Object counting is the core of perception components in the industry system. Nevertheless, real-world occlusion and dense clustering pose significant challenges to fruit harvesting. Existing methods closely rely on physics-agnostic 2D clustering with rigid thresholds, neglecting critical 3D geometric cues in complex scenes. Thus, they suffer from the multi-view double-counting issue. In this paper, we propose a 3D object counting framework that integrates semantic Neural Radiance Fields (NeRF) with a physics-guided adaptive clustering algorithm. In particular, we employ a semantic NeRF to achieve implicit 3D scene reconstruction, which effectively isolates target objects from the background. Based on the semantic NeRF, we introduce a physics-guided adaptive clustering algorithm that exploits physically interpretable features, including surface normals and elevation gradients, for accurate unsupervised point cloud segmentation. Subsequently, an energy function optimization mechanism is utilized to autonomously aggregate multi-dimensional features for dynamically adjusting clustering thresholds, which enables the 3D counting to adapt to diverse fruit morphologies without manual parameter tuning. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework, which improves counting accuracy by an average of 9.0 percentage points (6.9 pp on synthetic, 7.2 pp on real-world, and 12.9 pp on Fuji) over the state-of-the-art 3-D baseline.

This paper presents a novel dual event-driven hierarchical control architecture to achieve fully distributed practical prescribed-time consensus (Pd-TC) tracking for uncertain multi-agent systems (MASs). Firstly, a distributed estimate layer is established to reconstruct the leader’s states, guaranteeing practical prescribed-time convergence of estimation errors independent of global information. The proposed communication-triggered event-triggered mechanism (ETM) eliminates continuous communication among neighboring followers, effectively reducing communication resource consumption. Then, utilizing the estimated information, a local control layer is designed to realize practical Pd-TC tracking for uncertain MASs, where the control-update ETM reduces unnecessary control updates to conserve limited control resources, and adaptive gains eliminate the dependence on the bounds of disturbances and uncertain inherent parameters. Furthermore, a rigorous analysis confirms the exclusion of Zeno behavior in both communication-triggered and control-update ETMs. Finally, the proposed control architecture is applied to multiple ground vehicles, validating the theoretical findings.

In this article, the problem of practical prescribed-time (PT) cooperative path following (CPF) is investigated for underactuated autonomous surface vehicles (ASVs), which are not equipped with velocity sensors and subject to unmodeled dynamics and actuator saturation. First, a practical PT velocity observer (PTVO) is designed to estimate unmeasurable velocity information, which is then employed in the design of the guidance law and controller. At the kinematic level, a cooperative guidance law based on aperiodic intermittent communication is developed for synchronized path following, effectively saving communication resources. At the dynamic level, an aperiodic intermittent controller incorporating neural networks (NNs) is designed to approximate unmodeled dynamics and effectively avoid continuous operation of actuators with input saturation. Meanwhile, the intermittent adaptive law is constructed to estimate the optimal weights of the NNs, thereby reducing their complexity. The closed-loop system is verified to converge to a residual set within a PT interval. Finally, we conduct numerical simulations to demonstrate the effectiveness of the proposed algorithms.

Considering the satellites’ wide coverage and independence from geographical constraints, satellite edge computing (SEC) has demonstrated broad application prospects. In this paper, a joint offloading and caching framework is proposed, addressing issues such as redundant data transmission, heterogeneous resources, and the high-speed movement of satellites in SEC. In the framework, latency and system energy consumption are reduced by dynamically caching reusable data and formulating an appropriate offloading strategy. Considering the complexity of the problem, we propose a GRU-SAC algorithm that trains multiple distinct deep neural networks (DNNs) to output offloading and caching actions. It combines maximum entropy with policy gradient to enhance strategy stability and avoid local optima. Furthermore, the algorithm predicts the future request probabilities of tasks as part of the state space, aiding in strategy training and adapting to dynamically changing environments. Simulation results substantiate the effectiveness of our proposed method in reducing latency and system energy consumption. In the simulation environment comprising five users and two satellites, the latency decreased from nearly 132 to around 129, and the energy consumption decreased from nearly 22 to around 18.

