A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

## Early Access

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In this paper, the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures, parameter uncertainties, and unknown time-varying boundary disturbances is addressed. The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader. Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties, the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances. Furthermore, the Lyapunov stability theory is used to demonstrate that followers’ angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded. Finally, the numerical simulation results confirm the efficacy of the proposed controllers.
, Available online  , doi: 10.1109/JAS.2023.123204
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This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs. The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees. This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree. A distributed adaptive pole placement control scheme is developed, which consists of a distributed observer and an adaptive pole placement control law. It is shown that under the proposed distributed adaptive control scheme, all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically. The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.
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With advanced communication technologies, cyber-physical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks. While lots of benefits can be achieved with such a configuration, it also brings the concern of cyber attacks to the industrial control systems, such as networked manipulators that are widely adopted in industrial automation. For such systems, a false data injection attack on a control-center-to-manipulator (CC-M) communication channel is undesirable, and has negative effects on the manufacture quality. In this paper, we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model. Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack. The efficacy of the proposed method is validated via simulations.
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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.
, Available online  , doi: 10.1109/JAS.2023.123219
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Tourism route planning is widely applied in the smart tourism field. The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails, sharp peaks and disconnected regions problems, which leads to uneven distribution and weak diversity of optimization solutions of tourism routes. Inspired by these limitations, we propose a multi-objective evolutionary algorithm for tourism route recommendation (MOTRR) with two-stage and Pareto layering based on decomposition. The method decomposes the multi-objective problem into several subproblems, and improves the distribution of solutions through a two-stage method. The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method. The neighborhood is determined according to the weight of the subproblem for crossover mutation. Finally, Pareto layering is used to improve the updating efficiency and population diversity of the solution. The two-stage method is combined with the Pareto layering structure, which not only maintains the distribution and diversity of the algorithm, but also avoids the same solutions. Compared with several classical benchmark algorithms, the experimental results demonstrate competitive advantages on five test functions, hypervolume (HV) and inverted generational distance (IGD) metrics. Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing, our proposed algorithm shows better distribution. It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity, so that the recommended routes can better meet the personalized needs of tourists.
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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.
, Available online  , doi: 10.1109/JAS.2023.123228
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Despite the big success of transfer learning techniques in anomaly detection, it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification, especially for the data with a large distribution difference. To address this challenge, a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper. First, by integrating a hypersphere adaptation constraint into domain-adversarial neural network, a new hypersphere adversarial training mechanism is designed. Second, an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible. Through transferring one-class detection rule in the adaptive extraction of domain-invariant feature representation, the end-to-end anomaly detection with one-class classification is then enhanced. Furthermore, a theoretical analysis about the model reliability, as well as the strategy of avoiding invalid and negative transfer, is provided. Experiments are conducted on two typical anomaly detection problems, i.e., image recognition detection and online early fault detection of rolling bearings. The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.
, Available online  , doi: 10.1109/JAS.2023.123246
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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 constraint 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.
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In this paper, a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph. It is of two-degree-of-freedom nature. Specifically, a robust distributed controller is designed for consensus tracking, while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances. The condition for asymptotic disturbance rejection is derived. Moreover, even when the disturbance model is not exactly known, the developed method also provides good disturbance-rejection performance. Then, a robust stabilization condition with less conservativeness is derived for the whole multi-agent system. Further, a design algorithm is given. Finally, comparisons with the conventional one-degree-of-freedom-based distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.
, Available online  , doi: 10.1109/JAS.2022.106076
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Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects remotely. For the tracking control of teleoperation systems, velocity measurements are necessary to provide feedback information. However, due to hardware technology and cost constraints, the velocity measurements are not always available. In addition, the time-varying communication delay makes it challenging to achieve tracking task. This paper provides a solution to the issue of real-time tracking for teleoperation systems, subjected to unavailable velocity signals and time-varying communication delays. In order to estimate the velocity information, immersion and invariance (I&I) technique is employed to develop an exponential stability velocity observer. For the proposed velocity observer, a linear relationship between position and observation state is constructed, through which the need of solving partial differential and certain integral equations can be avoided. Meanwhile, the mean value theorem is exploited to separate the observation error terms, and hence, all functions in our observer can be analytically expressed. With the estimated velocity information, a slave-torque feedback control law is presented. A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking conditions. In particular, the relationship between the controller design parameters and the allowable maximum delay values is provided. Finally, simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.
