IEEE/CAA Journal of Automatica Sinica

What Does ChatGPT Say: The DAO from Algorithmic Intelligence to Linguistic Intelligence

Fei-Yue Wang, Qinghai Miao, Xuan Li, Xingxia Wang, Yilun Lin

2023, 10(3): 575-579. doi: 10.1109/JAS.2023.123486

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Meta-Energy: When Integrated Energy Internet Meets Metaverse

Chenghui Zhang, Shuai Liu

2023, 10(3): 580-583. doi: 10.1109/JAS.2023.123492

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A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems

Boqian Li, Shiping Wen, Zheng Yan, Guanghui Wen, Tingwen Huang

2023, 10(3): 584-602. doi: 10.1109/JAS.2023.123075

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This survey provides a brief overview on the control Lyapunov function (CLF) and control barrier function (CBF) for general nonlinear-affine control systems. The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming (QP) problem. The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems. These objectives imply important properties including controllability, convergence, and robustness of control problems. Under this framework, optimal control corresponds to the minimal solution to a constrained QP problem. When uncertainties are explicitly considered, the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances. The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper. Finally, we provide research directions that are significant for the advance of knowledge in this area.

Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

Qinghai Miao, Yisheng Lv, Min Huang, Xiao Wang, Fei-Yue Wang

2023, 10(3): 603-631. doi: 10.1109/JAS.2023.123375

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The virtual-to-real paradigm, i.e., training models on virtual data and then applying them to solve real-world problems, has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine learning. To summarize the advances in recent years, this survey comprehensively reviews the literature, from the viewport of parallel intelligence. First, an extended parallel learning framework is proposed to cover main domains including computer vision, natural language processing, robotics, and autonomous driving. Second, a multi-dimensional taxonomy is designed to organize the literature in a hierarchical structure. Third, the related virtual-to-real works are analyzed and compared according to the three principles of parallel learning known as description, prediction, and prescription, which cover the methods for constructing virtual worlds, generating labeled data, domain transferring, model training and testing, as well as optimizing the strategies to guide the task-oriented data generator for better learning performance. Key issues remained in virtual-to-real are discussed. Furthermore, the future research directions from the viewpoint of parallel learning are suggested.

UltraStar: A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

Xiaoyu Liu, Ting Ma, Zhixuan Tang, Xiaohan Qin, Haibo Zhou, Xuemin (Sherman) Shen

2023, 10(3): 632-645. doi: 10.1109/JAS.2023.123084

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The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation (6G) wireless communication systems. However, the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance evaluation. In this paper, we introduce UltraStar, a lightweight network simulator, which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented scale. Particularly, a systematic and extensible architecture is proposed, where the joint requirement for network simulation, quantitative evaluation, data statistics and visualization is fully considered. For characterizing the network, we make lightweight abstractions of physical entities and models, which contain basic representatives of networking nodes, structures and protocol stacks. Then, to consider the high dynamics of Walker constellations, we give a two-stage topology maintenance method for constellation initialization and orbit prediction. Further, based on the discrete event simulation (DES) theory, a new set of discrete events is specifically designed for basic network processes, so as to maintain network state changes over time. Finally, taking the first-generation Starlink of 11 927 low earth orbit (LEO) satellites as an example, we use UltraStar to fully evaluate its network performance for different deployment stages, such as characteristics of constellation topology, performance of end-to-end service and effects of network-wide traffic interaction. The simulation results not only demonstrate its superior performance, but also verify the effectiveness of UltraStar.

Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

Vu Phi Tran, Matthew A. Garratt, Kathryn Kasmarik, Sreenatha G. Anavatti

2023, 10(3): 646-661. doi: 10.1109/JAS.2023.123087

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A common assumption of coverage path planning research is a static environment. Such environments require only a single visit to each area to achieve coverage. However, some real-world environments are characterised by the presence of unexpected, dynamic obstacles. They require areas to be revisited periodically to maintain an accurate coverage map, as well as reactive obstacle avoidance. This paper proposes a novel swarm-based control algorithm for multi-robot exploration and repeated coverage in environments with unknown, dynamic obstacles. The algorithm combines two elements: frontier-led swarming for driving exploration by a group of robots, and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles. We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments. We measure both repeated coverage performance and obstacle avoidance ability. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.

