This paper investigates the discrete-time nonlinear zero-sum game problem with alternating dual-impulse control based on adaptive dynamic programming (ADP). Such problems appear in scenarios with scheduled discrete interventions, such as nonlinear oscillators with impulsive excitations or periodic medical dosing. The main objective is to determine equilibrium strategies and corresponding saddle-point solutions in impulsive nonlinear systems. The key challenge lies in handling nonsmooth state transitions and the computational complexity of nonlinear dynamics. To overcome these difficulties, we design a periodic cumulative utility function that focuses computation on impulse instants and develop ADP algorithms for efficient learning. Convergence to saddle-point equilibria is theoretically proven when such equilibria exist, and the approach remains effective even when exact equilibria do not exist. Simulations on Duffing and torsional systems confirm both the accuracy and robustness of the proposed framework.

This article focuses on the finite-time adaptive distributed Nash equilibrium seeking (NES) strategies for networked games of multiagent systems with partial-decision information. Multiplayer optimization and decision-making are widely applied in practical engineering scenarios such as smart grid dispatching, sensor networks, and distributed cooperative control. Existing distributed NES algorithms fail to simultaneously achieve finite-time convergence and adaptive gain tuning under partial-decision information, and often become invalid under switching topologies in dynamic networks. To overcome this challenge, we propose two types of seeking strategies leveraging gradient-based play, leader-following consensus algorithms, and adaptive control laws. First, a finite-time node-based adaptive seeking strategy is developed, where each agent adaptively adjusts its weight based on the consensus error in finite time. Then, a finite-time edge-based seeking strategy is designed by tuning the adaptive parameters combined with the weights of the edges of communication graphs in finite time. Moreover, we show that the proposed finite-time NES algorithms can be extended to switching communication networks, providing a feasible solution for distributed decision-making in large-scale networked systems requiring fast and reliable convergence. Finally, some illustrative examples are presented to verify the proposed algorithms’ effectiveness.

In complex adversarial games, cooperative multi-agent systems must maintain effective coordination with agent failures. Existing methods lack fault tolerance, hierarchical coordination, and training efficiency, which limits robustness in complex missions. To address these challenges, a hierarchical switched control network with impulsive attention is proposed. The network decouples high-level strategic coordination from low-level tactical execution, enabling flexible policy switching under decentralized execution. A spiking-attention actor-critic algorithm is designed to achieve fault-tolerant decision-making by masking invalid information from faulty agents. To mitigate sparse-reward issues, a hierarchical reward design is introduced, and training convergence and sample efficiency are improved via prioritized experience replay mechanism. Theoretical analysis is provided to show the correctness of faulty-token suppression and to characterize bounded perturbation under fault masking. Simulations and experiments demonstrate that the proposed method achieves stronger robustness and more stable performance in reconnaissance-attack games with agent failures.

This paper investigates the existence of Nash equilibria in multi-player nonzero-sum differential games governed by hybrid impulsive systems, where one player applies impulse controls while the remaining N players adopt piecewise-continuous controls. The proposed framework captures complex dynamics in which gradual adjustments coexist with abrupt interventions, with applications in traffic management, power dispatch, and emergency response. First, the necessary conditions for the existence of hybrid-strategy Nash equilibrium are rigorously derived using a variational method. Second, under traditional convexity assumptions, a sufficiency theorem for equilibrium existence is provided. By further introducing pseudoconvexity and invexity, a weak convexity framework with broader applicability is proposed, significantly relaxing the regularity requirements on the cost functions. Finally, numerical simulations, including a power system load dispatch case, demonstrate the validity of the theoretical results.

This paper investigates the zero-sum (ZS) game problem for discrete-time nonlinear impulsive systems based on adaptive dynamic programming (ADP). First, a nonlinear impulsive system model is constructed, in which two impulse control players alternately trigger impulses at specific times to achieve optimal system performance. Then, transformations are applied to the nonlinear systems and the utility function to enable the ADP framework. Subsequently, the upper and lower iterative impulsive ADP algorithms are designed to approximate the upper and lower optimal performance index functions for this ZS game. Through the iterative process of the ADP algorithm, the system gradually approaches a saddle-point equilibrium under impulse controls. Simulation results confirm the proposed improved ADP method's efficacy.

This article targets at addressing the controllability problem of a new introduced fractional-order impulsive and switching systems with time delay (FOISSTD). Toward this end, the algebraic method is adopted to establish the relevant controllability conditions. First, we obtain the solution representation of FOISSTD over every subinterval by resorting to the successive iterations and Laplace transform. Next, by introducing the Gramian matrices over every subinterval, we establish the controllability conditions without requiring the constraint that all impulse-dependent matrices are nonsingular. Furthermore, by introducing a row matrix composed by several Gramian matrices, we further construct a controllability test that is proved to be sufficient and necessary. In the last, a calculative example with three subsystems is worked out to confirm the theoretical controllability tests.

