This paper studies the prescribed-time (PT) event-triggered leader-following bipartite consensus (LFBC) for second-order multi-agent systems (SOMASs) under a directed communication topology. Firstly, to effectively achieve data transmission among agents in an MAS under a limited bandwidth constraint, an event-triggered mechanism (ETM) is introduced into the design of a control protocol (CP). Additionally, a novel CP is constructed by using a time-varying scaling function (TVSF) and the proposed ETM, and it can assure that SOMASs achieve the LFBC within a PT. Then, a sufficient condition for achieving the PT-LFBC of a SOMAS is given through the application of Lyapunov stability theory and algebraic graph theory. Theoretical analysis confirms that the proposed ETM can effectively prevent Zeno behavior. Finally, a simulation study demonstrates the practicability and availability of our theoretical results.

The prescribed-time time-varying formation tracking (PT-TVFT) task is investigated for heterogeneous multi-agent systems (HMASs) with multiple non-autonomous leaders under a directed topology. Firstly, multiple PT adaptive observers (PTAOs) are designed for each follower to observe multiple heterogeneous leaders' states within a PT, where each leader's dynamics is controlled by its input whose upper bound is unknown and cannot be measured by its followers. Moreover, using the PTAOs and an adaptive learning strategy, a fully distributed PT-TVFT controller is developed by incorporating a time-varying scaling function (TVSF). The PT-TVFT controller can guarantee that HMASs accomplish the formation task within another PT. Finally, the effectiveness of the theoretical results is demonstrated through a simulation example.

This article addresses the prescribed-time control of a direct current (DC) microgrid equipped with a hybrid energy storage system (HESS) comprising multiple storage batteries and supercapacitors. First, a novel droop control algorithm is proposed to mitigate constant power load (CPL) fluctuations and enable efficient power distribution among the energy storage components. Next, a prescribed-time extended state observer (PTESO) is developed to estimate the lumped perturbations, including both matched and mismatched components, arising from parameter uncertainties in capacitors and inductors of an HESS. Based on the PTESO, a prescribed-time control strategy is designed to guarantee that the dc microgrid bus voltage accurately tracks its reference value within a prescribed time, irrespective of system initial states. Finally, simulation cases under CPL fluctuations verify the effectiveness of the proposed strategy in improving the performance of a dc microgrid with an HESS.

This paper investigates sampled-data synchronization control of chaotic neural networks subject to actuator saturation. System states are sampled based on a monotonically-increasing-function-based event-triggered sampling scheme. The key feature of this scheme is that an event is triggered only when a prescribed threshold is exceeded by the accumulated square of errors rather than the instantaneous error. With this feature, Zeno phenomenon does not occur, which is proven theoretically. By constructing an event-triggering-function-dependent Lyapunov functional, a sufficient criterion is formulated to ensure asymptotic synchronization between the master and slave systems. Building upon this criterion, an LMI-based algorithm is presented to jointly design both control gains and event-triggered parameters. Finally, a well-studied numerical example is taken to further demonstrated the effectiveness of the proposed results.

The high penetration of distributed generators (DGs) has increasingly complicated the operational environment of distribution networks, posing severe challenges to the timeliness and safety of fault recovery. Accordingly, this paper proposes a cloud-edge collaboration-based intelligent decision scheme for fault recovery in active distribution networks (ADNs). Firstly, a fault dataset is generated using the Monte Carlo method and line graph transformation technology to establish a data foundation for deep learning. Secondly, a decision-making model based on the conditional generative adversarial network (CGAN) is established on the cloud. When a line fault occurs, the edge layer performs rapid inference using local data and the model deployed from the cloud to formulate a load recovery strategy, ensuring swift recovery of the distribution network. Finally, the proposed model is analyzed based on the modified IEEE 33 -node system to verify that the proposed scheme can meet the real-time and high reliability requirements of ADNs for fault recovery.