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Demand response (DR) using shared energy storage systems (ESSs) is an appealing method to save electricity bills for users under demand charge and time-of-use (TOU) price. A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study. In this scheme, the interactions between selfish users and an operator are characterized as a Stackelberg game. Operator holds a large-scale ESS that is shared among users in the form of energy transactions. It sells energy to users and sets the selling price first. It maximizes its profit through optimal pricing and ESS dispatching. Users purchase some energy from operator for the reduction of their demand charges after operator’s selling price is announced. This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model. The upper-level optimization maximizes operator’s profit and the lower-level optimization minimizes users’ costs. The bi-level model is transformed and linearized into a mixed-integer linear programming (MILP) model using the mathematical programming with equilibrium constraints (MPEC) method and model linearizing techniques. Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.
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Fidelity plays an important role in quantum information processing, which provides a basic scale for comparing two quantum states. At present, one of the most commonly used fidelities is Uhlmann-Jozsa (U-J) fidelity. However, U-J fidelity needs to calculate the square root of the matrix, which is not trivial in the case of large or infinite density matrices. Moreover, U-J fidelity is a measure of overlap, which has limitations in some cases and cannot reflect the similarity between quantum states well. Therefore, a novel quantum fidelity measure called quantum Tanimoto coefficient (QTC) fidelity is proposed in this paper. Unlike other existing fidelities, QTC fidelity not only considers the overlap between quantum states, but also takes into account the separation between quantum states for the first time, which leads to a better performance of measure. Specifically, we discuss the properties of the proposed QTC fidelity. QTC fidelity is compared with some existing fidelities through specific examples, which reflects the effectiveness and advantages of QTC fidelity. In addition, based on the QTC fidelity, three discrimination coefficients ${\boldsymbol{d_1^{{\bf{QTC}}} }}$, ${\boldsymbol{d_2^{{\bf{QTC}}}}}$, and ${\boldsymbol{d_3^{{\bf{QTC}}}}}$ are defined to measure the difference between quantum states. It is proved that the discrimination coefficient ${\boldsymbol{d_3^{{\bf{QTC}}} }}$ is a true metric. Finally, we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.
, Available online  , doi: 10.1109/JAS.2022.106004
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Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces learning performance in the target domain, and has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to address this issue. However, there does not exist a systematic survey. This paper fills this gap, by first introducing the definition of NT and its causes, and reviewing over fifty representative approaches for overcoming NT, which fall into three categories: domain similarity estimation, safe transfer, and NT mitigation. Many areas, including computer vision, bioinformatics, natural language processing, recommender systems, and robotics, that use NT mitigation strategies to facilitate positive transfers, are also reviewed. Finally, we give guidelines on NT task construction and baseline algorithms, benchmark existing TL and NT mitigation approaches on three NT-specific datasets, and point out challenges and future research directions. To ensure reproducibility, our code is publicized at https://github.com/chamwen/NT-Benchmark.
, Available online  , doi: 10.1109/JAS.2022.106064
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In this paper, we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients, which mainly includes the following steps. Firstly, by introducing several key transformation functions and selecting the initial value of the time-varying scaling function, the symmetric prescribed performance with global and semi-global properties can be handled uniformly, without the need for control re-design. Secondly, to handle the problem of unknown time-varying control coefficient with an unknown sign, we propose an enhanced Nussbaum function (ENF) bearing some unique properties and characteristics, with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer required. Thirdly, by utilizing the core-function information technique, the nonparametric uncertainties in the system are gracefully handled so that no approximator is required. Furthermore, simulation results verify the effectiveness and benefits of the approach.
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The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven event-triggerednetwork system (ETS) in cutting off data transmissions and the co-design procedure.
