IEEE/CAA Journal of Automatica Sinica

Stability, Control and Fault Diagnosis of Switched Linear Parameter Varying Systems: A Survey

Yanzheng Zhu, Junxing Che, Fen Wu, Xinkai Chen, Weixing Zheng, Donghua Zhou

2025, 12(9): 1745-1761. doi: 10.1109/JAS.2025.125282

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Abstract:
Switched linear parameter varying (LPV) systems have, in recent years, inspired a great number of research endeavors owing to their excellent ability to approximate nonlinear systems and handle complex hybrid dynamics in system analysis and synthesis. Nevertheless, numerous difficulties and challenges are also encountered due to the reciprocal effects of switching signals and scheduling parameters in the analysis and synthesis of switched LPV systems. In this paper, the standard description and specific characteristics of switched LPV systems are first introduced. Then, the main methodologies are proposed in the literature to cope with stability and performance analysis, control synthesis, as well as fault diagnosis and fault-tolerant control issues, and the typical applications in various fields are surveyed. Finally, several key open problems and current research activities are also discussed to elucidate the potential research directions in the future.

Review on Particle Swarm Optimization: Application Toward Autonomous Dynamical Systems

Kavan Bojappa, Junsoo Lee

2025, 12(9): 1762-1775. doi: 10.1109/JAS.2024.125028

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Complex autonomous dynamical systems require sophisticated optimization methods that encompass environment awareness, path planning, and decision-making. swarm intelligence algorithms, inspired by natural phenomena such as bird flocks and fish schools, have undergone significant advancements over recent decades. This paper provides a comprehensive review of particle swarm optimization (PSO) in the context of autonomous systems. We specifically examine the application of PSO to multi-agent dynamical systems, reviewing how PSO variants are employed to tackle diverse optimization challenges across various platforms, including ground vehicles, autonomous underwater vehicles, and unmanned aerial vehicles. Additionally, we delve into the use of PSO within swarm robotics and multi-agent systems. The paper concludes with an outline of potential future research directions, particularly focusing on the application of PSO to the multi-agent rendezvous problem in autonomous systems.

Data-Driven Time-Delay Optimal Control Method for Roller Kiln Temperature Field

Jiayao Chen, Weihua Gui, Ning Chen, Biao Luo, Binyan Li, Zeng Luo, Chunhua Yang

2025, 12(9): 1776-1787. doi: 10.1109/JAS.2025.125309

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Abstract:
In the industrial roller kiln, the time-delay characteristic in heat transfer causes the temperature field to be affected by both the current and historical temperature states. It presents a poor control performance and brings a significant challenge to the process precise control. Considering high complexity of precise modeling, a data-driven time-delay optimal control method for temperature field of roller kiln is proposed based on a large amount of process data. First, the control challenges and problem description brought by time-delay are demonstrated, where the cost function for the time-delay partial differential equation system is constructed. To obtain the optimal control law, the policy iteration in adaptive dynamic programming is adopted to design the time-delay temperature field controller, and neural network is used for the critic network in policy iteration to approximate the optimal time-delay cost function. The closed-loop system stability is proved by designing the Lyapunov function which contains the time-delay information. Finally, through establishing the time-delay temperature field model for roller kiln, the effectiveness and convergence of the proposed method is verified and proved.

Adaptive Fault-Tolerant Control for Unknown Affine Nonlinear Systems Based on Self-Organizing RBF Neural Network

Ran Chen, Donghua Zhou, Li Sheng

2025, 12(9): 1788-1800. doi: 10.1109/JAS.2025.125441

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Abstract:
This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances. To address the hyperparameter initialization challenges inherent in conventional neural network training, an improved self-organizing radial basis function neural network (SRBFNN) with an input-dependent variable structure is developed. Furthermore, a novel self-organizing RBFNN-based observer is introduced to estimate system states across all dimensions. Leveraging the reconstructed states, the proposed adaptive controller effectively compensates for all uncertainties, including estimation errors in the observer, ensuring accurate state tracking with reduced control effort. The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis. Finally, simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.