In this article, Nash equilibrium seeking in theN-coalition noncooperative game is studied for the second-order disturbed players. In this formulation, players are divided into different coalitions. Each coalition acts as a virtual player in the noncooperative game, but the real decision-maker is the individual player. Players within the same coalition work together to minimize the cost function of the coalition they belong to, while each coalition competitively minimizes its own cost function. A prescribed-time event-triggeredN-coalition Nash equilibrium seeking strategy is proposed based on the gradient descent method and the dynamic average consensus protocol. The proposed algorithm ensures that the players’ actions converge to the Nash equilibrium of theN-coalition game within a prescribed time, which can be assigned in advance, without any knowledge of the initial states and the system parameters. Additionally, information exchanges between players only happen when the designed event-triggering condition is met, thereby reducing the communication burden. The prescribed-time stability of theN-coalition Nash equilibrium is rigorously proven by Lyapunov stability analysis. The Zeno behavior is shown to be prevented until the Nash equilibrium is reached. Finally, the simulation experiments on maneuvering automated ground vehicles demonstrate the effectiveness of the proposed algorithm.

This paper investigates the practical prescribed-time consensus (Pd-TC) for nonlinear multi-agent systems (MASs) in the presence of uncertain disturbance, employing intermittent adaptive dynamic event-triggered and self-triggered controllers, respectively. A novel lemma for achieving the practical prescribed-time stability (Pd-TS) is proposed within the framework of intermittent control (IC), where a single parameter exclusively bounds the settling time. To further reduce the triggered instants, a dynamic variable is introduced to construct the dynamic event-triggered mechanism (D-ETM). Utilizing the proposed lemma, an intermittent adaptive dynamic event-triggered controller is developed by incorporating D-ETM with an intermittent adaptive control scheme, which achieves the practical Pd-TC for uncertain nonlinear MASs. Notably, the developed controller is devoid of global information, such as algebraic connectivity and system scale. Following this, an intermittent adaptive self-triggered controller is designed to eliminate the necessity for continuous monitoring. The results presented above are finally applied to Chua’s system, accompanied by a numerical example to demonstrate the efficacy of the designed controllers.

This article adopts the event-triggered control strategy (E-TCS) to achieve the practical predefined-time synchronization (PPTS) for dynamic complex networks (DCNs) with Markov switching topologies under stochastic denial-of-service (SDoS) attacks. We consider the Markov switching topologies, and the coupling weight of the complex networks between nodes is dynamic. For the proposed E-TCS, the minimum inter-event interval can be directly obtained, thereby eliminating the Zeno phenomenon. By employing the time-varying function, all states of the DCNs can achieve PPTS within the predefined time. Concretely, in contrast to finite/fixed-time synchronization, utilizing PPTS enables the arbitrary setting of convergence time, independent of initial values and controller parameters. Notably, the SDoS attacks occur with a certain probability within the attack intervals. Moreover, the intermittent attacks and the average non-attack rate are considered, and this approach leads to less conservative results. Additionally, we prove that a higher average non-attack rate makes it easier for all states of the DCNs to achieve PPTS. Finally, the validity of the proposed E-TCS is verified by the examples of Chua’s circuit and the Kuramoto oscillator network.

This article develops a new observer-based practical predefined-time distributed Nash equilibrium seeking (DNES) algorithm for a network of players in noncooperative games under aperiodically intermittent control (AIC). The proposed intermittent controller is designed in an aperiodic manner, offering a broader applicability compared with the existing periodically intermittent controllers. Considering the players with uncertain disturbances, a disturbance observer is established, which facilitates the development of the practical predefined-time DNES algorithm. The proposed predefined-time control algorithm can ensure the convergence of the players’ actions within an adjustable neighborhood around the Nash equilibrium in a prespecified time, regardless of the initial states and control parameters. Moreover, the dynamic event-triggered communication scheme is employed, allowing players to exchange information only when the triggering condition is satisfied, thereby reducing the communication burden. In addition, the Zeno behavior is excluded. Finally, a simulation example of connected automated ground vehicles is provided to demonstrate the theoretical results.

In this study, the fixed-time synchronization (FTSn) problem is investigated for coupled reaction-diffusion systems (RDSs) with time-varying delay based on an aperiodically intermittent control (AIC) strategy. For the fixed-time control, the convergence time can be estimated in advance, irrespective of initial states. Additionally, unlike the previous studies with the semi-intermittent control strategy, the FTSn is achieved for the coupled RDSs via completely AIC by adopting the average control rate, then the mechanism is more general. Meanwhile, the utilization of average control rate indicates that the results obtained are less conservative. Furthermore, a new auxiliary function is designed to demonstrate that the fixed-time convergence of the coupled RDSs can be guaranteed with or without the presence of time-varying delay. Finally, numerical examples are provided to verify the effectiveness of the theoretical results.

