Practical Tracking for Uncertain Euler-Lagrange systems

Sampled-Data Semi-Global Practical Tracking for Uncertain Eu...

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2022 Chinese Automation Congress, CAC 2022

作者： Zeng, Kaiji Liu, Wei South China University of Technology Ministry of Education School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked Control Guangzhou510641 China

ISBN: (纸本)9781665465335

We investigate a sampled-data tracking control for uncertain Euler-Lagrange systems in this paper. What makes our result interesting is that our system and exosystem encompass some uncertain parameters that belong to some compact domain that can be specified arbitrarily large. To handle the uncertainties in both the Euler-Lagrange systems and the exosystem, we need to resort to not only the adaptive control technique but also the internal model method. By proposing a sampled-data control law, we solve the problem with guaranteed tracking performance, that is, the tracking errors can converge exponentially, by any fast specified rate, to any small specified neighborhood of the origin. © 2022 IEEE.

关键词： Lagrange multipliers

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Uncertainty and Reliability Analysis of Neural Networks Based Soft-sensors with Applications in Wastewater Treatment

Uncertainty and Reliability Analysis of Neural Networks Base...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Yiqi Yuan, Jingyi Huang, Daoping Key Laboratory of Autonomous Systems and Networked Control Ministry of Education The School of Automation Science & Engineering South China University of Technology Guangzhou510640 China

ISBN: (纸本)9781665465335

To overcome the transparency and interpretability issues of neural network-based soft sensing, a data-driven framework by combining neural network prediction models and sensitivity analysis methods is constructed in this paper, with application in wastewater treatment plant analysis. The results show that readily biodegradable substrate effluent (SS-e) and NH4+ + NH3 nitrogen effluent (SNH-e) are both important input factors affecting the difficult-to-measured effluent quality, chemical oxygen demand (COD), by resorting to PAWN sensitivity analysis and four neural network models. In addition, four neural network models worked together with bilateral sensitivity analysis for the input perturbation analysis, which shows that the addition of Gaussian perturbation to SS-e and flow rate influent (Q-in) has a greater effect on the prediction which results large fluctuations of output results. Finally, the effectiveness of this framework is confirmed based on above results. © 2022 IEEE.

关键词： Sensitivity analysis

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Transforming data into actionable knowledge for fault detection, diagnosis and prognosis in urban wastewater systems with AI techniques: A mini-review

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 172卷 501-512页

作者： Liu, Yiqi Ramin, Pedram Flores-Alsina, Xavier Gernaey, Krist., V South China Univ Technol Minist Educ Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Guangdong Peoples R China Tech Univ Denmark Proc & Syst Engn Ctr PROSYS Dept Chem & Biochem Engn Bldg 228 A DK-2800 Lyngby Denmark

Recent advances in artificial intelligence (AI) and data analytics (DA) could provide opportunities for the fault management and the decision-making of the urban wastewater treatment systems (UWS) operations. The UWS is typically a large system, including Sewer networks (SNs), Wastewater Treatment plants (WWTPs) and also considering the Receiving media (RM). However, applications of AI and DA in the UWS can be challenging due to the complexities and size of systems, the large variation in the level of UWS instrumentation, and the relatively poor data quality. This review goes beyond the state of the art by critically analyzing previous work on AI-based data-driven methodologies to system-wide fault detection, life cycle fault management and transformation of big and small data into analytics, particularly, considering two different points of view: process faults (such as bulking sludge, sewer corrosion & technology specifics) and instrumentation faults (such as sensors and actua-tors), thereby offering more opportunities to distinguish complex patterns and dynamics. Our analysis reveals the relative strengths and weaknesses of the different approaches to design fault diagnosis tools and to apply these in the UWS. Finally, the opportunities and challenges about the inter-play among UWS, data and AI are discussed.

关键词： Fault detection Fault diagnosis Fault prognosis Data analytics Artificial intelligence

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Research on automatic switching of hydraulic feed dual process in honing processing 4

Research on automatic switching of hydraulic feed dual proce...