Modelling Diverse Interactions and Multimodality for Pedestrian Trajectory Prediction

Ruiping Wang, Zhijian Hu, Junzhi Yu, Jun Cheng

2025, 12(9): 1801-1813. doi: 10.1109/JAS.2025.125363

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Abstract:
Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the states and behavior intentions of surrounding pedestrians. However, existing trajectory prediction methods remain failing to effectively model the diverse and complex interactions in the real world, including pedestrian-pedestrian interactions and pedestrian-environment interactions. Besides, these methods are not effective in capturing and characterizing the multimodal property of future trajectories. To address these challenges above, we propose to devise a hand-designed graph convolution and spatial cross attention to dynamically capture the diverse spatial interactions between pedestrians. To effectively explore the impact of scenarios on pedestrian trajectory, we build a pedestrian map, which can reflect the scene constraints and pedestrian motion preferences. Meanwhile, we construct a trajectory multimodality-aware module to capture the different potential mode implicit in diverse social behaviors for pedestrian future trajectory uncertainty. Finally, we compared the proposed method with trajectory prediction baselines on commonly used public pedestrian benchmarks, demonstrating the superior performance of our approach.

Adaptive Dual-Loop Disturbance Observer-Based Robust Model Predictive Tracking Control for Autonomous Hypersonic Vehicles

Runqi Chai, Tianhao Liu, Shaoming He, Kaiyuan Chen, Yuanqing Xia, Hyo-Sang Shin, Antonios Tsourdos

2025, 12(9): 1814-1829. doi: 10.1109/JAS.2025.125291

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Abstract:
To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances, this paper designed a nonlinear robust model predictive control (RMPC) scheme, which can produce near-optimal tracking commands. Unlike the existing designs, the proposed scheme is less conservative and successfully prioritizes the solution optimality. The established RMPC follows a dual-loop structure. Specifically, in the outer feedback loop, the reference attitude angle profiles are optimally tracked, while in the inner feedback loop, the control moment commands are produced by optimally tracking the desired angular rate trajectories. Besides, an adaptive disturbance observer (ADO) is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances. The recursive feasibility of the optimization process, together with the input-to-state stability of the proposed RMPC, is theoretically guaranteed by introducing a tightened control constraint and terminal region. The derived property reveals that our proposal can steer the tracking error within a small region of convergence. Finally, the effectiveness of the proposed scheme is demonstrated by performing simulation studies.

A Decision Variables Classification-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems

Xuanxuan Ban, Jing Liang, Kangjia Qiao, Kunjie Yu, Yaonan Wang, Jinzhu Peng, Boyang Qu

2025, 12(9): 1830-1849. doi: 10.1109/JAS.2025.125276

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Abstract:
Solving constrained multi-objective optimization problems (CMOPs) is a challenging task due to the presence of multiple conflicting objectives and intricate constraints. In order to better address CMOPs and achieve a balance between objectives and constraints, existing constrained multi-objective evolutionary algorithms (CMOEAs) predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints, and the designed strategies usually are effective for the problems with simple constraints. However, these methods most ignore the relationship between decision variables and constraints. In fact, the essence of optimization is to find appropriate decision variables to meet various complex constraints. Therefore, it is hoped that the problem can be analyzed from the perspective of decision variables, so as to obtain more excellent results. Based on the above motivation, this paper proposes a decision variables classification approach, according to the relationship between decision variables and constraints, variables are divided into constraint-related (CR) variables and constraint-independent (CI) variables. Consequently, by optimizing these two types of variables independently, the population can sustain a favorable balance between feasibility and diversity. Furthermore, specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity. Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.

