With respect to the accurate tracking control for unmanned surface vehicles (USVs), this paper proposes a novel hierarchical control structure consisting of a velocity planner and a tracking controller. With full consideration of control accuracy and tracking safety, a model predictive control planning method based on dynamic artificial potential field method (DAPF-MPC) is proposed, aiming to generate the current optimal reference tracking velocity and achieve online obstacle avoidance guidance. Simultaneously, a fixed-time generalized super-twisting controller based on an extended state observer (ESO-FiTGST) is proposed to restrict the convergence time and tackle with challenge resulting from the uncertain disturbances and model parameters. Noticeably, the design process of the proposed control law and its rigorous stability analysis, especially for the computable fixed-time convergence property, are detailed. Ultimately, the simulation and experimental results demonstrate the superiority and feasibility of the proposed method, providing a reliable and effective reference solution for tracking control tasks of USVs in aquatic scenarios with complex obstacles.

In this article, the multisensor particle filtering problem is investigated for a class of nonlinear complex networks with multirate heterogeneous measurements. The underlying complex networks are subject to non-Gaussian noises and randomly switching couplings, while the multirate heterogeneous measurements (including fast-rate binary measurements and slow-rate integral measurements) are transmitted to remote filters via imperfect wireless communication channels. Both the deterministic and stochastic channel gains, along with possible transmission failures, are taken into account to characterize the properties of wireless communication channels. The purpose of this article is to propose a channel-related filtering scheme in the particle filtering framework to address these engineering-oriented complexities. To achieve this, a mixture distribution is established to reflect the effects of randomly switching couplings and generate new particle candidates. By utilizing the Monte Carlo approximation method, two types of update expressions for importance weights are explicitly derived based on the channel properties and the likelihood functions. Finally, numerical simulations are presented to demonstrate the viability and effectiveness of the proposed particle filtering algorithms.

Vector fields are advantageous in handling non holonomic motion planning, as they provide the robot with reference orientation across the workspace. However, additionally incorporating curvature constraints presents challenges due to the interconnection between the design of the curvature-bounded vector field and the tracking controller under limited actuation. In this paper, we present a novel framework to co-develop the vector field and the control law, guiding the nonholonomic robot to the target configuration with curvature-bounded trajectory. First, we formulate the problem by introducing the target positive limit set, which allows the robot to either converge to or pass through the target configuration, depending on its dynamics and the specific tasks. Next, we construct a curvature-constrained vector field (CVF) via blending and embedding elementary flows in the workspace. To track such CVF, a saturated control law with dynamic gains is proposed, under which the tracking error's magnitude decreases even when saturation occurs. Under the control law, the kinematically constrained nonholonomic robot is guaranteed to track the reference CVF and converge to the target positive limit set with bounded trajectory curvature. Numerical simulations show that the proposed CVF method outperforms other vector-field-based algorithms. Experiments on Ackermann UGVs and semi-physical fixed-wing UAVs demonstrate that the method can be effectively implemented in real-world scenarios.

Multi-agent trajectory planning requires ensuring both safety and efficiency, yet deadlocks remain a significant challenge, especially in obstacle-dense environments. To address this, we propose a novel distributed trajectory planning framework that bridges the gap between global path and local trajectory cooperation. At the global level, a homotopy-aware optimal path planning algorithm is proposed, which fully leverages the topological structure of the environment. A reference path is chosen from distinct homotopy classes by considering both its spatial and temporal properties, leading to improved coordination among agents globally. At the local level, a model predictive control-based trajectory optimization method is used to generate dynamically feasible and collision-free trajectories. Additionally, an online replanning strategy ensures its adaptability to changing environments. Simulations and experiments validate the effectiveness of our approach in mitigating deadlocks. Ablation studies demonstrate that by incorporating time-aware homotopic properties into the underlying global paths, our method can significantly reduce deadlocks and improve the average success rate from 4%-13% to over 90% in randomly generated dense scenarios.

