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Robust Output Regulation With Prescribed Performance for Second-Order Non-Polynomial Nonlinear systems

IEEE Transactions on Automation Science and Engineering 2025年 22卷 14234-14246页

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

This article focuses on the robust output regulation problem with prescribed performance for uncertain second-order nonlinear systems with non-polynomial nonlinearity. According to the existing framework for the robust output regulation problem of such nonlinear systems, we introduce some standard assumptions, and then construct two internal models of nonlinear forms to reproduce the steady-state information. Then, we convert the considered problem into the robust stabilization problem with prescribed performance of an augmented system containing the studied nonlinear system and the internal models. We incorporate a novel error transformation into the existing technique to design a stabilization controller, based on which we synthesize our control law to solve our original problem. Finally, we present two simulation examples to test our results. © 2004-2012 IEEE.

关键词： Nonlinear systems Performance evaluation Regulation Steady-state Transient analysis Polynomials Mathematical models Output feedback Adaptive systems Symmetric matrices

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Leader-Following Consensus With Prescribed Performance for Linear Multiagent systems

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第2期70卷 1402-1409页

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

In this article, we consider the leader-following consensus problem with prescribed performance for general linear multiagent systems. To solve this problem, we construct a distributed state feedback control law on the basis of some barrier Lyapunov functions. Then, all the consensus errors are proved to converge to zero asymptotically and the time-varying prescribed performance is proved to be achieved. Finally, we give a simulation example to confirm our results.

关键词： Vectors Convergence Transient analysis Switches State feedback Protocols Multi-agent systems Lyapunov methods Time-varying systems Output feedback Barrier Lyapunov function (BLF) leader-following consensus linear systems multiagent systems (MASs) prescribed performance

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Rapid dynamical learning from neural control of sampled-data nonlinear systems via pseudo-inverse regression filter vector signal

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第5期362卷

作者： Liang, Dengxiang Wang, Min South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China

In this paper, a rapid neural learning control method utilizing a pseudo-inverse regression filter vector signal strategy is developed for sampled-data strict-feedback nonlinear systems, targeting enhancements in both neural learning speed and output tracking performance. Firstly, the consistency condition is proposed to ensure the stability of the sampled-data system, which is derived from the stability framework of the approximate model. Subsequently, a pseudo-inverse regression filter vector signal-based adaptive neural dynamic surface control (PIRFVB-ANDSC) is proposed by integrating digital first-order filter, regression filter and pseudo-inverse technique. Specially, a new form of NN weight updating law is built upon the pseudo-inverse regression filter vector signal. The persistent excitation (PE) level of the pseudo-inverse regression filter signal is verified to be independent of the system control gain function and PE level of Gaussian activation function which is convenient for performance analysis. Then, it is verified that PIRFVB-ANDSC effectively accelerates convergence of NN estimated weights and reduces the consumption of computing resources. The convergent weights are utilized to develop a disturbance observer-based neural learning dynamic surface control (DOB-NLDSC) to improve robustness. Finally, the simulation comparison demonstrates several key advantages of the scheme with some merits including the rapid NN learning speed, the enhanced tracking performance, and the strong robustness.

关键词： Sampled-data system Neural networks Persistent excitation Regression filter Learning control Pseudo-inverse

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Sampled-data output regulation for a class of linear time-delay systems

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INTERNATIONAL JOURNAL OF systems SCIENCE 2025年

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

This paper presents an aperiodic sampled-data control scheme to address the output regulation problem for a class of linear time-delay systems. First, we demonstrate that the problem can be solved exactly by the sampled-data dynamic output feedback control law equipped with sequential predictors under a constant exogenous signal. Second, we demonstrate that the problem can be solved practically by the same control law under a time-varying exogenous signal with bounded first derivative. Also, we consider that delay occurs during the transmission of signals from predictors in both scenarios. Finally, we substantiate the effectiveness of our proposed control approach with the aid of two illustrative examples.