Dynamic Evolutionary Game-Based Staking Pool Selection Modeling and Decentralization Enhancement for Blockchain System

Shasha Yu, Yanan Qiao, Fan Yang, Wenjia Zhao, Junge Bo

2025, 12(9): 1850-1865. doi: 10.1109/JAS.2025.125447

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The proof-of-stake (PoS) mechanism is a consensus protocol within blockchain technology that determines the validation of transactions and the minting of new blocks based on the participant’s stake in the cryptocurrency network. In contrast to proof-of-work (PoW), which relies on computational power to validate transactions, PoS employs a deterministic and resource-efficient approach to elect validators. Whereas, an inherent risk of PoS is the potential for centralization among a small cohort of network participants possessing substantial stakes, jeopardizing system decentralization and posing security threats. To mitigate centralization issues within PoS, this study introduces an incentive-aligned mechanism named decentralized proof-of-stake (DePoS), wherein the second-largest stakeholder is chosen as the final validator with a higher probability. Integrated with the verifiable random function (VRF), DePoS rewards the largest stakeholder with uncertainty, thus disincentivizing stakeholders from accumulating the largest stake. Additionally, a dynamic evolutionary game model is innovatively developed to simulate the evolution of staking pools, thus facilitating the investigation of staking pool selection dynamics and equilibrium stability across PoS and DePoS systems. The findings demonstrate that DePoS generally fosters wealth decentralization by discouraging the accumulation of significant cryptocurrency holdings. Through theoretical analysis of stakeholder predilection in staking pool selection and the simulation of the evolutionary tendency in pool scale, this research demonstrates the comparative advantage in decentralization offered by DePoS over the conventional PoS.

A Robust Direct-Discretized RNN for Time-Dependent Optimization Constrained by Nonlinear Equalities and Its Applications

Guangfeng Cheng, Binbin Qiu, Jinjin Guo, Yu Han

2025, 12(9): 1866-1877. doi: 10.1109/JAS.2025.125627

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Abstract:
In recent years, numerous recurrent neural network (RNN) models have been reported for solving time-dependent nonlinear optimization problems. However, few existing RNN models simultaneously involve nonlinear equality constraints, direct discretization, and noise suppression. This limitation presents challenges when existing models are applied to practical engineering problems. Additionally, most current discrete-time RNN models are derived from continuous-time models, which may not perform well for solving essentially discrete problems. To handle these issues, a robust direct-discretized RNN (RDD-RNN) model is proposed to efficiently realize time-dependent optimization constrained by nonlinear equalities (TDOCNE) in the presence of various time-dependent noises. Theoretical analyses are provided to reveal that the proposed RDD-RNN model possesses excellent convergence and noise-suppressing capability. Furthermore, numerical experiments and manipulator control instances are conducted and analyzed to validate the superior robustness of the proposed RDD-RNN model under various time-dependent noises, particularly quadratic polynomial noise. Eventually, small target detection experiments further demonstrate the practicality of the RDD-RNN model in image processing applications.

Active Compression on Unknown Disturbance and Uncertainty via Extended State Observer

Jiuqiang Deng, Luyao Zhang, Wenchao Xue, Qiliang Bao, Yao Mao

2025, 12(9): 1878-1892. doi: 10.1109/JAS.2025.125342

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Abstract:
Extended state observer (ESO) is heavily limited by the unknown disturbance and its derivative, which requires high observing gains to decrease estimating error, resulting in serious noise sensitivity. To modify the disturbance estimation characteristics encountered by the observer, the active compression extended state observer (ACESO) is proposed in this study. The ACESO decreases the bound of residual lumped disturbance and its derivative by actively compressing the initial lumped disturbance, without relying on prior knowledge. The stability constraint and convergence results of ACESO are analyzed and compared with ESO theoretically. The results show that the ACESO mitigates the trade-off between noise sensitivity and high-gain observation. Benefiting from active compression, the ACESO has substantially less noise sensitivity than the ESO, while obtaining the same and even better estimating performance than the ESO. In addition, the nonlinear ACESO is discussed, which automatically balances the contradiction between estimation and convergence. Simulations and experiments demonstrate the effectiveness of the proposed methods.