Online coordination of multi-robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)-based approaches for scene reasoning and planning primarily focus on one-shot, end-to-end solutions in known environments, lacking both dynamic adaptation capabilities for online operation and explainability in the processes of planning. To address these issues, a novel framework (DEXTER-LLM) for dynamic task planning in unknown environments, integrates four modules: (i) a mission comprehension module that resolves partial ordering of tasks specified by natural languages or linear temporal logic formulas (LTL); (ii) an online subtask generator based on LLMs that improves the accuracy and explainability of task decomposition via multi-stage reasoning; (iii) an optimal subtask assigner and scheduler that allocates subtasks to robots via search-based optimization; and (iv) a dynamic adaptation and human-in-the-loop verification module that implements multi-rate, event-based updates for both subtasks and their assignments, to cope with new features and tasks detected online. The framework effectively combines LLMs’ open-world reasoning capabilities with the optimality of model-based assignment methods, simultaneously addressing the critical issue of online adaptability and explainability. Experimental evaluations demonstrate exceptional performances, with 100% success rates across all scenarios, 160 tasks and 480 subtasks completed on average (3 times the baselines), 62% less queries to LLMs during adaptation, and superior plan quality (2 times higher) for compound tasks. Project page at https://tcxm.github.io/DEXTER-LLM/.

This study addresses the problem of attitude tracking and vibration suppression for 3D flexible spacecraft subject to external disturbances and input quantization, which are two critical factors that can significantly degrade control performance in harsh space environments with limited communication capacity. Using Hamilton’s principle, the spacecraft is modeled by coupled ordinary and partial differential equations to accurately characterize its infinite-dimensional dynamics. Nonlinear observers are constructed to exactly estimate unknown boundary disturbances. To deal with the difficulty caused by quantization, a linear time-varying model is introduced to describe hysteretic quantizers, and adaptive laws incorporating exponential functions are developed to estimate resulting unknown terms. Furthermore, a novel disturbance observer-based adaptive quantized control scheme is proposed, which guarantees the boundedness of all closed-loop signals and ensures that both attitude tracking errors and vibrations converge exponentially to zero, whereas existing methods typically achieve only convergence to small residual sets due to quantization effects or disturbances. In particular, the proposed scheme allows the quantizer parameters to be freely adjusted during operation for balancing communication burden and tracking performance. Simulation results show that, compared to traditional PD control, the proposed scheme exhibits faster convergence in attitude tracking and vibration elimination, and achieves higher control accuracy under unknown disturbances and changeable quantizer parameters.

To plan the trajectories of a large-scale heterogeneous swarm, sequentially or synchronously distributed methods usually become intractable due to the lack of global clock synchronization. To this end, we provide a novel asynchronous spatial-temporal allocation method. Specifically, between a pair of agents, the allocation is proposed to determine their corresponding derivable time-stamped space and can be updated in an asynchronous way, by inserting a waiting duration between two consecutive replanning steps. It is theoretically shown that the inter-agent collision is avoided and the allocation ensures timely updates. Comprehensive simulations and comparisons with state-of-the-art baselines validate the effectiveness of the proposed method and illustrate its improvement in completion time and moving distance. Finally, hardware experiments are carried out, where 8 heterogeneous unmanned ground vehicles with onboard computation navigate in cluttered scenarios with high agility.

In this brief, the state estimation problem is investigated for a class of randomly delayed artificial neural networks (ANNs) subject to probabilistic saturation constraints (PSCs) and non-Gaussian noises under the redundant communication channels. A series of mutually independent Bernoulli distributed white sequences are introduced to govern the random occurrence of the time delays, the saturation constraints, and the transmission channel failures. A comprehensive redundant-channel-based communication mechanism is constructed to attenuate the phenomenon of packet dropouts so as to enhance the quality of data transmission. To compensate for the influence of randomly occurring time delays, the corresponding occurrence probability is exploited in the process of particle generation. In addition, an explicit expression of the likelihood function is established based on the statistical information to account for the impact of PSCs and redundant channels. By virtue of the modified operations of particle propagation and weight update, a particle-filter-based state estimation algorithm is proposed with mild restriction on the system type. Finally, an illustrative example with Monte Carlo simulations is provided to demonstrate the effectiveness of the developed state estimation scheme.

