This article studies the event-triggered prescribed-time distributed observer network design problem of linear time-invariant systems subject to unknown input (UI), where each observer node has access only to partial output information of the target system. The issue of the prescribed-time distributed event-triggered state estimation has not been adequately addressed. To this end, a novel prescribed-time distributed unknown input observer (PTDUIO), featuring the periodic delayed terms and prescribed-time coupling gain, is proposed under the scenario of continuous-time communication. It is analytically proved that the constructed PTDUIO enables each observer node to reconstruct the target system's states in the prescribed settling time regardless of initial conditions, with completely eliminating the influence of UI. To reduce the communication frequency among neighboring nodes, an improved event-triggered mechanism with the triggering condition related to the prescribed-time function is designed. Such a design facilitates the simultaneous realization of prescribed-time convergence and event-triggered communication. In addition, the sufficient criterion is derived to achieve the practical prescribed-time distributed state estimation in the event-triggered communication fashion, and the Zeno behavior is excluded. Finally, numerical examples are provided to demonstrate the effectiveness and superiority of the designed PTDUIO.

Multivehicle cooperative lane-change (MVCLC) is a challenging problem in multivehicle trajectory planning, as it involves complex coordination and collision avoidance among vehicles in dynamic environments. While optimal control-based methods have demonstrated promising performance, their high computational complexity often limits practical deployment. To address this, we propose a novel imitation learning (IL) framework for MVCLC trajectory planning, which integrates optimal control-based trajectory generation with neural network (NN) training for efficient real-time inference. Specifically, we first formulate an optimal control framework for MVCLC and employ numerical optimization to generate high-quality reference trajectories. These trajectories are then used to construct a diverse dataset for training two NNs, enabling fast trajectory generation via IL. To ensure the safety and feasibility of the generated trajectories, we introduce a safety monitoring module that guarantees collision-free execution. Extensive simulation results demonstrate that the proposed method achieves near-optimal performance with significantly improved computational efficiency—achieving a reduction in planning time by 97.1% compared to traditional methods.

This paper addresses the problem of circular formation control for nonholonomic vehicles in communication-free and GPS-free mode under obstacle occlusion with relative measurement. Firstly, an attractive vector is constructed based on relative position between vehicles and target. The constructed vector, combined with the angular deviation of the trajectory’s circular center associated with nonholonomic vehicles, is used to design circular formation controllers. The collisions during the formation process are effectively prevented by the control laws. Subsequently, a more applicable circular formation control law ensuring collision avoidance is proposed for a scenario with onboard sensor occlusion. The proposed control laws eliminate the need for the uniform angular spacing between vehicles and are completely independent of the number nonholonomic vehicles. Finally, the validation of the designed controllers are carried out through both numerical simulations and physical experiments.

As a critical security threat, Byzantine attacks in nodes sending tampered data to their neighbors in networks, significantly undermining the robustness of networked systems. This paper focuses on the leader-follower resilient consensus of continuous-time heterogeneous multiagent systems against Byzantine attacks on nodes. Considering the excessive communication burden in practice, a distributed dynamic event-triggered mechanism is devised to enable intermittent communication among agents. Building upon this mechanism, two protocols are applied to achieve the output consensus when the leader converges to dynamic and static states, respectively. The latter protocol can decrease both communication data and the frequency of event triggers when the leader converges to a static state. In addition, the output regulation control for uncertain system dynamics is achieved by utilizing the technique of internal model principle. Moreover, the designed event-triggered mechanism can be employed to the networks with bounded communication delays. Finally, the effectiveness of the designed protocols is demonstrated by some numerical simulations.