This paper studies the finite-time stability (FTS) for nonlinear impulsive systems (NISs), where the impulse effect is time-varying and could be stabilizing or destabilizing impulse. With the help of Lyapunov function methods and dwelltime approaches, some novel weak Lyapunov-based results on local/global FTS, including attraction domain and settling time estimation, are derived for NISs with Time-Varying impulsive strength factors. The improved Lyapunov theorems are based on a differential inequality, in which the derivative of Lyapunov function is not necessarily negative definite. Furthermore, for impulsive systems with fixed dwell-time conditions and average impulsive time conditions, more general theories are developed by analyzing the impact of impulse jumps on FTS. Finally, the effectiveness of the theoretical analysis is validated through a simulation example.

For heterogeneous multi-robot systems with unknown parameters and input constraints, the problem of simultaneous target tracking and collision avoidance is studied in this paper. A novel auxiliary system is proposed to generate the corresponding compensation signal to tackle the difficulty of controller design resulting from input constraints. For each heterogeneous robot system, a unique collision avoidance controller is constructed utilizing the repulsive potential field method. Then, the adaptive collisions-free tracking control strategy is presented under the control framework of the proposed auxiliary system. It can be proved via Lyapunov stability theory that the designed controllers can allow the heterogeneous multi-robot systems to track the reference target with collision avoidance and guarantee that all the closed-loop signals in the multi-robot systems are bounded. Subsequently, the validity of our control scheme is further verified through a common simulation example.

This paper investigates a pursuit-evasion game involving multiple pursuers and a single evader with unknown nonlinear dynamics under switching topologies. A distributed neural network-based estimator is designed to reconstruct the evader’s dynamics. An augmented system is constructed by integrating estimation errors with pursuer–evader relative errors, and a discounted performance index is established to derive optimal control strategies for both sides. To address unknown nonlinearities, an integral reinforcement learning-based actor–critic architecture is proposed for online approximation of the optimal value function and control strategies. The uniform ultimate boundedness of the closed-loop error system and network weight estimation error is rigorously proven. Simulation results verify the effectiveness of the proposed method.

Multi-agent games in complex adversarial scenarios have gained significant attention. Existing methods face challenges in modeling multi-stage games in complex reconnaissance environments and addressing incomplete information issues. Inspired by the results of brain science research, this paper proposes a hybrid predictive network coupled with a reinforcement learning algorithm with perceptual and environmental constraints. An impulsive long short-term memory network (ImpLSTM) is designed to simulate the brain’s short-term memory mechanism. A hybrid opponent decision prediction network combining ImpLSTM and Generative Adversarial Network (GAN) is established to enhance the prediction of opponent behavior under perception constraints. A reinforcement learning-based decision algorithm is developed to optimize decision strategies through various reward functions, enhancing the decision-making performance of agents in complex environments. Simulations and physical experiments demonstrate the performance and effectiveness of the proposed algorithm in complex games.

This article investigates a multipursuer single-evader game in multiagent systems with unknown nonlinear dynamics. Unlike most of the existing studies that rely on complete information, this work addresses a more realistic scenario where pursuers face incomplete perception and limited communication ranges. To address these challenges, a novel performance index is formulated based on distributed relative distance errors, augmented with a communication cost term that penalizes excessive interpursuer separation, thereby promoting reliable information exchange among pursuers. A synchronous integral-reinforcement-learning-based actor-critic framework is then developed, which learns both the optimal value function and control strategies online directly using trajectory data, eliminating the need for prior dynamics knowledge or stabilizing initial strategies. Rigorous analysis establishes sufficient conditions for saddle-point equilibrium and guarantees uniform ultimate boundedness of the closed-loop errors with an explicit bound. Simulation results based on an autonomous aerial vehicle pursuit-evasion scenario demonstrate the effectiveness of the proposed approach.

To intercept an attacker in the attack-defense confrontation scenario, this paper investigates the distributed formation defense game problem for heterogeneous nonlinear multi-agent systems (MAS) with actuator faults. To address actuator faults in defenders, a class of fault observers is proposed to estimate unknown actuator faults for fault compensation. Based on differential graphical game theory, a novel distributed formation defense game strategy design method is presented. An online learning algorithm based on adaptive dynamic programming (ADP) is proposed to approximate the optimal formation defense game controller. The convergence of both actuator fault estimation errors and Actor-Critic neural networks weight estimation errors is theoretically proved. Finally, a numerical simulation example demonstrates the effectiveness of the proposed approach.