In this paper, an aperiodically intermittent event-based control strategy is developed to investigate the practical fixed-time consensus (FTC) tracking problem of nonlinear multi-agent systems (MASs). Different from the traditional event-based scheme, we incorporate the event-based scheme into the intermittent control mechanism, and the aperiodically intermittent event-based mechanism is developed, which can significantly save resources, particularly in terms of reducing the energy consumption of communication. Additionally, our proposed mechanism enables practical intermittent event-based FTC tracking for a directed graph, while eliminating the dependence on initial states for convergence time estimation. Moreover, the measurement error and intermittent event-based controller are constructed based on the hyperbolic tangent function, then the non-differentiable problem and Zeno behavior can be avoided. Furthermore, an improved triggering mechanism of the event-based scheme is designed to avoid continuous monitoring in control intervals. Hence, resource consumption can be further reduced. Finally, the multiple ground vehicles and Chua’s circuit are considered in simulation examples to verify the effectiveness of theoretical results.Note to Practitioners—This paper addresses the FTC tracking problem of MASs via intermittent event-based control for a directed graph, which can be applied to multiple ground vehicles and Chua’s circuit system. Unlike the asymptotic and finite-time stability results, the upper bound of the convergence time can be estimated, which is unrelated to the initial states and can better satisfy the application requirements. Considering the limitation of communication bandwidth and saving resources, we take the event-based scheme into the intermittent control mechanism, and a new aperiodic intermittent event-based controller is designed under the fixed-time convergence. Contrary to the traditional fixed-time control strategies via intermittent control or event-based control, the proposed algorithms in this study can effectively reduce the update frequency of the controller and significantly save energy under intermittent monitoring, which is more friendly for control engineers. The feasibility of the obtained results is demonstrated by examples of multiple ground vehicles and Chua’s circuit. Potential applications of the proposed control algorithms include smart grid, cooperative search and exploration.

In this article, a new event-based aperiodic intermittent fixed-time consensus control strategy is developed for multiagent systems (MASs) with nonlinear uncertainties and unknown control directions. Concretely, the intermittent control strategy is considered to construct the intermittent event-based control (IEBC) algorithm, which leads to substantial savings in communication resources. In addition, an enhanced triggering algorithm is further designed to eliminate continuous monitoring of neighbors' and its own states. Consequently, different from the existing event-based control strategy, the IEBC algorithms proposed herein can further reduce communication energy consumption. To handle the nonlinear uncertainties in MASs, fuzzy logic systems are utilized, enhancing the algorithm to tackle more general problems. Considering the problem of unknown control directions, a controller employing Nussbaum-type functions is formulated. Moreover, the fixed-time consensus control algorithm is incorporated, and the estimation of the convergence time is not reliant on the initial states. Finally, the feasibility of the proposed algorithm is demonstrated through a numerical example.

In this article, the practical fixed-time synchronization (PFIXTS) problem of multilayer complex networks (CNs) is investigated based on an intermittent event-triggered control (IE-TC) strategy. Under a new framework of intermittent control (IC), a practical fixed-time stability lemma is proposed. In addition, the conservatism of the results is reduced resulting from the use of average control rate (ACR) for IC. Based on the practical fixed-time stability lemma, a new theorem is developed to achieve the PFIXTS for multilayer CNs, which can further reduce the energy consumption of communication and save resources. Moreover, the emergence of Zeno behavior in the IE-TC strategy is excluded. Finally, the effectiveness of the results is verified by numerical simulations.

This article deals with the problem of multiple-event-triggered dissipative tracking control for nonlinear Markov jump systems with incomplete transition probabilities. An interval type-2 fuzzy model with partially known transition probability matrix is used to capture the underlying nonlinearities and a hidden Markov model with an incomplete conditional probability matrix is employed to describe the possible asynchronous phenomenon between the plant and the tracking controller. A multiple-event-triggered methodology involving two adaptive event-triggered schemes for the actuator channel and the sensor channel is proposed. By using the Lyapunov and dissipativity theory, sufficient conditions for the desired tracking controller are established in terms of linear matrix inequalities. Last, two examples, involving one numerical and one practical model named the Hénon system, are utilized to show the effectiveness of the proposed tracking control algorithm.