Event-Triggered Asymmetric Bipartite Consensus Tracking for Nonlinear Multi-Agent Systems Based on Model-Free Adaptive Control

Jiaqi Liang, Xuhui Bu, Lizhi Cui, Zhongsheng Hou

2023, 10(3): 662-672. doi: 10.1109/JAS.2022.106070

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In this paper, an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism. For the agents described by a structurally balanced signed digraph, the asymmetric bipartite consensus objective is firstly defined, assigning the agents’ output to different signs and module values. Considering with the completely unknown dynamics of the agents, a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents’ triggered outputs and an equivalent compact form data model. By utilizing the Lyapunov analysis method, the threshold of the triggering condition is obtained. Subsequently, the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle. Finally, the simulation example further demonstrates the effectiveness of the protocol.

Cooperative Target Tracking of Multiple Autonomous Surface Vehicles Under Switching Interaction Topologies

Lang Ma, Yu-Long Wang, Qing-Long Han

2023, 10(3): 673-684. doi: 10.1109/JAS.2022.105509

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This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles (ASVs) under switching interaction topologies. For the target to be tracked, only its position can be measured/received by some of the ASVs, and its velocity is unavailable to all the ASVs. A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs’ dynamics. Accordingly, a novel kinematic controller is designed, which takes full advantage of known information and avoids the approximation of some virtual control vectors. Moreover, a disturbance observer is presented to estimate unknown time-varying environmental disturbance. Furthermore, a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target. It enables each ASV to adjust its forces and moments according to the received information from its neighbors. The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.

Distributed Momentum-Based Frank-Wolfe Algorithm for Stochastic Optimization

Jie Hou, Xianlin Zeng, Gang Wang, Jian Sun, Jie Chen

2023, 10(3): 685-699. doi: 10.1109/JAS.2022.105923

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This paper considers distributed stochastic optimization, in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network. Stochastic optimization problems are usually tackled by variants of projected stochastic gradient descent. However, projecting a point onto a feasible set is often expensive. The Frank-Wolfe (FW) method has well-documented merits in handling convex constraints, but existing stochastic FW algorithms are basically developed for centralized settings. In this context, the present work puts forth a distributed stochastic Frank-Wolfe solver, by judiciously combining Nesterov’s momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over networks. It is shown that the convergence rate of the proposed algorithm is ${\cal{O}}(k^{-\frac{1}{2}})$ for convex optimization, and ${\cal{O}}(1/\mathrm{log}_2(k))$ for nonconvex optimization. The efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives.

Connectivity-maintaining Consensus of Multi-agent Systems With Communication Management Based on Predictive Control Strategy

Jie Wang, Shaoyuan Li, Yuanyuan Zou

2023, 10(3): 700-710. doi: 10.1109/JAS.2023.123081

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This paper studies the connectivity-maintaining consensus of multi-agent systems. Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption, a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved. In this paper, communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus. The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller, so there is more freedom to develop an appropriate control strategy for achieving consensus. For the multi-agent systems with this novel communication management, a predictive control based strategy is developed for achieving consensus. Simulation results indicate the effectiveness and advantages of our scheme.

Axial Assembled Correspondence Network for Few-Shot Semantic Segmentation

Yu Liu, Bin Jiang, Jiaming Xu

2023, 10(3): 711-721. doi: 10.1109/JAS.2022.105863

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Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support exemplars. It remains a challenge because of large intra-class variations between the support and query images. Existing approaches utilize 4D convolutions to mine semantic correspondence between the support and query images. However, they still suffer from heavy computation, sparse correspondence, and large memory. We propose axial assembled correspondence network (AACNet) to alleviate these issues. The key point of AACNet is the proposed axial assembled 4D kernel, which constructs the basic block for semantic correspondence encoder (SCE). Furthermore, we propose the deblurring equations to provide more robust correspondence for the aforementioned SCE and design a novel fusion module to mix correspondences in a learnable manner. Experiments on PASCAL-5ⁱ reveal that our AACNet achieves a mean intersection-over-union score of 65.9 % for 1-shot segmentation and 70.6 % for 5-shot segmentation, surpassing the state-of-the-art method by 5.8 % and 5.0 % respectively.

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Da Yu, Mingyi Zhang, Mantian Li, Fusheng Zha, Junge Zhang, Lining Sun, Kaiqi Huang

2023, 10(3): 722-736. doi: 10.1109/JAS.2023.123090

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Continual learning (CL) studies the problem of learning to accumulate knowledge over time from a stream of data. A crucial challenge is that neural networks suffer from performance degradation on previously seen data, known as catastrophic forgetting, due to allowing parameter sharing. In this work, we consider a more practical online class-incremental CL setting, where the model learns new samples in an online manner and may continuously experience new classes. Moreover, prior knowledge is unavailable during training and evaluation. Existing works usually explore sample usages from a single dimension, which ignores a lot of valuable supervisory information. To better tackle the setting, we propose a novel replay-based CL method, which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge. Specifically, besides the previous raw samples, we store the corresponding logits and features in the memory. Furthermore, to imitate the prediction of the past model, we construct extra constraints by leveraging multi-level information stored in the memory. With the same number of samples for replay, our method can use more past knowledge to prevent interference. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory. We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