Nanoemulsion has significant application potential in enhancing oil recovery in low-permeability reservoirs due to its unique nanoscale size and excellent interfacial properties. In this paper, a novel nanoemulsion flooding system was prepared by a microemulsion dilution method, using nonionic surfactant fatty alcohol polyoxyethylene ether (AEO-9), zwitterionic surfactant cocoamidopropyl hydroxy sulfobetaine (CHSB), liquid paraffin, oleic acid, n-butanol, and aqueous sodium tosylate. The key performance parameters, such as droplet size, interfacial tension (IFT), and wettability, were evaluated in the laboratory, and the oil displacement performance of the nanoemulsion was assessed through an oil-washing ability experiment and a displacement experiment. The results showed that the droplet size of the nanoemulsion system was 30-50 nm. It has ultralow IFT (<1 × 10-2 mN/m) and could change the core surface from hydrophobic to hydrophilic, maintaining emulsion stability even at a high temperature of 100 °C. Through the displacement experiment, the nanoemulsion demonstrated the characteristics of "rapid breakthrough-high-efficiency transport" (diffusion coefficient of 5.086 × 10-4 cm2/s). Finally, an additional oil recovery of 17.71% was achieved when the nanoemulsion injection concentration was 0.30 wt % and the injection volume was 0.4 PV. The nanoemulsion system using aqueous sodium tosylate (NaOTs) as the continuous phase exhibits both high-salt tolerance and high-temperature hydrolysis resistance, making it suitable for nanoemulsion flooding in high-temperature, high-salt reservoirs. This paper aids in selecting the optimal nano-oil displacement agent for enhanced oil recovery (EOR) projects in high-temperature, high-salinity, low-permeability reservoirs and promotes the application of nanoemulsions in oil fields.

This article tackles the challenge of state bounding estimation for discrete-time switched genetic regulatory networks (DSGRNs) under constraints involving time delays and bounded exogenous disturbances. By introducing specific assumptions regarding system parameters, a polytope is formulated, ensuring that all solutions of the considered DSGRNs exhibit exponential convergence towards this polytope through an average dwell time (ADT) method and mathematical induction. Furthermore, particular attention is given to additional beneficial results, such as those arising in special cases, notably when the disturbances are absent (i.e., the disturbances vanish) and when initial conditions are set to zero. In the scenario of zero disturbances, a sufficient condition is established to ensure the global exponential stability of the system. Regarding zero initial conditions, a polytope is introduced to confine all system trajectories within bounds. By leveraging special properties of Metzler matrices and nonnegative matrices, we derive more succinct criteria for state bounding. Subsequently, two numerical simulations are conducted to validate the theoretical findings. Unlike existing literature, our study focuses on DSGRNs with a more generalized structure, and our employment of the ADT approach circumvents the need for complex matrix inequality computations typically found in the Lyapunov functional method.

In large-scale crowd evacuation scenarios, a critical challenge lies in effectively balancing evacuation efficiency with congestion control. This paper introduces a crowd evacuation model based on mean field game (MFG) theory, which is designed to optimize evacuation strategies and mitigate congestion in large-scale scenarios. First, an optimal control model for crowd evacuation is reformulated using MFG theory. Subsequently, a nested numerical algorithm combining Newton and Picard iterations is developed to compute the equilibrium solutions, effectively mitigating numerical instability and reducing computational burdens. Experimental results validate the effectiveness of the proposed algorithm and highlight the potential of MFG approach in multi-agent evacuation modeling.

Low-permeability reservoirs, characterized by poor reservoir quality and pronounced heterogeneity, consistently encounter engineering challenges during waterflooding operations, such as suboptimal displacement efficiency and restricted sweep coverage. Nanoemulsions, through wettability alteration and enhancing macroscopic sweep efficiency, are widely applied in the efficient development of low-permeability reservoirs. However, current field applications in Reservoir G exhibit inefficacy, necessitating laboratory experiments to identify factors affecting poor injection performance and enhance oil recovery. This study conducted laboratory core displacement experiments using native cores from the target reservoir, integrated field operational data for injection parameter optimization, and implemented field applications. Results indicate: (1) The modified nano-SiO₂ emulsion, with an average particle size of 26.01 nm, is substantially below the pore-throat dimensions of reservoir cores; (2) Wettability reversal was confirmed through contact angle reduction on oil-wet substrates from 132.9° to 53.6°; (3) Optimal parameters-0.1 mL/min injection rate, 0.3 wt% concentration, and 0.3 PV slug volume-Utilizing dual-slug injection (0.15 PV × 2 + 0.1 PV water spacer) enhanced oil recovery by 18.83%. Segmented injection effectively mitigates particle adsorption-induced plugging and achieves deep reservoir penetration, outperforming single-slug injections. Field experimental results show that segmented injection with water spacers increased daily fluid production from 2.86 m³ to 5.22 m³ and daily oil production from 2.23 t to 4.3 t, retaliating a synergistic mechanism of the modified nano-SiO₂ emulsion in "performance-parameter-mode" optimization.

