Continuous underwater bearing-only target tracking with limited sensor detection sectors presents a significant challenge for Autonomous Underwater Vehicles (AUVs). This challenge arises from the tight coupling between target state estimation and AUV trajectory optimization. This paper proposes the Model Predictive Path Integral with Dynamic Weight Integration Tracking (MPPI-DWIT) framework to solve the problem of bearing-only target tracking under limited sensor detection sectors, by integrating an observability metric based on Fisher Information Matrix (FIM) into the MPPI path planner. The observability metric directly improves the accuracy of target state estimation during trajectory sampling. The framework uses a hard-boundary strategy to strictly enforce sensor detection sectors limits and AUV maneuverability constraints. An innovative dynamic weight integration mechanism is proposed, which automatically balances the need to maximize observability with the need to maintain a safe tracking distance. Extensive Monte Carlo simulations show that MPPI-DWIT outperforms the state-of-the-art methods, in terms of estimation accuracy, tracking times, and failure rates. Real-world single AUV tracking trials further confirm the effectiveness and robustness of MPPI-DWIT.

This article investigates the event-triggered anti-disturbance control for multiagent systems (MASs) subjected to multiple disturbances and time-varying transmission delays (TDs). Unlike existing studies that only consider the abrupt changes in parameters or communication topologies, this work employs the dual-Markov jumping processes with a switching signal to describe stochastic behaviors based on a novel mapping technique. The dynamic event-triggered protocol (DETP) is established to reduce communication burdens by incorporating a packet loss schedule (PLS). Additionally, the composite anti-disturbance controllers are developed based on disturbance observers (DOs) and extended dissipative performance analysis. By employing Lyapunov-Krasovskii functional (LKF) and the Finsler lemma, the stabilization conditions of the switched dual-Markov jumping MAS (SDMJMAS) are derived. Finally, the effectiveness of the proposed methods is validated through comparative experiments.

The lag consensus problem of multi-unmanned aerial vehicle (UAV) systems under hybrid attacks is investigated in this paper. First, a dynamic memory event-triggered control protocol is proposed for the multi-UAV system whose normal network communication would be hindered by denial-of-service (DoS) attacks. Different from traditional event-triggered mechanisms, we consider both historically transmitted data and dynamic threshold in the dynamic memory event-triggered protocol, and it will make less data transmissions and better control performance. Second, due to the communication structure is not fixed, we establish a switching-topology-based distributed control architecture. In view of the fact that communication delays among agents cannot be ignored, a distributed controller is proposed to achieves lag consensus of the multi-UAV system. And then, an estimator is designed to address the situation which the system state cannot be measured during the control process. Additionally, the practicality of the distributed control scheme is analyzed by ruling out Zeno behavior. Ultimately, the effectiveness and validity of the proposed control scheme are confirmed through a simulation example.

This paper centers on the distributed fault-tolerant control (DFTC) issues of time-varying delayed nonlinear multiagent systems (TVDNMASs) with switching topologies and external disturbances by considering multiple faults and event-triggered consensus strategy (ETCS). The switching topologies satisfy generally uncertain semi-Markovian switching topologies (GUSMSTs), and they contain uncertain and partially unknown semi-Markovian transition rates (TRs). In addition, the ETCS is adopted in this paper to decide the update of controllers, which alleviates the load of the correspondence network. In view of Lyapunov-Krasovskii functional (LKF), the tracking control protocols are presented to guarantee the DFTC of nonlinear multiagent systems (MASs). Moreover, the controller gain and observer gain matrices are derived through the solution of linear matrix inequalities (LMIs). Finally, a simulation example is proposed to exhibit the capability of our design technique. Note to Practitioners—Due to the complexity of engineering environment, the cooperative control of MASs has gained widespread attention. Nowadays, the MASs are generally utilized in diverse fields, such as multi-motor synchronization, drone swarm formation, and smart grids. As one of the significant research interests in cooperative control, the consensus control of MASs has become a research hotspot. However, in practical applications, due to stochastic system failures and sudden changes in the external environment, it is hard for the fixed communication topologies to cope with these unexpected situations. Therefore, the DFTC issues of delayed nonlinear MASs with GUSMSTs and external disturbances by considering multiple faults are investigated in this paper. Moreover, the mode-dependent distributed time-delay intermediate observers and active fault-tolerant consensus controllers are designed on the basis of distributed fault-tolerant control consensus protocol.

This article is concerned with the memory-based event-triggered leader-following dissipative fault-tolerant consensus (LFDFTC) problem for the nonlinear multi-agent systems (NMASs) with semi-Markov switching topologies subject to the generally uncertain semi-Markov (GUSM) jumping process. Unlike the existing event-triggered (ET) consensus results, the dynamic memory event-triggered mechanism (DMETM) and memory-based distributed fault-tolerant (FT) controllers are designed to reduce the ET times. By constructing a general mode-dependent Lyapunov-Krasovskii functional (LKF) and strictly$(\bf {\mathcal {R,Q,T}})-\boldsymbol {\gamma }$dissipative analysis, the dissipative FT consensus conditions of NMASs are derived in this paper. Finally, three actual physical systems are utilized to verify the validity of the proposed method. Note to Practitioners—Owing to the complexity of engineering environment, the consensus control issue of NMASs has attracted widespread attention. Nowadays, the consensus control of NMASs is generally utilized in diverse fields, such as multi-vehicle coordination, smart grids, and unmanned aerial vehicle formation. However, for the electronic device in practical applications, the channel bandwidth is limited due to power and energy constraints, and it is difficult for the fixed communication topologies and traditional periodic sampled-data control method to cope with these unexpected situations. Therefore, the LFDFTC issue for the NMASs with GUSM switching topologies is investigated by adopting DMETM and memory-based distributed FT controllers in this paper. In addition, the proposed FT consensus control methods with prescribed dissipative performance are applied to multiple vehicles time-invariant formation, Chua’s circuits synchronization, and multiple manipulators consensus.