Synchronization of complex dynamical networks (CDNs) is one of fundamental objectives in control and network science. This paper introduces an event-triggered sampling scheme defined by a monotonically increasing function (MIF), which starts from an initially negative value and triggers a sampling event once it reaches zero. Compared with some existing event-triggered strategies, the MIF-based mechanism is more natural and flexible, as it yields an explicit expression for the estimation on the inter-event times. This event-triggered scheme is then applied to synchronization control of coupled complex dynamical networks via a time-varying state-feedback controller. By employing the looped-functional method, several algorithms are developed to jointly design appropriate control gains and event-triggering parameters. Finally, a numerical example based on an unforced isolate node is presented to demonstrate the effectiveness of the proposed approach.

In this paper, the bounded control gain based pre-scribed-time (pre-T) consensus problem for general linear multi-agent systems (MASs) with controllable agent dynamics is addressed. First, an observer with Pre-T performance is designed for each agent to estimate the leader's state within a prescribed time. Then, based on the estimated states, a Pre-T switching controller integrating a bounded control gain is developed by employing a special coordinate transformation in combination with the backstepping technique, under the assumption that the agents' system matrix pair is controllable. It is shown that the proposed controller enables general linear MASs to achieve the Pre-T consensus independently of the agents' initial conditions and control parameters. Notably, the controller eliminates the numerical implementation problem associated with unbounded control gains, without compromising the consensus performance. The proposed approach is further applied to high-order single-input MASs to demonstrate its broader applicability. Finally, a simulation example validates the effectiveness of both the proposed observer and the Pre-T switching controller.

This paper addresses the robust load frequency control (RLFC) problem in multi-area power systems (MAPSs) with high penetration of renewable energy sources (RESs). A novel hierarchical control framework is proposed, which combines an RLFC strategy with an electro-hydrogen hybrid energy storage system (EH-HESS), to mitigate frequency fluctuations from both slow-varying inter-area tie-line interactions and fast-varying RESs. By treating the inter-area tie-line interaction as inherent system dynamics, the proposed RLFC strategy achieves significant suppression of frequency fluctuations compared to conventional methods. To handle the fast-varying RESs, an EHHESS is integrated, which consists of an electrolyzer array and an energy storage aggregator (ESA) composed of multiple distributed battery units. The electrolyzer array provides a steady base power support, while the ESA rapidly regulates residual power imbalances and ensures precise power tracking against RESs fluctuations. Furthermore, a prescribed-time consensus controller is developed for the ESA, ensuring the prescribed-time tracking of frequency regulation signals while maintaining state-of-charge (SoC) balance across all distributed battery units. The proposed control framework, by actively managing the inter-area couplings and fully leveraging the complementary characteristics of the EH-HESSs, significantly enhances the frequency stability and robustness of MAPSs. Simulation results validate the effectiveness of the proposed overall control framework.

This paper addresses the problem of predefined-time load frequency control (LFC) of power systems with a prescribed precision by a fractional order sliding mode control (FOSMC) approach. In order to guarantee the prescribed precision, a class of specified time prescribed performance (STPP) functions is proposed for the LFC of power systems. Unlike most existing results that are based on prescribed performance (PP) functions, where the prescribed precision is achieved at infinite time, the value of an STPP function evolves to a desired value at an arbitrarily specified time, thereby achieving a prescribed precision at an arbitrarily specified time. Then, an FOSMC approach is developed based on an STPP function as well as system state information for a single-area power system. The proposed FOSMC approach ensures that the frequency derivation of a single-area power system converges to a desired value within a predefined-time, which is an adjustable time parameter and is larger than or equal to the specified time of the STPP function. Next, the rigorous theoretical analysis of the predefined-time LFC is performed using Lyapunov stability theory, the sliding mode control theory and the fractional order calculus. It is demonstrated that the frequency deviation of a single-area power system can achieve the prescribed precision at a specified time and tends to a desired value within a predefined-time. Furthermore, we extend the theoretical result of a single-area power system to the case of a multi-area power system. Finally, the feasibility and superiority of the proposed approach are validated through two simulation cases.