, Available online  , doi: 10.1109/JAS.2022.106055
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With the rapid increase of high-dimensional data mixed with labelled and unlabelled samples, the semi-supervised feature selection technique has received much attention in recent years. However, most existing approaches ignore the fuzziness of the data. Moreover, many feature selection methods need to measure the relationships among all samples, which is inefficient and difficult to be applied to large-scale data. To address the problems mentioned above, we propose an effective semi-supervised feature selection with the soft label learning (SFS-SLL) method in this paper. Specifically, we first learn initial soft labels based on the local distance between samples and clustering centers using an efficient fuzzy C-means clustering. We propose a supervised semantic constraint to exploit labelled and unlabelled data using manual labels as the soft label learning guidance. Then, we propose a simple yet effective sparse regression model which integrates soft label learning and feature selection into a unified framework. Finally, we derive an effective optimization strategy based on the alternating direction method of multipliers (ADMM) to iteratively solve the formulated problem. Experiment results on several benchmark datasets show a performance improvement on feature selection accuracy and efficiency over compared methods.
, Available online  , doi: 10.1109/JAS.2022.106061
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Three-way decision (T-WD) theory is about thinking, problem solving, and computing in threes. Behavioral decision making (BDM) focuses on effective, cognitive, and social processes employed by humans for choosing the optimal object, of which prospect theory and regret theory are two widely used tools. The hesitant fuzzy set (HFS) captures a series of uncertainties when it is difficult to specify precise fuzzy membership grades. Guided by the principles of three-way decisions as thinking in threes and integrating these three topics together, this paper reviews and examines advances in three-way behavioral decision making (TW-BDM) with hesitant fuzzy information systems (HFIS) from the perspective of the past, present, and future. First, we provide a brief historical account of the three topics and present basic formulations. Second, we summarize the latest development trends and examine a number of basic issues, such as one-sidedness of reference points and subjective randomness for result values, and then report the results of a comparative analysis of existing methods. Finally, we point out key challenges and future research directions.
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For autonomous vehicles and driver assist systems, path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers. In the literature, the algorithms that provide driver intent belong to two categories: those that use physics based models with some type of filtering, and machine learning based approaches. In this paper we employ barrier functions (BF) to decide driver intent. BFs are typically used to prove safety by establishing forward invariance of an admissible set. Here, we decide if the “target” vehicle is violating one or more possibly fictitious (i.e., non-physical) barrier constraints determined based on the context provided by the road geometry. The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives. The predicted intent is then used by a control barrier function (CBF) based collision avoidance system to prevent unnecessary interventions, for either an autonomous or human-driven vehicle.
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In this paper, we investigate the peaking issue of extended state observers and the anti-disturbance control problem of tethered aircraft systems subject to the unstable flight of the main aircraft, airflow disturbances and deferred output constraints. Independent of exact initial values, a modified extended state observer is constructed from a shifting function such that not only the peaking issue inherently in the observer is circumvented completely but also the accurate estimation of the lumped disturbance is guaranteed. Meanwhile, to deal with deferred output constraints, an improved output constrained controller is employed by integrating the shifting function into the barrier Lyapunov function. Then, by combining the modified observer and the improved controller, an anti-disturbance control scheme is presented, which ensures that the outputs with any bounded initial conditions satisfy the constraints after a pre-specified finite time, and the tethered aircraft tracks the desired trajectory accurately. Finally, both a theoretical proof and simulation results verify the effectiveness of the proposed control scheme.
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, Available online  , doi: 10.1109/JAS.2022.106046
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, Available online  , doi: 10.1109/JAS.2022.106058
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To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
, Available online  , doi: 10.1109/JAS.2022.106031
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, Available online  , 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.
, Available online  , doi: 10.1109/JAS.2022.106067
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, Available online  , 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.
, Available online  , doi: 10.1109/JAS.2022.106028
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, Available online  , doi: 10.1109/JAS.2022.105941
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Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
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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.
, Available online  , doi: 10.1109/JAS.2022.106019
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, Available online  , doi: 10.1109/JAS.2022.106025
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, Available online  , doi: 10.1109/JAS.2022.106037
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, Available online  , 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.