This article is concerned with the maximum correntropy filtering (MCF) problem for a class of nonlinear complex networks subject to non-Gaussian noises and uncertain dynamical bias. With aim to utilize the constrained network bandwidth and energy resources in an efficient way, a componentwise dynamic event-triggered transmission (DETT) protocol is adopted to ensure that each sensor component independently determines the time instant for transmitting data according to the individual triggering condition. The principal purpose of the addressed problem is to put forward a dynamic event-triggered recursive filtering scheme under the maximum correntropy criterion, such that the effects of the non-Gaussian noises can be attenuated. In doing so, a novel correntropy-based performance index (CBPI) is first proposed to reflect the impacts from the componentwise DETT mechanism, the system nonlinearity, and the uncertain dynamical bias. The CBPI is parameterized by deriving upper bounds on the one-step prediction error covariance and the equivalent noise covariance. Subsequently, the filter gain matrix is designed by means of maximizing the proposed CBPI. Finally, an illustrative example is provided to substantiate the feasibility and effectiveness of the developed MCF scheme.

In our paper[1], the last inequality of[1, eq. (20)]is not correct, which affects the proof of the converge of$z_{I}$. In the following, we will give a corrected proof.

This article investigates the robustness issues of a set of distributed optimization algorithms, which aim to approach the optimal solution to a sum of local cost functions over an uncertain network. The uncertain communication network consists of transmission channels perturbed by additive deterministic uncertainties, which can describe quantization and transmission errors. A new robust initialization-free algorithm is proposed for the distributed optimization problem of multiple Euler-Lagrange systems, and the explicit relationship of the feedback gain of the algorithm, the communication topology, the properties of the cost function, and the radius of the channel uncertainties is established in order to reach the optimal solution. This result provides a sufficient condition for the selection of the feedback gain when the uncertainty size is less than the unity. As a special case, we discuss the impact of communication uncertainties on the distributed optimization algorithms for first-order integrator networks.

In this article, the distributed finite-time and fixed-time optimization problems are investigated by adopting the zero-gradient-sum (ZGS) framework in multiagent systems. Specifically, when the local convex functions are nonquadratic, a basic optimization protocol is proposed to obtain a finite-time convergence, such that the networked system can cooperatively seek the optimal solution of the global objective, the sum of local objective, in a limited time. By utilizing the property of quadratic functions, a reduced algorithm can remove the dependence of initial conditions in the estimation of the upper bound of settling time and achieve a fixed-time result. Besides, the problem with time-varying topologies is studied by introducing a modified algorithm with an artificial potential function to preserve the network connectivity. Finally, the validity of the protocols is demonstrated via some example simulations.

This paper considers the formation control problem for general linear networked agents constrained with event-triggered communications. We propose four kinds of edge-based event-triggered protocols, each of which can be used to achieve given formation structures and eliminate the unexpected Zeno behavior. Since the whole protocols are designed according to sampled information at event instants rather than real-time information, these protocols efficiently avoid continuous communications, reduce the bandwidth need of communication, and decrease the energy consuming. The distributed static state feedback edge-based event-triggered protocol or the adaptive one is applicable for the occasions with available states. Different from the state feedback protocols, users can choose the distributed static output feedback edge-based event-triggered protocol or the adaptive one no matter whether agents' states are available or not. It is worth emphasizing that the adaptive state (or output) feedback event-triggered protocol requires no global information of the network topology and can be used in a fully distributed and scalable manner. Finally, numerical examples on formation control are offered to testify the feasibility of the proposed protocols.

