Search Results (161)

A Lyapunov-based framework for stability analysis and synthesis of adaptive neural-network (NN) controllers for a class of uncertain second-order nonlinear systems (SNS) with bounded external perturbations and unmodelled dynamics is presented. Online learning is employed for the reconstruction of the plant nonlinearity with the use of a radial-basis-function (RBF) network whose weights are adapted using a direct adaptation law deduced from a single composite Lyapunov function. The proposed controller couples the weight update to a persistent robustifying action, while the closed-loop stability is guaranteed throughout the learning transient, in contrast to schemes that guarantee stability after learning has converged. Using a composite Lyapunov function in the filtered tracking error and the weight-estimation error, we prove that all closed-loop signals are uniformly ultimately bounded (UUB) and that the tracking error converges to an explicitly characterized residual set whose radius is governed by the network reconstruction accuracy, the disturbance bound and the design gains. A $\sigma$-modification ensures parameter boundedness without persistency of excitation, and a robustness theorem shows that bounded parametric perturbations of the plant preserve stability and enlarge the ultimate bound only gradually (a graceful degradation, rather than a loss of the guarantee). The open-loop plant (a forced double-well Duffing oscillator) is characterized by means of equilibrium and Jacobian analyses. A bifurcation diagram and the largest Lyapunov exponent are presented, which show a chaotic regime (with ${\lambda }_1\approx 0.17$). Numerical experiments indicate that the proposed controller is able to suppress the chaotic motion with a small value of the ultimate bound, and maintain a smooth reference motion with a small and constant RMS error of order ${10}^{-3}$, which is approximately 26 times less than the RMS error obtained with a tuned fixed-gain baseline, and the theoretical dependence of the ultimate bound on the disturbance and the design gains is confirmed by sensitivity sweeps. Full article

Seafloor undulations are common on continental shelves and slopes, but their origin is often difficult to determine because similar ridge-and-trough morphologies may originate from depositional processes, post-depositional deformation, or a combination of both. This study investigates seafloor undulations offshore the Sinni River mouth, southern Italy, to assess their depositional, deformational, or hybrid origin, using high-resolution bathymetry, sparker seismic profiles, sub-bottom profiler data, and morphometric analysis. Bathymetric data reveal a seaward-deepening shelf-to-slope system affected by submarine canyons, troughs, slide scarps, crests/ridge, and fields of undulated seafloor. Seismic profiles show that the undulations are mainly developed within the shallow sedimentary wedge, where continuous to gently wavy reflectors pass laterally and downslope into sectors characterized by reduced reflector continuity, local reflector bending, minor reflector offsets, low-angle reflector surfaces, disturbed sediment bodies, vertical to subvertical acoustic anomalies, and locally chaotic to semi-transparent seismic facies. Deeper stratified deposits are generally more continuous, indicating that deformation is mainly confined to the upper sedimentary succession. Morphometric analysis of 65 seabed features distinguishes broader seafloor undulations, characterized by longer wavelengths, low relief, and higher L/H ratios, from shorter, steeper deformation-related sectors with lower L/H ratios. The integrated evidence indicates that the Sinni undulation field is best interpreted as a hybrid depositional–deformational system. Broad, low-relief undulations record depositional accumulation and seabed reworking within the upper sedimentary wedge, whereas shorter, steeper, and more asymmetric sectors indicate localized post-depositional deformation, possibly favored by weak fine-grained deposits, gas/fluid-related acoustic disturbance, slope steepening, and local sediment remobilization. Full article

This study proposes a biomimetic multi-objective optimization framework for airflow distribution design in mine ventilation systems by integrating sensitivity screening with an improved multi-objective sparrow search algorithm (IMOSSA). The design problem is formulated with network-balance, critical branch airflow, fan-boundary, and adjustable-resistance constraints, while theoretical ventilation air power and pressure-drop disturbance are minimized as two conflicting objectives. Resistance-perturbation sensitivity analysis is used to identify high-impact adjustable branches and construct branch-specific search bounds, thereby forming a compact and physically feasible decision domain. Inspired by the foraging and vigilance behaviors of sparrow populations, IMOSSA is employed as a swarm-intelligence Pareto-search engine and integrates three strategies: chaotic opposition-based elite initialization to enhance initial population diversity, density-penalized external-archive guidance to maintain Pareto-front diversity, and stagnation-triggered differential–Cauchy perturbation to improve late-stage escape capability. ZDT and DTLZ benchmark functions verify the computational reliability of IMOSSA; in particular, on the multimodal ZDT4 function, IMOSSA achieves GD, IGD, and HV values of 0.0096, 0.0195, and 0.8479, respectively, indicating strong robustness in complex Pareto-front search. A mine ventilation network case further validates the engineering applicability of the proposed framework. For 13 adjustable branches, the compromise solution reduces model-computed ventilation air power from 313.61 kW to 281.11 kW, corresponding to a reduction of 10.36%, with a pressure-drop deviation of 354.15 Pa; the energy-priority solution further reduces the power to 256.91 kW, corresponding to a reduction of 18.08%. The results show that the proposed biomimetic multi-objective optimization framework can provide computable and interpretable Pareto decision support for airflow distribution design in mine ventilation systems. Full article