This article addresses the resilient consensus problem of multiagent systems subject to cyber attacks on communication links, where the attacks on different links may collude to maintain undetectable. For the case with noncollusive attacks on links, a distributed fixed-time observer is designed so that the attack on each link can be detected by the two associated agents. A necessary and sufficient condition is derived to ensure the isolation of attacked links and no mistaken isolation of normal ones. For the case with collusive attacks on links, a novel attack isolation algorithm is proposed by constructing extra observers on the basis of the previous designed distributed fixed-time observer via sequentially removing the information associated with one of the links. Based on the isolation of the attacked links, a control algorithm is designed, and a necessary and sufficient condition is provided to achieve resilient consensus. Numerical examples corroborate the effectiveness of the proposed strategies.

In this article, the collaborative parameter estimation of multiple unmanned surface vessels with model structure uncertainties is studied. The considered parameter estimation problem is first converted into a distributed state and parameter joint estimation problem. Then, the robust distributed estimator is constructed to handle the inevitable model structure uncertainties, and the upper bounds of prediction and estimation error covariance matrices are derived, respectively. In addition, the upper bound of the estimation error covariance matrix is minimized by designing appropriate estimator gains. Finally, the advantages of the proposed distributed parameter estimation approach from the perspectives of information interaction and consideration of model structure uncertainties are verified via simulations and practical experiments.

This article addresses the event-based fully distributed consensus problem for linear heterogeneous multiagent systems (MASs) subject to input saturation. A leader with unknown but bounded control input is also considered. Based on an adaptive dynamic event-triggered protocol, all the agents can reach output consensus without knowing any global knowledge. Moreover, by applying a multiple-level saturation technique, the input-constrained leader-following consensus control is achieved. The given event-triggered algorithm can be utilized for the directed graph containing a spanning tree with the leader as the root. One distinct feature compared with previous works is that the proposed protocol can achieve saturated control without any a priori condition, instead, the local information is needed. Finally, the numerical simulations are illustrated to verify the performance of the proposed protocol.

The distributed Nash equilibrium (NE) seeking problem for multicoalition games has attracted increasing attention in recent years, but the research mainly focuses on the case without agreement demand within coalitions. This article considers a class of networked games among multiple coalitions where each coalition contains multiple agents that cooperate to minimize the sum of their costs, subject to the demand of reaching an agreement on their state values. Furthermore, the underlying network topology among the agents does not need to be balanced. To achieve the goal of NE seeking within such a context, two estimates are constructed for each agent, namely, an estimate of partial derivatives of the cost function and an estimate of global state values, based on which, an iterative state updating law is elaborately designed. Linear convergence of the proposed algorithm is demonstrated. It is shown that the consistency-constrained multicoalition games investigated in this article put the well-studied networked games among individual players and distributed optimization in a unified framework, and the proposed algorithm can easily degenerate into solutions to these problems.

In this article, a new kind of adaptive unscented Kalman filter is proposed to deal with the parameter estimation problem for a class of nonlinear unmanned surface vessel (USV) models with unknown statistical characteristics of process noises. Specifically, the considered parameter estimation problem is first transformed into the state estimation problem by extending 18 parameters and 3 unknown inputs into augmented states for the USVs. With the help of such a transformation, the unknown inputs including disturbances and modeling errors are estimated effectively, and employed to construct the estimators such that the effect of these unknown inputs on parameter estimation can be significantly suppressed. Under the condition that the structure of the covariance matrix of the process noise is available, an adaptive law is further designed to estimate such a high-dimensional covariance matrix where the covariance estimation errors can be reduced. Finally, the proposed estimation approach is verified via performing the practical experiment as well as numerical simulations.

We study the asymptotical consensus problem for multi-agent systems (MASs) consisting of a high-dimensional leader and multiple followers with unknown nonlinear dynamics under directed switching topology by using a neural network (NN) adaptive control approach. First, we design an observer for each follower to reconstruct the states of the leader. Second, by using the idea of discontinuous control, we design a discontinuous consensus controller together with an NN adaptive law. Finally, by using the average dwell time (ADT) method and the Barbǎlat's lemma, we show that asymptotical neuroadaptive consensus can be achieved in the considered MAS if the ADT is larger than a positive threshold. Moreover, we study the asymptotical neuroadaptive consensus problem for MASs with intermittent topology. Finally, we perform two simulation examples to validate the obtained theoretical results. In contrast to the existing works, the asymptotical neuroadaptive consensus problem for MASs is firstly solved under directed switching topology.