This paper investigates the cooperative tracking control problem of multiagent systems where each agent is described as a Lur'e system. We first consider the case where the leader's control input is zero and design a distributed adaptive event-triggered protocol for the followers to track the leader. We then deal with the general case where the leader contains a bounded control input by proposing a novel event-based protocol, including a nonlinear term to restrain the effect of the leader's input. Both of the proposed event-triggered protocols can guarantee the uniform ultimate boundedness of the tracking error and the adaptive gains without the requirement of any global information of the network. The relationship between the upper bound of the tracking error and the controller parameters is explicitly derived.

This paper is concerned with general coopetition networks with signed graphs, based on which both the bipartite consensus and tracking control problems for networked systems subject to nonidentical matching uncertainties are studied. For the case of undirected and connected communication graphs, we propose a distributed discontinuous nonlinear controller which can achieve the bipartite consensus. To cancel the chattering phenomenon of the discontinuous controller, a continuous one is designed by using the boundary layer technique, under which the bipartite consensus error is shown to be uniformly ultimately bounded and can exponentially converge to a small adjustable bounded set. Further, considering the case of a leader having a bounded control action, we present a continuous controller to guarantee the ultimate boundedness of the bipartite tracking error.

This paper considers the consensus control problem of heterogeneous linear multiagent systems. A distributed proportional-integral (PI) protocol is presented to ensure consensus of heterogeneous linear multiagent systems with directed communication graphs. Sufficient conditions for the choice of control parameters are derived. For the case that the agents are subject to the external time-varying but bounded disturbances, the proposed distributed PI protocol can assure uniformly ultimate boundedness of the consensus error. The effectiveness of the results is illustrated both theoretically and numerically.

This paper investigates the distributed continuous-time optimization problem, which consists of a group of agents with variant local cost functions. An adaptive consensus-based algorithm with event triggering communications is introduced, which can drive the participating agents to minimize the global cost function and exclude the Zeno behavior. Compared to the existing results, the proposed event-based algorithm is independent of the parameters of the cost functions, using only the relative information of neighboring agents, and hence is fully distributed. Furthermore, the constraints of the convexity of the cost functions are relaxed.

This paper investigates the distributed event-based consensus problem of switching networks satisfying the jointly connected condition. Both the state consensus of homogeneous linear networks and the output consensus of heterogeneous networks are studied. Two kinds of event-based protocols based on local sampled information are designed, without the need to solve any matrix equation or inequality. Theoretical analysis indicates that the proposed event-based protocols guarantee the achievement of consensus and the exclusion of Zeno behaviors for jointly connected undirected switching graphs. These protocols, relying on no global knowledge of the network topology and independent of switching rules, can be devised and utilized in a completely distributed manner. They are able to avoid continuous information exchanges for either controllers' updating or triggering functions' monitoring, which ensures the feasibility of the presented protocols.

This paper considers the distributed event-triggered consensus control problem for a network of linear systems subject to bounded uncertainties satisfying the matching condition. Due to the existence of nonidentical uncertainties, the multiagent system studied in this paper is essentially heterogeneous, and the event-triggered consensus problem of which is much more challenging than that of homogeneous linear networks in the existing works. We propose a static nonsmooth event-triggered protocol that includes a nonlinear term to ensure that consensus is achieved and the Zeno behavior is excluded. To avoid the undesirable chattering effect caused by the nonsmooth protocol, we design a static continuous event-based protocol, which can guarantee that the consensus error is ultimately bounded and the upper bound of the consensus error can be made satisfactorily small by choosing properly the design parameters. We also design a continuous adaptive event-triggered protocol that includes time-varying weights into both the control law and the triggering function. Contrary to the event-triggered protocols in the previous related works, the adaptive event-based protocol is fully distributed and scalable, whose design does not require any global information of the network graph. Besides, all the event-triggered protocols in this paper do not need continuous communications among neighboring agents in either control laws' updating or triggering conditions' monitoring.