, Available online  , doi: 10.1109/JAS.2022.106043
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, Available online  , doi: 10.1109/JAS.2022.106073
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, Available online  , doi: 10.1109/JAS.2022.106085
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, Available online  , doi: 10.1109/JAS.2022.105995
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, Available online  , doi: 10.1109/JAS.2022.106007
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This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle (multi-USV) system. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, a multi-USV target stalking (MUTS) algorithm is proposed. Firstly, a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG algorithm. The advantages of the proposed method are twofold: 1) it can reduce the amount of data and shorten training time; 2) it can filter out more important data in the experience buffer for training. Secondly, in order to avoid the collisions of USVs during the stalking process, an action constraint method called Safe DDPG is introduced. Finally, the MUTS algorithm and some existing algorithms are compared in cooperative target stalking scenarios. In order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks, mission operating scenarios and reward functions are well designed in this paper. The proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission execution. Moreover, compared with some existing algorithms, the newly proposed one can provide a higher convergence speed and a narrower convergence domain.
, Available online  , doi: 10.1109/JAS.2022.106010
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, Available online  , 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.
, Available online  , doi: 10.1109/JAS.2022.105857
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This paper is concerned with distributed Nash equilibrium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization of players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized information flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respectively. Through Lyapunov stability analysis, it is shown that players’ actions can be steered to a neighborhood of the Nash equilibrium with logarithmic and uniform quantizers, and the quantified convergence error depends on the parameter of the quantizer for both undirected and directed cases. A numerical example is given to verify the theoretical results.
, Available online  , doi: 10.1109/JAS.2022.105929
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Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state responses. This paper presents a deep observation on and a comparison between two of those methods: the generalized extended-state observer (GESO) and the equivalent input disturbance (EID) from assumptions, system configurations, stability conditions, system design, disturbance-rejection performance, and extensibility. A time-domain index is introduced to assess the disturbance-rejection performance. A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method. A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.
, Available online  , doi: 10.1109/JAS.2022.106001
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, Available online  , doi: 10.1109/JAS.2022.106034
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, Available online  , doi: 10.1109/JAS.2022.105980
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Deep matrix factorization (DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data. However, existing multi-view DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a hypergraph regularized diverse deep matrix factorization (HDDMF) model for multi-view data representation, to jointly utilize multi-view diversity and a high-order manifold in a multi-layer factorization framework. A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data. Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view. An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis. Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms state-of-the-art multi-view learning approaches.
, Available online  , doi: 10.1109/JAS.2022.105938
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In this paper, a disturbance observer-based safe tracking control scheme is proposed for a medium-scale unmanned helicopter with rotor flapping dynamics in the presence of partial state constraints and unknown external disturbances. A safety protection algorithm is proposed to keep the constrained states within the given safe-set. A second-order disturbance observer technique is utilized to estimate the external disturbances. It is shown that the desired tracking performance of the controlled unmanned helicopter can be achieved with the application of the backstepping approach, dynamic surface control technique, and Lyapunov method. Finally, the availability of the proposed control scheme has been shown by simulation results.
, Available online  , doi: 10.1109/JAS.2022.105992
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This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation. Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning (RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
, Available online  , doi: 10.1109/JAS.2022.105845
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Connected automated vehicles (CAVs) serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety, and reducing fuel consumption and vehicle emissions. A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads. This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service (DoS) attacks that disrupt vehicle-to-vehicle communications. First, a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties, including diverse vehicle masses and engine inertial delays, unknown and nonlinear resistance forces, and a dynamic platoon leader. Then, a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability, attack resilience, platoon safety and scalability. Furthermore, a numerically efficient offline design algorithm for determining the desired platoon control law is developed, under which the platoon resilience against DoS attacks can be maximized but the anticipated stability, safety and scalability requirements remain preserved. Finally, extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.
, Available online  , doi: 10.1109/JAS.2022.105983
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This paper explores the problem of distributed Nash equilibrium seeking in games, where players have limited knowledge on other players’ actions. In particular, the involved players are considered to be high-order integrators with their control inputs constrained within a pre-specified region. A linear transformation for players’ dynamics is firstly utilized to facilitate the design of bounded control inputs incorporating multiple saturation functions. By introducing consensus protocols with adaptive and time-varying gains, the unknown actions for players are distributively estimated. Then, a fully distributed Nash equilibrium seeking strategy is exploited, showcasing its remarkable properties: 1) ensuring the boundedness of control inputs; 2) avoiding any global information/parameters; and 3) allowing the graph to be directed. Based on Lyapunov stability analysis, it is theoretically proved that the proposed distributed control strategy can lead all the players’ actions to the Nash equilibrium. Finally, an illustrative example is given to validate effectiveness of the proposed method.