Existing approaches to constrained-input optimal control problems mainly focus on systems with input saturation, whereas other constraints, such as combined inequality constraints and state-dependent constraints, are seldom discussed. In this article, a reinforcement learning (RL)-based algorithm is developed for constrained-input optimal control of discrete-time (DT) systems. The deterministic policy gradient (DPG) is introduced to iteratively search the optimal solution to the Hamilton-Jacobi-Bellman (HJB) equation. To deal with input constraints, an action mapping (AM) mechanism is proposed. The objective of this mechanism is to transform the exploration space from the subspace generated by the given inequality constraints to the standard Cartesian product space, which can be searched effectively by existing algorithms. By using the proposed architecture, the learned policy can output control signals satisfying the given constraints, and the original reward function can be kept unchanged. In our study, the convergence analysis is given. It is shown that the iterative algorithm is convergent to the optimal solution of the HJB equation. In addition, the continuity of the iterative estimated Q -function is investigated. Two numerical examples are provided to demonstrate the effectiveness of our approach.

This article focuses on the bounded stabilization issue for stochastic Takagi–Sugeno (T–S) fuzzy systems with external disturbances under fuzzy intermittent event-triggered control. Different from the common intermittent control scheme, the intermittent control proposed is based on an event-triggered mechanism instead of a traditional time-triggered mechanism during the work intervals. As a result, it reduces unnecessary sampling times and resource waste to a great extent. Meanwhile, the minimum interexecution time is obtained for T–S fuzzy systems under the stochastic case. In addition, this article presents a novel Lyapunov function, which simplifies the proof compared to the traditional Lyapunov function for intermittent control. Based on the average control rate adopted and the Lyapunov method, a bounded stability criterion is established, which is less conservative. Then, a corollary is given under the fuzzy event-triggered control. Finally, an example is shown to illustrate the effectiveness of the results obtained.

This study proposes a novel dynamic event-triggered impulsive control (ETIC) scheme to study the exponential stabilization of general stochastic nonlinear systems, where the impulsive sequence is determined by a dynamic event-triggered mechanism. The dynamic ETIC can effectively reduce controller updates and significantly save energy under the same decay rate compared to traditional static event-triggered impulsive generators. Additionally, there is a guaranteed positive minimum inter-event time for each sample path solution of systems. Furthermore, the proposed dynamic ETIC scheme is employed to stabilize stochastic complex networks based on the graph theory and the Lyapunov method. Finally, we provide two illustrative examples to verify the effectiveness and correctness of the proposed dynamic ETIC scheme.

This article investigates the adaptive event-triggered finite-time dissipative filtering problems for the interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy Markov jump systems (MJSs) with asynchronous modes. By designing a generalized performance index, the$H_{\infty }$,$L_{2}-L_{\infty }$, and dissipative fuzzy filtering problems with network transmission delay are addressed. The adaptive event-triggered scheme (ETS) is proposed to guarantee that the IT2 T–S fuzzy MJSs are finite-time boundedness (FTB) and, thus, lower the energy consumption of communication while ensuring the performance of the system with extended dissipativity. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on an adaptive law, which is obtained online rather than as a predefined constant. Besides, the asynchronous phenomenon between the plant and the filter is considered, which is described by a hidden Markov model (HMM). Finally, two examples are presented to show the availability of the proposed algorithms.

This article addresses the event-triggered asynchronous fault detection (FD) problem of fuzzy-model-based nonlinear Markov jump systems (MJSs) with partially unknown transition probabilities. For this objective, the nonlinear plant is modeled as an interval type-2 (IT2) fuzzy MJS with the aid of the IT2 fuzzy sets capturing the uncertainties of the membership functions. An adaptive event-triggered scheme is introduced to bring down the costs of the communication network from the system to the fuzzy fault detection filter (FDF), in which the triggering parameter can be adaptively tuned with the system dynamics. A hidden Markov model (HMM) is employed to characterize the asynchronous phenomenon between the system and the FDF. Unlike the existing results, the transition probabilities of the plant and the FDF are allowed to be partially known. By using the Lyapunov and the membership-function-dependent methods, the existence conditions of the FDF are derived. Finally, the proposed FD methods are verified by a numerical simulation.