This paper considers the distributed output consensus problem of heterogeneous linear multi-agent networks with event-triggered communications. To handle the heterogeneity existing in the network, we first design adaptive event-based dynamic compensators for the agents. Distributed output feedback protocols, consisting of the dynamic compensators and the distributed state observers, the local controllers, and the triggering functions, are then designed to ensure that output consensus is achieved. Compared to the previous related works, our main contribution is that we design a fully distributed and scalable adaptive event-based protocol, whose design does not rely on any global information of the network graph and is independent of the network's scale. Continuous communications are not required for either control laws updating or triggering functions monitoring.

This paper is concerned with the distributed consensus tracking problem of uncertain multiagent systems with directed communication topology and a single high-dimensional leader. Compared with existing related works, the dynamics of each follower in the present framework are subject to unmodeled dynamics and unknown external disturbances, which is more practical in various applications. Furthermore, the dimensions of leader's dynamics may be different with those of the followers' dynamics. Under the mild assumption that each follower can directly or indirectly sense the output information of the leader, a distributed robust adaptive neural network controller together with a local observer are designed to each follower to ensure that the states of each follower ultimately synchronize to the leader's output with bounded residual errors under a fixed topology. By appropriately constructing some multiple Lyapunov functions, the derived results are further extended to consensus tracking with switching directed communication topologies. The effectiveness of the analytical results is demonstrated via numerical simulations.

In this paper, the adaptive consensus disturbance rejection problem is considered for the liner multi-agent systems under directed graphs. Based on the relative state information of the neighboring agents, the consensus protocols, including a state observer and a disturbance observer, are designed to guarantee that the consensus error goes to zero with the complete disturbance rejection. Furthermore, the state observer is designed in a fully distributed fashion with adaptive coupling gain, which has the advantage that the consensus protocol design is independent of the Laplacian matrix associated with the communication network. Finally, an example is given to verify the effectiveness of the theoretical results.

This paper studies a class of fast consensus algorithms for a group of identical multiagent systems each described by the linear state-space model. By using both the current and delayed state information, the proposed delay-induced consensus algorithm is shown to achieve synchronization with a faster convergence speed than the standard one when the eigenvalues of the open-loop system, control parameters, the Laplacian matrix of the network, and the delay satisfy certain conditions. In addition, some sufficient or necessary and sufficient conditions are established to guarantee the closed-loop stability of the delay-induced consensus algorithm, where an extra control parameter on the coupling strength is introduced to adjust the convergence speed of the closed-loop system flexibly. We then show that the delay-induced algorithm is robust to the small intrinsic communication or input delays, i.e., the proposed delay-induced consensus algorithm may also produce a faster convergence speed than the standard one even if there exist small intrinsic communication or input delays. Furthermore, we extend the results from the case of an undirected communication topology to those of a directed communication topology and a switching communication topology. Several simulation examples are presented to illustrate the theoretical results.

This paper addresses the coordinated tracking control problem for multi-agent systems with general linear dynamics and a leader whose control input might be nonzero and not available to any follower. Based on the relative states of neighboring agents, two distributed continuous controllers with, respectively, static and adaptive coupling gains, are designed, under which the tracking error of each follower is uniformly ultimately bounded, if the communication graph among the followers is undirected, the leader has directed paths to all followers, and the leader's control input is bounded. A sufficient condition for the existence of the distributed controllers is that each agent is stabilizable.

This paper addresses the distributed consensus tracking problem for a class of multi-agent systems with Lipschitz-type node dynamics in the presence of a single leader. The main contribution in the present work is to solve the consensus tracking problem without the over-idealized assumption that the communication topology among dynamic agents is strongly connected and fixed. A distributed protocol based only on the relative states between neighboring agents is designed. Then, by using tools from nonnegative matrix analysis and switching systems theory, it is theoretically shown that consensus tracking in a closed-loop multi-agent network with a switching directed topology can be achieved if there always exists a directed path from the leader to each follower, with the control parameters suitably selected.