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Geometric attitude tracking control of quadrotor UAVs with adaptive extended state observers

ISA Transactions 2025年 160卷 289-297页

作者： Wang, Liping Chen, Jiapeng Huang, Yuyuan Zhu, Yongbin Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AESO can be transformed into a canonical form which is easier to analyze by linear time-varying theory. In the presence of unknown disturbances and system uncertainties, the estimated error and stability of AESO can be analyzed. Simultaneously, a geometric tracking controller is developed on SO(3) with AESOs which guarantees that the error exponentially converges to a bounded set related to the estimated error. Some examples and a flight test are given to verify the availability of the control scheme. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Persistent Excitation of Improved RBF Neural Networks: Neuron Dynamic-Growing Strategy

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2025年 PP卷 PP页

作者： Wang, Min Wang, Mingyu Yang, Chenguang South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

This brief proposes a novel neuron dynamic-growing (NDG) strategy for radial basis function neural networks (RBF NNs). Only one neuron is selected in advance relying on the system initial states, and other neurons are dynamically generated based on the designed threshold for the distance between the current NN input and the closest neuron. Compared with the RBF NN using neuron fixed evenly spaced strategy (NFES), the improved RBF NN has two major advantages: one is to extremely reduce the number of neurons, especially for the high dimensional NN inputs;and the other is to provide a theoretical criteria for the choice of NN structure parameters including the neuron center and the compact set size. To guarantee the dynamic learning ability of the improved RBF NN, the persistent excitation (PE) is verified strictly by subtly constructing the threshold and the center of newly added neurons. Simulation and experimental results illustrate that the improved RBF NN integrated into the existing dynamic learning control effectively enhances the transient control performance, reduces the computational burden, and saves data storage space.

关键词： Artificial neural networks Neurons Trajectory Vectors Biological neural networks Accuracy Learning systems System dynamics Radial basis function networks Nonlinear dynamical systems Dynamic learning neuron dynamic-growing (NDG) strategy neural network (NN) approximation persistent excitation (PE)

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Model Predictive Optimization and Terminal Sliding Mode Motion control for Mobile Robot With Obstacle Avoidance

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Li, Bin Ji, Zhengcong Zhao, Zhijia Yang, Chenguang South China Univ Technol Key Lab Autonomous Syst & Networked Control Coll Automat Sci & Engn Guangzhou 510640 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

This article proposes an optimization dynamic movement primitives (ODMP) strategy and terminal sliding mode motion control (TSMC) method for the generalization problem of mobile robot when facing with obstacles. In scenarios where obstacles are present along the reproduced trajectories, an online trajectory planning algorithm based on model predictive optimization ensures that the robot reproduces the demonstrated trajectory as closely as possible without colliding with obstacles, in which the obstacles are modeled as the soft and hard constraint simultaneously, and the model predictive control (MPC) is utilized to search optimal solutions for path planning in the presence of obstacle. Additionally, a dynamic controller based on super-twisting practice terminal sliding mode (STPTSM) control ensures rapid and accurate tracking of the trajectories planned by MPC while improve accuracy during tracking, thus ensuring convergence within a finite convergence time. Experiments on a two-wheeled differential mobile robot are carried out to verify the proposed method.

关键词： Trajectory Collision avoidance Path planning Optimization Dynamics Robots Predictive models Trajectory planning Prediction algorithms Mobile robots Dynamic movement primitives mobile robot model predictive optimization obstacle avoidance terminal Sliding mode control

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Min-max tracking model predictive control for linear parameter-varying systems using polyhedral invariant sets

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ASIAN JOURNAL OF control 2025年

作者： Peng, Kai-Yu Xie, Wei Zhang, Langwen South China Univ Technol Sch Automat Sci & Engn 381 Wushan Rd Guangzhou 510641 Peoples R China Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou Peoples R China South China Univ Technol Guangdong Prov Key Lab Tech & Equipment Macromol A Guangzhou Peoples R China

In this paper, a min-max tracking model predictive control (MPC) method for linear parameter-varying (LPV) systems using polyhedral invariant sets is proposed. The method aims to expand the error state stabilizable domain and improve dynamic performance while handling asymmetric system constraints and guaranteeing robust stability under parameter uncertainty, with low computational burden. Firstly, the augmented dynamic formulation is constructed based on the original state-space model and reference trajectory dynamic model to obtain the tracking error state. Secondly, a min-max optimization problem considering parameter variation and system constraints is formulated based on the tracking error state. Thirdly, a sequence of optimal control laws is offline obtained by solving the optimization problem to design the nested corresponding polyhedral invariant sets. These sets have a larger error state stabilizable domain than ellipsoidal invariant sets. An interpolation method is applied to improve dynamic tracking performance during online control. Finally, the simulation results and comparative analysis substantiate the effectiveness of the proposed min-max tracking MPC method.