The oil reservoirs of the metamorphic rocks in Bohai Bay have geological characteristics such as low matrix porosity and permeability, developed natural microfractures, which result in the injection water rapidly advancing along fractures, a fast increase in the water content, and difficulties in extracting the remaining oil. In order to reveal water channeling and the residual oil formation mechanisms in fractured low-permeability reservoirs and solve the water channeling problem, we first analyzed the reservoir development status, then studied the formation mechanism of residual oil using a microfluidic chip device, and formed a method of hierarchical control to effectively control the water channeling problem of fractured reservoirs and maximize the displacement of residual oil. The results show that (1) Due to the low permeability of the reservoir matrix, a large amount of injected water flows along the fracture channel, which leads to the long-term high water cut of some oil wells and the retention of a large amount of crude oil in the matrix. (2) The results of microfluidic experiments show that the distribution of residual oil after water flooding mainly includes five types: blind end of the pore throat, columnar, cluster, flake and film, and residual oil. Among them, sheet-like and clustered residual oil are dominant, accounting for 75 similar to 85% and 10 similar to 13%, respectively. (3) Based on the characteristics of fracture development in buried-hill reservoirs, a hierarchical control technology of "gel particle + liquid crosslinked gel system" is established. The field application effect predicted that the input-output ratio was 1:3. This study provides a reference for the comprehensive treatment of water channeling in the same type of offshore fractured low-permeability metamorphic rock reservoirs.

The development of low-porosity and low-permeability oil reservoirs in the Changqing oilfield presents a significant challenge due to the high injection pressure and limited space for profile control and pressure raising. Therefore, the synergistic profile control and flooding technology of polymer microspheres (PMs) and nanoemulsions is proposed, which makes use of the plugging performance of PMs and the pressure-reducing and oil-driving properties of nanoemulsions, with the objective of controlling the water-absorbing profile and increasing the recovery rate. The paper assessed the properties of nanoemulsions and PMs and evaluated their suitability for reservoirs. The construction parameters of profile control and flooding were optimized on an experimental basis. The experimental results demonstrate that the initial particle size of microspheres can more closely match the pore diameter of the formation and that the core exhibits a higher plugging rate after hydration and expansion. Moreover, the nanoemulsion can effectively reduce the interfacial tension, thereby exerting a beneficial effect on pressure reduction. The construction parameters of microspheres and nanoemulsions were optimized by alternately injecting them with 2:1 multistage plugs, and the injected pressure was lowered by 43%, with a high plugging rate and value-added recovery rate of up to 26.5%. Based on the experimental study, 8 well sets of field tests were conducted, and the results demonstrated that the efficiency rate of profile control reached 50%. Furthermore, the average injection pressure of the injected wells decreased by 1.7 MPa, the rise in water content decreased by 2.27%, and the petroleum production increased by 1110.2 tons. The validity period reached 162 days, indicating a positive application effect.

This paper is devoted to investigating two structural properties for a class of fractional-order switched and impulsive systems. The structural properties, i.e., observability and controllability, are explored mainly based on an algebraic approach. More specifically, firstly, according to the Laplace transform and mathematical induction, the general solution of such hybrid fractional-order systems is obtained over every impulsive interval. Next, applying the solution derived and relevant matrix theory, several necessary and sufficient controllability and observability criteria that take the form of a row of Gramian matrices are analytically established in terms of a deterministic impulse-switching time sequence. Resorting to the property of matrix Mittag-Leffler function, the developed controllability and observability Gramian criteria are further converted to some easy-test Kalman-type rank conditions. Finally, a numerical example illustrating the theoretical controllability and observability conditions is given.

In this article, the bipartite containment control problem is considered for discrete-time linear descriptor multi-agent systems (DMASs) with multiple dynamic leaders under fixed signed digraph topology, in which each leader can be autonomous or evolving dynamically via communicating with other leaders in its neighbourhood. Based upon the properties of the solutions to discrete-time modified generalised algebraic Riccati equations(MGAREs), three different types of distributed observer-based protocols utilising only available local information are devised. Algorithms for constructing the devised protocols are also presented. Moreover, sufficient conditions ensuring the bipartite containment of the DMASs are established. Finally, the effectiveness of the presented protocols is validated by numerical simulations.