This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC-DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine kernel-based prescribed performance function with smoothly decaying characteristics is designed to form a dynamic performance boundary that gradually tightens as the system state evolves. Furthermore, to effectively eliminate the restriction of traditional PPC on the system's initial state, a time-varying modulation function is introduced. This function dynamically scales the tracking error, thereby improving the system's adaptability at the initial state. A neural network disturbance observer (NNDO) is employed to approximate and compensate for unknown nonlinearities and external disturbances, thereby enhancing system robustness and adaptability. Consequently, a prescribed performance controller that integrates dynamic error modulation and a dual-channel NNDO is proposed. The proposed controller not only guarantees that the tracking error satisfies the prescribed performance constraints but also avoids the computation of high-order derivatives. Simulation results demonstrate that the proposed method maintains bounded convergence of the tracking error and achieves smooth voltage regulation during CPL variations. The results further exhibit excellent dynamic response and steady-state performance.

This article investigates the problem of nonsingular prescribed-time (NSPT) secondary control for heterogeneous battery energy storage systems (HBESSs) subject to operational constraints. Conventional prescribed-time control relies on time-varying gains diverging to infinity as the terminal instant approaches, which inevitably triggers the gain singularity problem and severely hinders its practical application in HBESSs. To overcome this bottleneck, a novel NSPT secondary controller is developed, under which the frequency regulation, proportional active power sharing, and relative energy level balancing of HBESSs are achieved within a user-defined time while effectively avoiding the gain singularity problem. Furthermore, projection operators are introduced to embed the operational constraints of HBESSs into the controller design, which strictly constrains the system’s dynamic trajectory during transient processes to prevent power constraint violations and ensure underlying hardware safety. Finally, comprehensive simulations are conducted on a modified IEEE 33-bus test system to verify the effectiveness and feasibility of the proposed NSPT secondary controller.

Aiming at the problem that it is difficult to adjust the parameters of the controller in the circulating fluidized bed (CFB) boiler combustion system due to its multivariable and strong coupling, an improved linear active disturbance rejection controller (ILADRC) parameter tuning strategy based on the Lévy flight double chaotic sparrow search algorithm (LF-DCSSA) is proposed. The LF-DCSSA algorithm is used to tune the parameters of the ILADRC controller in the multivariable coupled combustion control system of the CFB boiler built by Simulink, so that its control effect can reach the best state. The step response simulation and perturbation simulation are carried out with the theoretically tuned PID and ILADRC. The simulation results show that LF-DCSSA-ILADRC has obvious advantages in the three indexes of time–domain response, such as adjustment time, overshoot, and ITAE, which is more efficient and accurate than that of the theoretical setting, providing a new strategy for the control of the CFB boiler combustion system.

This paper addresses the exact prescribed-time distributed optimization problem (PT-DOP) for second-order multiagent systems (MASs) with a global optimization objective. Some bounded time-varying functions are introduced into controller gains to avoid the numerical implementation problem of unbounded time-varying control gains and ensure that the global optimization objective is reached within a prescribed time. A time-switching centralized optimization controller containing a virtual controller is developed. The exact PT-DOP of the secondorder MASs under the time-switching centralized optimization controller is solved in three phases: [0, TQ1), [TQ1, TQ2), and [TQ2, T). First, the second-order MASs are transformed into a first-order form by the time-switching centralized optimization controller during [0, TQ1). Next, the transformed first-order MASs based on the virtual controller reach the consensus in their positions at TQ2. Finally, the global optimization objective is exactly solved by the virtual controller during [TQ2, T). To avoid each agent utilizing the global gradient information of the MASs, a time-switching distributed optimization controller containing a virtual controller is also introduced, and it can guarantee the exact PT-DOP of the MASs. Three numerical simulation examples are provided to confirm the effectiveness of the proposed controllers.

This paper address the problem of enabling linear multi-agent systems to achieve formation tracking within a user-assigned prescribed-time under a directed communication topology. Initially, a distributed control law is designed for the followers, in which every individual node adjusts its behavior using local information to both maintain the pattern of prescribed-time formation and precisely follow the leader within the assigned time. The controller incorporates a time-varying scaling function(T-Vsf) that reshapes the formation errors, allowing the convergence rate to be explicitly regulated and ensuring that the system meets the prescribed-time requirement. Subsequently, it is demonstrated by Lyapunov stability theory that the MASs under a directed graph achieve the desired formation under the proposed formation tracking controller within any pre-set settling time, and this convergence does not rely on the agents’ initial states or parameter selections. In the final analysis, numerical examples are provided to illustrate the performance capabilities of the proposed formation tracking scheme.