关键词： LPV systems model predictive control polyhedral invariant sets robust control tracking control

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A Fractional-Order ADRC With Improved Robustness to Plant Gain Variations

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Wang, Shaohua Huang, Pengjie Luo, Ying Wang, Xiaohong Luo, Xin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China South China Univ & Technol Minist Educ Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Peoples R China

This article proposes a fractional-order active disturbance rejection control (ADRC) scheme to enhance robustness against plant gain variations. The scheme integrates a model-aided extended state observer (MESO) and a fractional-order feedback controller. The MESO ensures equivalence to an ideal double-integrator model, while the fractional-order feedback controller, designed based on Bode's ideal transfer function (BITF), achieves robust performance in the face of plant gain variations. An analytical synthesis method is introduced to ensure that the open-loop system maintains an approximate BITF characteristic near the gain crossover frequency, resulting in a consistent phase margin despite changes in plant gain. Simulation analysis on two different systems demonstrates improved robustness to plant parameter uncertainties using the proposed scheme and tuning method. Extensive experiments conducted on a permanent magnet synchronous motor (PMSM) speed servo system further validate the effectiveness of the proposed scheme, showing significantly enhanced robustness to plant gain variations compared with other control methods. Additionally, the scheme provides superior reference tracking and disturbance rejection performance.

关键词： Robustness Adaptive control Uncertainty Transfer functions control systems Tuning Robust control Observers Gain Servomotors Active disturbance rejection control (ADRC) Bode's ideal transfer function (BITF) fractional order control model-aided ESO plant gain variations robustness

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Biomimetic swarm fission driven algorithm with preassigned target subgroup size

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BIOINSPIRATION & BIOMIMETICS 2025年第2期20卷 026021-026021页

作者： Cai, He Wang, Hao Bei, Zixin Zhou, Dongkuan Gao, Huanli South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China Guangdong Engn Technol Res Ctr Unmanned Aerial Veh Guangzhou Peoples R China

Inspired by killer whale hunting strategies, this study presents a biomimetic algorithm for controlled subgroup fission in swarms. The swarm agents adopt the classic social force model with some practical modifications. The proposed algorithm consists of three phases: cluster selection phase via a constrained K-means algorithm, driven phase with strategic agent movement, including center pushing, coordinated oscillation, and flank pushing by specialized driven agents, and judgment phase confirming subgroup separation using the Kruskal algorithm. Simulation results confirm the algorithm's high success rate and efficiency in subgroup division, demonstrating its potential for advancing swarm-based technologies.

关键词： biomimetic algorithm swarm fission subgroup size control social force model

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Event-Triggered Distributed Resilient State Estimator for Nonlinear Cyber-Physical systems Under Hybrid Cyberattacks

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INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL PROCESSING 2025年第3期39卷 529-543页

作者： Wu, Zhenwei Zhang, Langwen South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou Peoples R China South China Univ Technol Guangdong Prov Key Lab Tech & Equipment Macromol A Equipment Macromol Adv Mfg Guangzhou Peoples R China

This article investigates the problem of event-triggered distributed resilient state estimator (ET-DRSE) for nonlinear cyber-physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattacks is constructed by considering both false data injection (FDI) and Denial-of-Service (DoS) attacks. By proposing an event-triggered mechanism, the network burden is reduced by decreasing the transmission rate of measurement information. To improve the flexibility, a detector-based distributed resilient state estimator framework is derived. Coupled Riccati-like difference equations (CRDEs) are proposed to obtain an upper bound (UB) of the estimation error covariance (EEC) of every subsystem. An estimation gain is designed to minimize the UB upon the error covariance of the resilient estimator by recursively solving the CRDEs. It is proved that the estimation error is probabilistically bounded through the application of random analytic theory. Finally, the performance of the proposed ET-DRSE is verified through a numerical example and a chemical process example.