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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.
, Available online  , doi: 10.1109/JAS.2022.105665
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The attitude tracking operations of an on-orbit spacecraft with degraded performance exhibited by potential actuator uncertainties (including failures and misalignments) can be extraordinarily challenging. Thus, the control law development for the attitude tracking task of spacecraft subject to actuator (namely reaction wheel) uncertainties is addressed in this paper. More specially, the attitude dynamics model of the spacecraft is firstly established under actuator failures and misalignment (without a small angle approximation operation). Then, a new non-singular sliding manifold with fixed time convergence and anti-unwinding properties is proposed, and an adaptive sliding mode control (SMC) strategy is introduced to handle actuator uncertainties, model uncertainties and external disturbances simultaneously. Among this, an explicit misalignment angles range that could be treated herein is offered. Lyapunov-based stability analyses are employed to verify that the reaching phase of the sliding manifold is completed in finite time, and the attitude tracking errors are ensured to converge to a small region of the closest equilibrium point in fixed time once the sliding manifold enters the reaching phase. Finally, the beneficial features of the designed controller are manifested via detailed numerical simulation tests.
, Available online  , 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.
, Available online  , doi: 10.1109/JAS.2022.105437
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During the last three decades, evolutionary algorithms (EAs) have shown superiority in solving complex optimization problems, especially those with multiple objectives and non-differentiable landscapes. However, due to the stochastic search strategies, the performance of most EAs deteriorates drastically when handling a large number of decision variables. To tackle the curse of dimensionality, this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions. The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution, and provides a fast clustering method to highly reduce the dimensionality of the search space. More importantly, all the operations related to the decision variables only contain several matrix calculations, which can be directly accelerated by GPUs. While existing EAs are capable of handling fewer than 10 000 real variables, the proposed algorithm is verified to be effective in handling 1 000 000 real variables. Furthermore, since the proposed algorithm handles the large number of variables via accelerated matrix calculations, its runtime can be reduced to less than 10% of the runtimes of existing EAs.
, Available online  , doi: 10.1109/JAS.2022.105530
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High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.
, Available online  , doi: 10.1109/JAS.2022.105926
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A critical component of visual simultaneous localization and mapping is loop closure detection (LCD), an operation judging whether a robot has come to a pre-visited area. Concretely, given a query image (i.e., the latest view observed by the robot), it proceeds by first exploring images with similar semantic information, followed by solving the relative relationship between candidate pairs in the 3D space. In this work, a novel appearance-based LCD system is proposed. Specifically, candidate frame selection is conducted via the combination of Super-features and aggregated selective match kernel (ASMK). We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task. It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance. To dig up consistent geometry between image pairs during loop closure verification, we propose a simple yet surprisingly effective feature matching algorithm, termed locality preserving matching with global consensus (LPM-GC). The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs, where a global constraint is further designed to effectively remove false correspondences in challenging sceneries, e.g., containing numerous repetitive structures. Meanwhile, we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds. The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets. Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks. We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.
, Available online  , doi: 10.1109/JAS.2022.105428
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This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players’ objective functions. Through Lyapunov stability analysis, it is proven that the players’ actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.
, Available online  , doi: 10.1109/JAS.2022.105998
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, Available online  , doi: 10.1109/JAS.2022.105521
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Weighted vertex cover (WVC) is one of the most important combinatorial optimization problems. In this paper, we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks. We first model the WVC problem as a general game on weighted networks. Under the framework of a game, we newly define several cover states to describe the WVC problem. Moreover, we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums (SNEs) of the game. Then, we propose a game-based asynchronous algorithm (GAA), which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time. Subsequently, we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms, termed the improved game-based asynchronous algorithm (IGAA), in which we prove that it can obtain a better solution to the WVC problem than using a the GAA. Finally, numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
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This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.
, Available online  , 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.
, Available online  , doi: 10.1109/JAS.2022.106088
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