关键词： Output regulation linear time-delay systems sampled-data control sequential predictors

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Semi-Global Adaptive Sampled-Data Formation control for a Class of Uncertain Nonlinear Multiagent systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第4期55卷 2771-2781页

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

This article studies the semi-global adaptive sampled-data formation control problem for a class of nonlinear multiagent systems. The nonlinear growth rate is not limited, which expands the applicable scope of the obtained results. To remove the assumption that the system matrix of the leader system can be obtained by all follower systems, a sampled-data adaptive distributed observer is proposed to estimate not only the state but also the system matrix of the leader system, which are measured only at discrete time. All the estimation errors of the observers tend to zero as time tends to infinity. Then, by using the estimated state, a sampled-data adaptive control law is designed to achieve the formation control practically. Finally, a simulation example is shown to demonstrate the feasibility of our result.

关键词： Observers Multi-agent systems Vectors Event detection Adaptive control Trajectory Symbols Nonlinear dynamical systems Training Adaptive distributed observer formation control multiagent systems (MASs) nonlinear systems sampled-data control sampled-data control

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A Variable Parameter LoD Model Point Cloud Compression Method Based on Attention Mechanism 17th

A Variable Parameter LoD Model Point Cloud Compression Metho...

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17th International Conference on Intelligent Robotics and Applications

作者： Su, Kainan Zhou, Yajing Zhan, Hong Yang, Chenguang South China Univ Technol Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China

ISBN: (纸本)9789819607884;9789819607891

In certain hazardous environments, teleoperation allows human operators to maintain a safe distance while controlling robots to perform tasks. Point clouds can be utilized to create an immersive environment, enhancing the accuracy and safety of remote operations. To address the inefficiency of the current 3D point cloud compression methods in robot teleoperation systems, by integrating teleoperation devices, this paper proposes a variable parameter level of detail (LoD) model point cloud compression method based on attention mechanism. By extracting the operator's attention information, remote robots are guided to compress the point cloud, significantly improving the efficiency of point cloud compression. Firstly, a LoD compression model is designed. During runtime, operator's eye gaze and arm stiffness information is obtained using virtual reality (VR) glasses and MYO armband, which is translated into focal point, focus range and precision requirements, dynamically adjusting the parameters of the LoD compression model accordingly. Finally, point cloud downsampling is conducted based on the LoD compression model, and serialization is performed using octrees. Experimental results demonstrate the effective improvement in point cloud compression efficiency achieved by the proposed method.

关键词： 3D point cloud attention mechanism teleoperation LoD model transmission

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Rapid dynamical pattern modeling for sampled-data nonlinear systems using regression filter

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NONLINEAR DYNAMICS 2025年第10期113卷 11439-11454页

作者： Liang, Dengxiang Wang, Min South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China Pengcheng Lab Shenzhen Peoples R China

This paper investigates a rapid dynamical pattern modeling method for a class of nonlinear sampled-data systems. Firstly, within the nonlinear sampled-data systems framework, the consistency condition is presented based on the approximate discrete-time model. Then, a regression filter-based dynamic learning method is proposed to enhance online learning performance of neural networks (NNs). The exponential convergence of NN estimated weights, which stem from regression filter-based weight update law, is deduced based on two newly derived corollaries. The unknown system nonlinear dynamics are accurately modeled using the dynamic learning method. The modeling knowledge is defined as training patterns which are expressed in a group of constant NNs. Analytical results concerning the pattern recognition condition and recognition time are derived based on the duty ratio, consistency condition, and feature interval. The stored knowledge can be reused for dynamical pattern recognition. The effectiveness of the proposed scheme is illustrated through the simulation results.