This article is devoted to designing distributed adaptive attack-free protocols for the consensus of linear multi-input multioutput multiagent systems under directed graphs, where the appointed-time reduced-order observers are proposed based only upon the relative output information among neighboring agents. One of the distinguishing features of the attack-free protocols lies in the prohibition on information transmission via the communication channel. By viewing the relative control input as the unknown input on the dynamics of each agent, a class of new unknown input observers is introduced with only the relative output measurement involved. The appointed-time estimation of the consensus error is achieved by utilizing jump discontinuity in the observer design and employing the property of the nilpotent matrix. Moreover, a linear transformation is made on the system of consensus error to realize the observer order reduction. Both theoretical analysis and simulation illustration are presented to reveal the effectiveness of the proposed attack-free protocols.

This article aims to establish an appointed-time observer-based framework to efficiently address the resilient consensus control problem of linear multiagent systems with malicious attacks. The local appointed-time state observer is skillfully designed for each agent to estimate the agent's actual state value at the appointed time, even in the presence of unknown malicious attacks. Based on the state estimation, a new kind of resilient control strategy is proposed, where a virtual system is constructed for each agent to generate an ideal state value such that the consensus of normal agents can be achieved with the exchange of ideal state values among neighboring agents. To specify the consensus trajectory while achieving resilient consensus, the leader-follower resilient consensus is further studied, where the leader is assumed to be a trusted agent with a bounded control input. Compared with the existing results on the resilient consensus, the proposed distributed resilient controller design reduces the requirement on communication connectivity significantly, where the allowable communication graph is only assumed to contain a directed spanning tree. To verify the theoretical analysis, numerical simulations are finally provided.

This article works on the consensus problem of nonlinear multiagent systems (MASs) under directed graphs. Based on the local output information of neighboring agents, fully distributed adaptive attack-free protocols are designed, where speaking of attack-free protocol, we mean that the observer information transmission via communication channel is forbidden during the whole course. First, the fixed-time observer is introduced to estimate both the local state and the consensus error based on the local output and the relative output measurement among neighboring agents. Then, an observer-based protocol is generated by the consensus error estimation, where the adaptive gains are designed to estimate the unknown neural network constant weight matrix and the upper bound of the residual error vector. Furthermore, the fully distributed adaptive attack-free consensus protocol is proposed by introducing an extra adaptive gain to estimate the communication connectivity information. The proposed protocols are in essence attack-free since no observer information exchange among agents is undertaken during the whole process. Moreover, such a design structure takes the advantage of releasing communication burden.

This article presents a novel technique for terminal-time synchronization of multivehicle systems. Taking sampled-data communications into account, the terminal-time synchronization problem of multivehicle systems is formulated as three demands. An estimation of terminal time is introduced for each vehicle, and a cooperative algorithm is designed based on motion planning to synchronize all estimated terminal times by using only local sampled-data information. The proposed algorithm not only can ensure the consensus of estimated terminal times at appointed time, but also can make the estimates finally be equal to the actual terminal times. To avoid Zeno behavior, the explicit expression of the norm of the consensus error is formulated, based on which the sampling interval sequence is modified to realize appointed-time approximate consensus of the actual terminal times. Local controller is also designed for each vehicle to ensure the fixed-time convergence of the relative velocity normal to the line of sight, and thus the singularity of control input at terminal time instant is successfully avoided. Experiments are performed to verify the effectiveness of the algorithms.