This article investigates the event-triggered multiasynchronous dissipative sliding mode control problem for the gird-connected systems, where the coupled Inductance-Capacitance (LC) oscillators in electrical networks are subject to actuator attacks and external disturbances. To reduce the communication burden, the dynamic event-triggered mechanisms (DETMs) are introduced along with the switching mechanism for multiple topologies. Specifically, the topology switching process is further viewed as a general uncertain semi-Markov (GUSM) jumping process. This jumping process along with the DETM is thus represented by hidden Markov model (HMM). Then the distributed integral-type sliding mode controller is constructed on the top of the HMM. Sufficient conditions for the desired performance of the closed-loop synchronization error system are derived by constructing the mode-dependent Lyapunov-Krasovskii functional (LKF) with extended dissipativity analysis. The numerical simulation of LC oscillators in the single-phase photovoltagic grid interconnection process is conducted to validate the proposed method.

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

This article focuses on the event-triggered consensus control (ETCC) issue of the time-varying delayed leader-following nonlinear multiagent systems (TVDLFNMASs). In order to minimize the influence on uncertain factors of the information transmission and the data information loss, the switching topologies are constructed as the generally uncertain Markovian jumping forms whose transition rates include completely unknown elements and estimate values of uncertain elements. In addition, the event-triggered (ET) transmission strategy is given based on the threshold parameter and the ET matrix to relieve the communication burden of TVDLFNMASs. The new leader-following (LF) consensus conditions and control gains are obtained based on ET strategy. Finally, the effectiveness of the ET consensus criteria is demonstrated in the simulation section.

This article focuses on the dissipativity-based consensus tracking control (DBCTC) problems of time-varying delayed leader-following nonlinear multiagent systems (LFNMASs) with the event-triggered transmission strategy. The switching topologies of the LFNMASs are subject to the uncertain and partially unknown generally Markovian jumping process. The control inputs of the following agents are updated according to the proposed event-triggered transmission strategy, which could reduce the communication burden. Based on the event-triggered transmission condition and distributed consensus protocol, some dissipativity-based criteria obtained by adopting the delay-product-term Lyapunov-Krasovskii functional (DPTLKF) and higher order polynomial-based relaxed inequality (HOPRII) are proposed to guarantee the LFNMAS consensus. The validity of the main results is verified by two simulation examples.

This article focuses on the synchronization problem of delayed inertial neural networks (INNs) with generally uncertain Markovian jumping and their applications in image encryption. The random connection weight strengths and generally uncertain Markovian are discussed in the INNs model. Compared with most existing INNs models that have constant connection weight strengths, our model is more practical because connection weight strengths of INNs may randomly vary due to the external and internal environment and human factor. The delay-range-dependent synchronization conditions (DRDSCs) could be obtained by adopting the delay-product-term Lyapunov-Krasovskii functional (DPTLKF) and higher order polynomial-based relaxed inequality (HOPRII). In addition, the desired controllers are obtained by solving a set of linear matrix inequalities. Finally, two examples are shown to demonstrate the effectiveness of the proposed results.

This paper is concerned with the finite-time synchronization problem of coupled hierarchical hybrid delayed neural networks. This coupled hierarchical hybrid neural networks consist of a higher level switching and a lower level Markovian jumping. The time-varying delays are dependent on not only switching signal but also jumping mode. By using a less conservative weighted integral inequality and stochastic multiple Lyapunov-Krasovskii functional, new finite-time synchronization criteria are obtained, which makes the state trajectories be kept within the prescribed bound in a time interval. Finally, an example is proposed to demonstrate the effectiveness of the obtained results.

This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.

This paper investigates the problem of sampled-data synchronization for Markovian neural networks with generally incomplete transition rates. Different from traditional Markovian neural networks, each transition rate can be completely unknown or only its estimate value is known in this paper. Compared with most of existing Markovian neural networks, our model is more practical because the transition rates in Markovian processes are difficult to precisely acquire due to the limitations of equipment and the influence of uncertain factors. In addition, the time-dependent Lyapunov-Krasovskii functional is proposed to synchronize drive system and response system. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilize the upper bound of variable sampling interval and the sawtooth structure information of varying input delay. Moreover, the desired sampled-data controllers are obtained. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

In this paper, the local synchronization problem of Markovian nonlinearly coupled neural networks with uncertain and partially unknown transition rates is investigated. Each transition rate in this Markovian nonlinearly coupled neural networks model is uncertain or completely unknown because the complete knowledge on the transition rates is difficult and the cost is probably high. By applying the Lyapunov-Krasovskii functional, a new integral inequality combining with free-matrix-based integral inequality and further improved integral inequality, the less conservative local synchronization criteria are obtained. The new delay-dependent local synchronization criteria containing the bounds of delay and delay derivative are given in terms of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.

This paper investigates the stochastic synchronization problem for Markovian hybrid coupled neural networks with interval time-varying mode-delays and random coupling strengths. The coupling strengths are mutually independent random variables and the coupling configuration matrices are nonsymmetric. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is proposed, where some terms involving triple or quadruple integrals are considered, which makes the LKF matrices mode-dependent as much as possible. This gives significant improvement in the synchronization criteria, i.e., less conservative results can be obtained. In addition, by applying an extended Jensen's integral inequality and the properties of random variables, new delay-dependent synchronization criteria are derived. The obtained criteria depend not only on upper and lower bounds of mode-delays but also on mathematical expectations and variances of the random coupling strengths. Finally, two numerical examples are provided to demonstrate the feasibility of the proposed results.