In this paper, the prescribed-time (PT) leader-following bipartite consensus (LFBC) problem of second-order multi-agent systems (SOMASs) with two different topologies is studied, where the two topologies are an undirected topology and a directed topology. First, a novel control protocol (CP) based on a time-varying function (TVF) is proposed to make the MASs realize the LFBC. The settling time (ST) can be prescribed, and is independent of the initial states of all agents. Second, in order to realize the convergence of the MASs at a PT, a new Lyapunov function is designed for the MASs under a directed topology and the corresponding sufficient conditions are given, and guarantee that all the agents’ state errors converge to zero at the PT. Finally, simulation studies demonstrates the availability of our main results.

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled- data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H infinity performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

In this paper, we develop a bounded time-varying gain approach, consisting of a time base generator (TBG)-based observer and a switching controller, to achieve the accurate prescribed-time output consensus for heterogeneous multi-agent systems (MASs). First, the TBG-based observer is designed for each agent to estimate the leader's state, and the accurate estimation time can be pre-set arbitrarily. Next, a switching controller is designed, which integrates a TBG-based time-varying feedback and a fractional-order feedback, with a pre-determined switching instant before a prescribed time. Under this switching controller, the accurate prescribed-time output consensus of heterogeneous MASs is achieved, irrespective of agents' initial states and control parameters. The bounded nature of the TBG gain within the prescribed time interval ensures that the proposed switching controller is non-singular, avoiding the numerical calculation problem associated with unbounded time-varying gains. Moreover, a control optimization algorithm is introduced to select the optimal switching instant, thus solving the accurate prescribed-time output consensus for heterogeneous MASs with minimal control effort. Notably, the proposed approach does not require a temporal scale transformation of the prescribed time intervals and state variables of all agents, simplifying the analysis process. Finally, the theoretical results are validated through a simulation example. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper deals with robust stabilisation of a class of nonlinear sampled-data control systems using the so-called looped functional method. It is known that constructing a proper looped functional is responsible for less conservative stability criterion. This paper paves a new way to construct a looped functional inspired from the Wirtinger inequality. The constructed looped functional can produce an extra negative quadratic term in its derivative, leading to an enhanced stability criterion with both less conservatism and less decision variables. Based on the obtained stability criterion, suitable robust controllers can be designed to maximise the bounds on the nonlinearity that maintain the system stability. Simulation illustrates the efficiency of the proposed method.

This paper designs an attack detection scheme for multi-area hybrid power systems (MAHPSs) with steamhydropower hybrid configurations under covert attacks. Firstly, the dynamics of the MAHPSs with load disturbances and measurement noises under covert attacks is proposed. Subsequently, an observer is designed to reconstruct the true output of the system under the covert attacks. Then, a coding-based detection scheme is built and it can expose the covert attacker. Finally, the feasibility and effectiveness of the proposed scheme are verified by a simulation example.

This article investigates the prescribed-time output consensus (PTOC) of heterogeneous multiagent systems (MASs) using sampled data. First, a novel dynamic compensator is designed for each agent, with its state used for interactions with neighboring agents. A hybrid sampling strategy (HSS) is then developed, combining dynamic event-triggered sampling (ETS) and time-triggered sampling (TTS) to determine when to sample and broadcast the compensator states. The execution of the HSS occurs in two stages: initially, a dynamic ETS is used before the first prescribed time (PT), with a dynamic threshold that decays to zero as the first PT approaches. Following this, a TTS with a constant sampling period (CSP) is introduced after the first PT. The proposed HSS ensures that all compensators achieve state consensus at the first PT while preventing Zeno behavior. Leveraging these dynamic compensators and the HSS, a fully distributed controller with high scalability and flexibility is developed. Through Lyapunov stability theory, it is proven that the heterogeneous MASs, under the proposed controller, achieve output consensus at the second PT, which occurs after the first PT. Finally, a numerical simulation involving eight agents is conducted to verify the validity of the theoretical results.