Group Hybrid Coordination Control of Multi-Agent Systems With Time-Delays and Additive Noises

Chuanjian Li, Xiaofeng Zong

2023, 10(3): 737-748. doi: 10.1109/JAS.2022.105917

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A new kind of group coordination control problem-group hybrid coordination control is investigated in this paper. The group hybrid coordination control means that in a whole multi-agent system (MAS) that consists of two subgroups with communications between them, agents in the two subgroups achieve consensus and containment, respectively. For MASs with both time-delays and additive noises, two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case, respectively. By developing a new analysis idea, some sufficient conditions and necessary conditions related to the communication intensity between the two subgroups are obtained for the following two types of group hybrid coordination behavior: 1) Agents in one subgroup and in another subgroup achieve weak consensus and containment, respectively; 2) Agents in one subgroup and in another subgroup achieve strong consensus and containment, respectively. It is revealed that the decay of the communication impact between the two subgroups is necessary for the consensus-oriented case. Finally, the validity of the group control results is verified by several simulation examples.

Observer-Based Path Tracking Controller Design for Autonomous Ground Vehicles With Input Saturation

Heng Wang, Tengfei Zhang, Xiaoyu Zhang, Qing Li

2023, 10(3): 749-761. doi: 10.1109/JAS.2023.123078

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This paper investigates the problem of path tracking control for autonomous ground vehicles (AGVs), where the input saturation, system nonlinearities and uncertainties are considered. Firstly, the nonlinear path tracking system is formulated as a linear parameter varying (LPV) model where the variation of vehicle velocity is taken into account. Secondly, considering the noise effects on the measurement of lateral offset and heading angle, an observer-based control strategy is proposed, and by analyzing the frequency domain characteristics of the derivative of desired heading angle, a finite frequency H_∞ index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error. Thirdly, sufficient conditions are derived to guarantee robust H_∞ performance of the path tracking system, and the calculation of observer and controller gains is converted into solving a convex optimization problem. Finally, simulation examples verify the advantages of the control method proposed in this paper.

Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control Systems: A Case Study in Level Control Process

Majid Ghorbani, Mahsan Tavakoli-Kakhki, Aleksei Tepljakov, Eduard Petlenkov

2023, 10(3): 762-780. doi: 10.1109/JAS.2022.105986

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The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this work. Interval uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world plants. Also, in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative (FOPID) controller. To the best of the authors’ knowledge, no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain, time-constants, and time delay. The three primary contributions of this study are as follows: i) a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractional-order control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system; ii) an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis; and iii) two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise reduction. Finally, four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

Policy Iteration for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

Guangyu Zhu, Xiaolu Li, Ranran Sun, Yiyuan Yang, Peng Zhang

2023, 10(3): 781-791. doi: 10.1109/JAS.2023.123096

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Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems, in this paper, a new iterative adaptive dynamic programming algorithm, which is the discrete-time time-varying policy iteration (DTTV) algorithm, is developed. The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance. The admissibility of the iterative control law is analyzed. The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution. To implement the algorithm, neural networks are employed and a new implementation structure is established, which avoids solving the generalized Bellman equation in each iteration. Finally, the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm, where the mass and pendulum bar length are permitted to be time-varying parameters. The effectiveness of the developed method is illustrated by numerical results and comparisons.

Current-Aided Multiple-AUV Cooperative Localization and Target Tracking in Anchor-Free Environments

Yichen Li, Wenbin Yu, Xinping Guan

2023, 10(3): 792-806. doi: 10.1109/JAS.2022.105989

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In anchor-free environments, where no devices with known positions are available, the error growth of autonomous underwater vehicle (AUV) localization and target tracking is unbounded due to the lack of references and the accumulated errors in inertial measurements. This paper aims to improve the localization and tracking accuracy by involving current information as extra references. We first integrate current measurements and maps with belief propagation and design a distributed current-aided message-passing scheme that theoretically solves the localization and tracking problems. Based on this scheme, we propose particle-based cooperative localization and target tracking algorithms, named CaCL and CaTT, respectively. In AUV localization, CaCL uses the current measurements to correct the predicted and transmitted position information and alleviates the impact of the accumulated errors in inertial measurements. With target tracking, the current maps are applied in CaTT to modify the position prediction of the target which is calculated through historical estimates. The effectiveness and robustness of the proposed methods are validated through various simulations by comparisons with alternative methods under different trajectories and current conditions.