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2024 4th International Conference on Mechatronics, Automation and Intelligent control, MAIC 2024

作者： Lu, Yong Pei, Hailong Li, Guang Deng, Haijun Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems School of Automation Science and Engineering South China University of Technology Guangzhou510640 China School of Information Guangdong Polytechnic of Science and Trade Guangzhou510440 China

Honing is a finishing process used for the inner cylindrical surfaces of engine blocks or liners. The process involves abrasive particles bonded to the surface of a honing head, which come into contact with the inner bore surface, performing an oscillating and rotational motion. These motions follow a specific pattern, allowing for the creation of crosshatch patterns on the inner bore surface, thereby improving dimensional accuracy, form accuracy and reducing surface roughness. Common honing processes are divided into four distinct phases: material removal, crosshatching, finish honing, and plateau honing. Each phase corresponds to specific tasks such as material removal from the bore, creating crosshatch patterns, honing the peaks in the crosshatch, and final surface finishing. In computer numerical control (CNC)-based honing machine, individual control commands are typically used to manage each of these phases separately. The commands are combined according to process requirements to achieve the desired honing results. To enhance the efficiency of the honing process, this study focuses on the hydraulic feed of the abrasive strips on the honing head, specifically examining the automatic continuous switching between finish honing and plateau honing. The research analyzes the honing principles, develops a control algorithm for dual-process automatic switching, and validates the process through machining tests. Compared to single-process control, the proposed algorithm significantly improves processing efficiency. Additionally, this algorithm can be applied to other dual-process automatic switching controls, providing valuable insights for the design of CNC honing systems. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Honing machines

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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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Swift and Accurate Point Cloud Registration Using SGUformer

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Luo, Jiaxiang Dong, Duoqin Liu, Haiming South China Univ Technol Engn Res Ctr Minist Educ Precis Mfg Equipment Sch Automat Sci & Engn Guangzhou 510641 Peoples R China South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

Point cloud registration aims to find a rigid transformation that aligns two point clouds. Applications such as augmented reality (AR) and robot navigation often require real-time performance for point cloud registration algorithms. In this article, we propose SGUformer, a novel point cloud registration method that achieves fast alignment by redesigning the feature extraction pipeline and employing a lightweight global feature extraction framework. The gating mechanism is utilized, and local coordinates are embedded to enhance the representation of point-level features. To facilitate the extraction of global features, a Transformer with 3-D rotary position embedding (RoPE) is implemented, circumventing the need to compute relative position information, thereby improving computational efficiency. Furthermore, a part attention mechanism is designed to tackle outlier pollution issues. In the final registration stage, the registration results obtained from each patch pair, weighted by their respective confidence scores, are combined to vote and acquire a more robust final result. In the conducted experiment, the superior quality of features derived from the novel structure's feature extractor enabled our method to attain a better feature matching recall (FMR) in comparison to existing leading methodologies. Moreover, the implementation of the proposed registration method resulted in the highest recorded registration success rate, exceeding the second-best method by 0.8%. In addition, our approach demonstrated remarkable efficiency, being 26% faster than the alternative methods.

关键词： Feature extraction Point cloud compression Accuracy Transformers Data mining Three-dimensional displays Deep learning Attention mechanisms Robustness Real-time systems Gating mechanisms multilevel feature extraction part attention point cloud registration rotary position embedding (RoPE)

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Formation control for linear multi-agent systems with asynchronously sampled outputs

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INFORMATION SCIENCES 2024年 658卷

作者： He, Quangui Liu, Wei South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China

The focus of this paper is to investigate the problem of formation control for linear multi-agent systems through output feedback, utilizing the asynchronous sampled-data mechanism. Each agent matches one state observer to estimate its own state by only using the asynchronously sampled outputs. Based on the asynchronously sampled estimates of states, we develop a formation control law that operates in a distributed manner. The establishment of auxiliary time sequences plays a significant role in coping with the difficulty of asynchronous sampling. Through the study, an explicit upper bound of sampling periods and admissible formation condition, which guarantee that the predefined geometric shape is asymptotically formed, are given. Two examples are eventually carried out to show that formation is achieved successfully.

关键词： General linear dynamics Formation control Asynchronous sampled-data control Output feedback

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Yiqi Li, Zhi Chen, Hongtian The School of Automation Science & Engineering Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350337754

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor. © 2023 IEEE.

关键词： Support vector machines

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