The Application of RVM in GNSS Anti-Spoofing Field Based on the Hybrid Kernel Function

Junzhi Li, Qiuying Yu, Gangqiang Li, Yu He

2025, 12(9): 1893-1907. doi: 10.1109/JAS.2025.125522

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Abstract:
With the widespread application of global navigation satellite system (GNSS), spoofing attacks pose a threat to the security and reliability of GNSS. It is of great significance to design effective GNSS spoofing detection technology to ensure the security and reliability of GNSS system applications for receiver users. Traditional spoofing detection techniques generally only determine whether a spoofing attack has occurred by monitoring the feature changes of one or two data information in the receiver. However, some spoofing modes can cleverly make the monitored data very close to the real data, thus avoiding these detection methods and easily making them ineffective. In this study, a GNSS spoofing jamming detection method based on hybrid kernel relevance vector machine (RVM) is proposed. The improved signal quality monitoring (SQM) movement variance, carrier noise ratio movement variance, pseudo range Doppler consistency, pseudorange residual, Doppler frequency, clock offset and clock drift are used as detection characteristics. This technology can detect GNSS spoofing signals, effectively improving the safety and reliability of GNSS systems. The experimental results show that this technology has high detection accuracy and anti-interference ability and can effectively respond to various forms of spoofing attacks.

Model-Based Decentralized Dynamic Periodic Event-Triggered Control for Nonlinear Systems Subject to Packet Losses

Chengchao Li, Xudong Zhao, Wei Xing, Ning Xu, Ning Zhao

2025, 12(9): 1908-1919. doi: 10.1109/JAS.2025.125459

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This paper studies the problem of designing a model-based decentralized dynamic periodic event-triggering mechanism (DDPETM) for networked control systems (NCSs) subject to packet losses and external disturbances. Firstly, the entire NCSs, comprising the triggering mechanism, packet losses and output-based controller, are unified into a hybrid dynamical framework. Secondly, by introducing dynamic triggering variables, the DDPETM is designed to conserve network resources while guaranteeing desired performance properties and tolerating the maximum allowable number of successive packet losses. Thirdly, some stability conditions are derived using the Lyapunov approach. Differing from the zero-order-hold (ZOH) case, the model-based control sufficiently exploits the model information at the controller side. Between two updates, the controller predicts the plant state based on the models and received feedback information. With the model-based control, less transmission may be expected than with ZOH. Finally, numerical examples and comparative experiments demonstrate the effectiveness of the proposed method.

Unsupervised Dynamic Discrete Structure Learning: A Geometric Evolution Method

Chaoqun Fei, Yangyang Li, Xikun Huang, Ge Zhang, Ruqian Lu

2025, 12(9): 1920-1937. doi: 10.1109/JAS.2025.125165

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Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical method for discovering latent geometric structures, has provided important nonlinear insight for the theoretical development of unsupervised representation learning. However, due to the shallow learning mechanism of the existing methods, they can only exploit the simple geometric structure embedded in the initial data, such as the local linear structure. Traditional manifold learning methods are fairly limited in mining higher-order nonlinear geometric information, which is also crucial for the development of unsupervised representation learning. To address the abovementioned limitations, this paper proposes a novel dynamic geometric structure learning model (DGSL) to explore the true latent nonlinear geometric structure. Specifically, by mathematically analysing the reconstruction loss function of manifold learning, we first provide universal geometric relational function between the curvature and the non-Euclidean metric of the initial data. Then, we leverage geometric flow to design a deeply iterative learning model to optimize this relational function. Our method can be viewed as a general-purpose algorithm for mining latent geometric structures, which can enhance the performance of geometric representation methods. Experimentally, we perform a set of representation learning tasks on several datasets. The experimental results show that our proposed method is superior to traditional methods.