关键词： distributed resilient state estimation event-triggered mechanism hybrid cyberattacks nonlinear cyber-physical systems

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Resilient distributed model predictive control of state-delayed linear parameter varying systems with quantitative communication against denial of service attacks

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ASIAN JOURNAL OF control 2025年第2期27卷 728-740页

作者： Zhong, Aiping Lu, Wanlin Zhang, Langwen South China Univ Technol Sch Automat Sci & Technol Guangzhou Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China South China Univ Technol Guangdong Prov Key Lab Tech & Equipment Macromol A Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou Peoples R China

This work presents a resilient distributed model predictive control (MPC) method for linear parameter varying (LPV) systems with state delays and attacks in communication networks. Coordinations are required for distributed MPC (DMPC) to achieve the global performance of centralized MPC (CMPC). However, control performance can be severely degraded by unreliable communication networks, for example, with denial of service (DoS) attacks. A resilient control framework is derived to address the unreliable communications in DMPC. A global system is divided into subsystems for the distributed control purpose. To deal with the model uncertainties and state delays, a "min-max" DMPC algorithm is presented with a buffer to ensure resilience against DoS attacks. A quantization scheme is introduced to quantize the control information exchanged between subsystems. An iterative interaction scheme is proposed to exchange feedback control laws among subsystems. The stability of the closed-loop system under the proposed algorithm is ensured by using a Lyapunov function method. The effectiveness of the proposed DMPC is demonstrated through two simulation examples.

关键词： denial of service attacks distributed model predictive control linear parameter varying quantized communication state-delayed

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A Novel Robotic Skill Learning Approach for Assembly Task With Dynamical System and Broad Learning

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Zhang, Jian Jin, Zhehao Zhao, Zhijia Yang, Chenguang South China Univ Technol Minist Educ Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Nanyang Technol Univ Singapore 639798 Singapore Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

This study introduces a strategy for robotic peg-hole assembly designed to efficiently transfer skills from a human instructor to a robot, achieved through a series of demonstrations and training sessions. Moving the peg fixed to the robotic arm's end from the distal position to the top of the hole, which is represented as an autonomous dynamical system. During the modeling and generalization phases, a fast modeling learning method based on an extreme learning machine is proposed, and an energy function is used to improve the generalization accuracy. This approach enables the system to maintain high tracking accuracy and stability while resisting disturbances. Additionally, an obstacle avoidance strategy is implemented to prevent the peg from contacting obstacles during movement. The incremental learning-based broad learning system (BLS) is used for peg-hole assembly, where peg-hole position and attitude are adjusted by continuous neural network training during insertion until 1-3 adjustments are made to complete the assembly. Finally, the developed skill-learning strategy's effectiveness is confirmed through the validation of simulations and experiments.

关键词： Assembly Robots Training Accuracy Stability analysis Asymptotic stability Trajectory Service robots Collision avoidance Lyapunov methods Broad learning system (BLS) dynamical systems (DSs) peg-hole assembly robotic skill learning

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Predefined time trajectory tracking control and hardware in loop verification for quadrotor UAV under input delay and disturbances

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ASIAN JOURNAL OF control 2025年第0期

作者： Li, Shuo Duan, Na Pei, Hailong South China Univ Technol Guangdong Engn Technol Res Ctr Unmanned Aerial Veh Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked ControlMinist Guangzhou 510640 Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou Peoples R China

This paper deals with the predefined time trajectory tracking control of quadrotor unmanned aerial vehicle (UAV) system in the presence of input delay and external disturbances. By combining the backstepping with the predefined time nonlinear disturbance observer, the uncertainty caused by the external disturbance is eliminated, enabling the UAV to reach the control target within a predefined time. Moreover, in order to compensate for the impact of the control input delay, an appropriate auxiliary system is introduced. Simulation and hardware-in-the-loop (HIL) experimental are carried out to show the effectiveness of the proposed control scheme.

关键词： input delay predefined time nonlinear disturbance observer predefined time tracking control quadrotor UAV

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