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems via sensor networks. A novel robust distributed extended Kalman filter, which can handle norm-bounded uncertainties in both the system model and its Taylor series expansion, is developed with ensured estimation performance. The filter is distributed in the sense that for each sensor, only its own measurements and its neighbors' information are utilized to optimize the upper bound of the estimation error covariance. Besides, a sufficient condition for the proposed algorithm is derived, which is simple and user-friendly since it depends on the property of the original nonlinear system instead of the estimation error covariance calculated at every step. Finally, the simulation results are presented to demonstrate the effectiveness of the filtering algorithm.

We aim to solve the consensus problem of linear multiagent systems (MASs) with input saturation under directed interaction graphs in this article, where only local output information of neighbors is available for each agent. By introducing the multilevel saturation feedback control approach, a fully distributed adaptive anti-windup protocol is proposed, where a local observer, a distributed observer, as well as an anti-windup observer are separately constructed for each agent to estimate consensus error, achieve consensus for a certain internal state, and provide anti-windup compensator, respectively. A dual protocol is further presented with the distributed observer designed based on the input matrix, which gives a thorough view on the connection between the distributed observer and the anti-windup observer, and provides the opportunity to reduce the order of the controller by designing the integrated distributed anti-windup observer. Then, three types of distributed anti-windup protocols are proposed based on the integrated distributed anti-windup observer, which requires different assumptions. Specifically, the first protocol needs two-hop relay information to generate the local observer to estimate consensus error; the second protocol designs the local observer with absolute output information to estimate the state instead; while the last protocol introduces certain assumption on transmission zero of agents' dynamics to design the unknown input observer to estimate consensus error. All of the protocols are validated by strictly theoretical proof, and are illustrated by performing simulation examples.

The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent's weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.

In this article, a class of uncertain nonstrict-feedback fractional-order nonlinear single-input-single-output (SISO) systems is investigated. Fuzzy-logic systems (FLSs) are employed to approximate the unknown nonlinear functions and model the uncertain fractional-order nonlinear systems. For the states measurable case, an adaptive fuzzy state-feedback control scheme is developed under the framework of the backstepping technique. For the states unmeasurable case, an observer-based output-feedback control design is proposed by introducing a serial-parallel estimation model and using the dynamic surface control (DSC) technique. Under the drive of the reference signals, the semiglobally uniformly ultimate boundedness for all signals and the tracking errors converging to a small neighborhood of the origin are proved based on the Lyapunov function theory by choosing appropriate design parameters. Two examples with numerical simulations are presented to illustrate the availability of the proposed control approaches.

This paper considers the output-feedback consensus problem of linear multi-agent systems under directed communication topologies. To avoid the network attack from potential malicious attacker, the concept of attack-free protocol is proposed, wherein the information transmission via communication channel is forbidden. Under this circumstance, only relative output measurement can be utilized to generate the observer and the protocol. In this paper, by viewing the relative input of neighboring agents as the unknown input, novel distributed unknown input observer is presented for each agent to achieve appointed-time estimation of the consensus error, where only measured relative output information is used. The fully distributed adaptive protocol is then designed based on the proposed observer. The distributed observer takes a pulsative form at some time instants, which is noncontinuous; while such form takes the advantage of reducing computational cost, compared with the pairwise observer design structure. Simulation results are also conducted to illustrate the effectiveness of the proposed attack-free protocol.

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 presents some necessary and sufficient conditions for the synchronization of second-order interconnected networks, where fixed inner-linked connections with information communication exist. First, a novel derivation of the conditions for a second-order polynomial with complex coefficients to be Hurwitz is provided. Based on this, a sufficient and necessary condition is proposed for the synchronization of the coupled complex network. Next, the design method of the synchronization protocol is constructively given, where the upper and lower bounds of the control gains are obtained through the properties of the polynomial. In addition, we consider some special cases where the second-order model is a double integrator, or general multiagent model without fixed interactions. Finally, the simulation result is given to verify the theoretical analysis.

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.