This article addresses the fixed-time average consensus problem of nonlinear multiagent systems (MASs) subject to input delay, external disturbances, and switching topologies. Different from the finite-time convergence, the convergence time of the fixed-time convergence is independent of initial conditions. Then, an event-based control strategy is presented to reach the fixed-time average consensus under switching topologies and intermittent communication. Because the nonlinear dynamics, external disturbances, switching topologies, and triggering condition for intermittent communication are considered, the fixed-time consensus problem is more challenging under the event-based control than under the continuous-time control. Besides, a new measurement error is designed based on the hyperbolic tangent function to avoid Zeno behavior. Furthermore, an improved triggering function is designed to avoid continuous monitoring. Hence, resource consumption is reduced significantly. Finally, the effectiveness of the algorithms is validated by three simulation examples.

This article considers the practical fixed-time self-triggered consensus tracking problem of delayed multiagent networks (MANs) subject to external disturbances under undirected topology and directed topology. The fixed-time consensus implies that the consensus is reached in a finite time and the convergence time is independent of initial conditions under the nonlinear consensus protocols. A self-triggered control (STC) strategy is developed based on the event-triggered control (ETC) strategy. For the ETC strategy, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid a nondifferential problem and Zeno behavior. To avoid continuous monitoring, the STC strategy is presented. Furthermore, the minimal interevent interval is strictly positive, which implies that no Zeno behavior occurs in the STC strategy. Finally, a numerical example is presented to verify the availability of the algorithms.

This paper investigates the exponential synchronization (ES) problem for complex networks (CNs) under an intermittent adaptive event-triggered control (IAE-TC) strategy which is based on dynamic IAE-TC during the control activation intervals. In this control mechanism, the intermittent controller has adaptability to the evolution results of the controlled networks and is activated only when the event-triggered condition is violated. Then, by employing this kind of control strategy and the Lyapunov method, some sufficient conditions are proposed to achieve the ES of CNs and it is proven that the Zeno behavior can be eliminated. Besides, an application about the islanded microgrid system and some numerical simulations are provided to verify the effectiveness of the derived theoretical results. Moreover, in the numerical simulations, it is shown that the dynamic IAE-TC strategy can decrease the number of event-triggered instants more availably than the static one.

This article addresses the team-triggered fixed-time consensus problems for a class of double-integrator agents subject to uncertain disturbance. Compared with the finite-time results, the convergence time of the fixed-time results is independent of the initial conditions. Furthermore, a novel team-triggered control (TTC) strategy is presented. This control strategy incorporates the event-triggered control (ETC) and self-triggered control (STC). The ETC and STC are proposed to achieve the fixed-time consensus of second-order multiagent systems (MASs), and no Zeno behavior occurs. The TTC scheme, derived by combining the ETC scheme and the STC scheme, is able to relax the requirement of continuous communication and thus lowering the energy consumption of communication while ensuring the performance of the system. The effectiveness of the proposed algorithms is validated by numerical simulations.

The optimal solution to the leader-follower bipartite output synchronization problem is proposed for heterogeneous multiagent systems (MASs) over signed digraphs in the presence of adversarial inputs in this article. For the MASs, the dynamics and dimensions of the followers are different. Distributed observers are first designed to estimate the leader's two-way state and output over signed digraphs. Then, the leader-follower bipartite output synchronization problem on signed graphs is translated into a conventional output distributed leader-follower problem over nonnegative graphs after the state transformation by using the information of followers and observers. The effect of adversarial inputs in sensors or actuators of agents is mitigated by designing the resilient H∞controller. A data-based reinforcement learning (RL) algorithm is proposed to obtain the optimal control law, which implies that the dynamics of the followers is not required. Finally, a simulation example is given to verify the effectiveness of the proposed algorithm.

This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.

In this paper, we study the fixed-time event-triggered consensus problem of the uncertain nonlinear multiagent systems. Two fixed-time event-triggered consensus controllers are proposed. In contrast to finite-time results, the convergence time of fixed-time results is independent of initial conditions. Furthermore, continuous communications can be avoided both in the update of controllers and in the triggering condition monitoring. It is proved that there is no Zeno behavior under the fixed-time event-triggered consensus control strategies. The availability of the control algorithms is verified by numerical simulations.