关键词： Sampled-data systems Neural networks Persistent excitation Regression filter Pattern recognition

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SkateDuct: Utilizing Vector Thrust of Ducted Fan UAVs for Terrestrial-Aerial Locomotion

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第6期10卷 6047-6054页

作者： Yin, Zhong Pei, Hailong South China Univ Technol Minist Educ Guangdong Engn Technol Res Ctr Unmanned Aerial Veh Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Peoples R China

Ducted fan UAVs (DFUAVs), characterized by vector thrust, vertical takeoff and landing (VTOL) capabilities, and high safety, have found widespread applications in both military and civilian scenarios. However, their limited endurance remains a significant constraint on their broader applications. To address this challenge, in this letter we explore a novel approach that exploits the vector thrust capabilities of DFUAVs to enable terrestrial-aerial locomotion through simple modifications without the need for additional actuators. The design of a DFUAV employing passive wheels for continuous ground and aerial operation is presented. This configuration allows for unchanged attitude and static stability during ground movement, with only a 10.3% increase in weight. Fluid simulations were conducted to analyze the variation in control vane aerodynamic efficiency under ground effect, leading to the development of a ground-effect-adjusted aerodynamic model based on experimental data. Furthermore, the dynamics of ground movement are analyzed, and a corresponding controller is developed, establishing a complete framework for seamless transition between terrestrial and aerial modes. Extensive real-world flight experiments validate the proposed structural design and control methods. By utilizing terrestrial locomotion, the UAV's energy consumption is reduced to just 33.9% of that during flight, effectively extending its operational duration by more than ten times.

关键词： Blades Fans Actuators Wheels Aerodynamics autonomous aerial vehicles Vectors Force Rotors Analytical models Aerial systems: applications aerial systems: mechanics and control

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A dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy

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Multimedia Tools and Applications 2025年 1-19页

作者： Su, Kainan Wang, Zunran Yang, Chenguang The Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510641 China Huawei Technologies Co Ltd Shenzhen518028 China

Dynamic point clouds can be compressed by eliminating spatial and temporal redundancy, but few research studies have considered both simultaneously. Existing research can only distinguish the specific foreground and background of the scene, achieving the former with a limited compression ratio, while the latter relies on 3D motion estimation with high computing complexity. Therefore, it is difficult to meet the real-time requirement of building remote human-robot interaction system. Aiming at the shortcomings of the above methods, a dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy is proposed. On the one hand, for eliminating the spatial redundancy, the information of the recognized objects in point cloud data will be replaced with their categories and poses. On the other hand, we eliminate the temporal redundancy by using the motion vector of the point cloud macroblock. Specifically, in order to reduce cost time of the 3D motion estimation, we firstly introduce the 2D image ORB feature to calculate the motion vector from current frame to key frame, and then look for the similar macroblock in key frame. Thus, the information of the macroblocks similar to the key frame in the current frame is replaced by the index of the corresponding macroblocks of the key frame and the motion vector. Experimental results show that the proposed method has higher computational efficiency and compression quality than the existing compression algorithm. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Hybrid variable admittance force tracking and fixed-time position control for robot-environment interaction

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2025年第1期45卷 1-12页

作者： Zhan, Hong Ye, Dexi Zeng, Chao Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China Univ Liverpool Dept Comp Sci Liverpool England

PurposeThis paper aims to deal with the force and position tracking problem when a robot performs a task in interaction with an unknown environment and presents a hybrid control strategy based on variable admittance control and fixed-time ***/methodology/approachA hybrid control strategy based on variable admittance control and fixed-time control is presented. Firstly, a variable stiffness admittance model control based on proportional integral and differential (PID) is adopted to maintain the expected force value during the task execution. Secondly, a fixed-time controller based on radial basis function neural network (RBFNN) is introduced to handle the model uncertainties and ensure the fast position tracking convergence of the robot system, while the singularity problem is also avoided by designing the virtual control variable with piecewise *** studies conducted on the robot manipulator with two degrees of freedom have verified the superior performance of the proposed control strategy comparing with other ***/valueA hybrid control scheme for robot-environment interaction is presented, in which the variable stiffness admittance method is adopted to adjust the interaction force to the desired value, and the RBFNN-based fixed-time position controller without singularity problem is designed to ensure the fast convergence of the robot system with model uncertainty.

关键词： Variable admittance Force tracking Fixed-time control RBFNN Robot-environment interaction

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