This paper presents a recurrent wavelet cerebellar brain controller (RWCBC) for synchronizing uncertain 4-D memristive hyper-chaotic master-slave systems affected by nonlinear uncertainty, memory-dependent dynamics, and bounded external disturbances. The proposed controller combines three elements: a linear stabilizing feedback term, a single-branch recurrent wavelet cerebellar approximator, and a smooth tanh-type robust compensation term. The recurrent association memory is used to exploit temporal information in the synchronization error, whereas the wavelet receptive fields improve local approximation of sharp nonlinear variations in the hyper-chaotic trajectory. The tanh compensation attenuates residual approximation errors and disturbances while avoiding the discontinuity of sign-based switching control. A Lyapunov-based analysis is developed to derive the adaptive learning law and to establish uniform ultimate boundedness (UUB) of the synchronization errors with an explicit ultimate bound. Numerical simulations on a 4-D memristive hyper-chaotic system indicate that the proposed RWCBC provides competitive tracking accuracy and a favorable accuracy-complexity trade-off compared with the considered WCMAC and FBELC baselines under the tested conditions. Full article

To address the inherent limitations of the traditional Animated Oat Optimization Algorithm (AOO), including poor uniformity of initial random population distribution and insufficient dynamic balance between global exploration and local exploitation, this paper proposes an Enhanced Animated Oat Optimization Algorithm (EAOO) incorporating multi-strategy improvements. First, the Sinusoidal chaotic map is introduced to replace the original random initialization method. Leveraging the ergodicity and uniformity of chaotic sequences, the spatial distribution of the population is optimized, and the diversity of the initial population is significantly enhanced. Second, a nonlinear disturbance factor is embedded into the position update of leaders during both the exploration and exploitation phases, enabling dynamic and adaptive adjustment of the search range. This effectively balances the algorithm’s capabilities in global exploration and local exploitation. Finally, an adaptive t-distribution mutation operator, combined with a dynamic selection strategy, is integrated. The degrees of freedom are adaptively adjusted throughout the iterative process, allowing the algorithm to switch between global escape and local fine-search modes, thereby overcoming the premature convergence deficiency of the original algorithm. Simulation and comparative experiments are conducted based on the CEC2017 and CEC2020 benchmark function suites. Systematic evaluations are carried out from multiple perspectives, including optimization accuracy, convergence speed, and statistical significance. The experimental results demonstrate that the proposed EAOO achieves superior comprehensive performance across various complex function types—including unimodal, multimodal, hybrid, and composite functions—exhibiting higher optimization accuracy, faster convergence speed, and stronger robustness. Statistical tests further confirm the significant performance differences between EAOO and the compared algorithms. Furthermore, EAOO is applied to two typical constrained engineering optimization problems: welded beam design and pressure vessel design. The simulation results show that EAOO yields better structural design parameters and lower manufacturing costs, demonstrating outstanding practical value and broad application prospects in solving high-dimensional, nonlinear, constrained engineering optimization problems. Full article

Hypersonic air-breathing propulsion via the Oblique Detonation Wave Engine (ODWE) offers superior thermodynamic efficiency compared to conventional scramjets by utilizing a stationary oblique detonation wave (ODW). While fundamental research has predominantly focused on two-dimensional planar wedges, realistic applications feature axisymmetric conical configurations. Over a cone, radial Taylor–Maccoll (TM) compression decelerates the flow and, in the mixed flow regime, establishes a localized subsonic pocket near the cone surface. However, the unsteady structures, triple-point kinetics, and cellular evolution under the competing influences of stabilizing TM compression and destabilizing Prandtl–Meyer (PM) expansions induced by a finite-length cone remain poorly understood. To address this gap, high-resolution numerical simulations of axisymmetric conical ODWs on a finite cone (semi-cone angle θ = 49°) were conducted at an inflow Mach number of Ma₀ = 7.5 using OpenFOAM. The methodology solves the reactive Euler equations coupled with a single-step Arrhenius model and three levels of adaptive mesh refinement to resolve fine-scale wave structures. Numerical results reveal that the localized subsonic pocket completely obliterates the smooth ZND-like initiation zone typical of purely supersonic configurations. Within this subsonic channel, acoustic disturbances propagate upstream against the bulk flow at a relative velocity of c − u, bypassing the supersonic wave-blocking effect to continuously impinge upon the detonation front. This acoustic feedback loop disrupts shock–reaction coupling, accelerating wave front bifurcation into single triple-point, dual triple-point, and PM-affected segments. Shock polar analysis validates that upstream-facing triple points exhibit greater shock strength, driving slow upstream migration and causing adjacent triple points to collide and reform into distinct, chaotic cell morphologies. Trajectory tracking confirms that the mixed flow cells are substantially larger and more chaotic than supersonic cases, directly reflecting amplified perturbations from the subsonic pockets. These insights provide crucial design criteria for optimizing cone angles to suppress irregular modes and stabilize conical ODWs. Full article

With the deep integration of artificial intelligence and big data, intelligent optimization algorithms have become key tools for solving many complex problems. However, as problem scale and complexity grow rapidly, the performance of traditional algorithms often faces significant challenges. The Teaching Learning Based Optimization algorithm has attracted widespread attention for its simple structure, few parameters, and high solution efficiency, and has been successfully applied across various engineering and scientific fields. Nevertheless, when dealing with high-dimensional, multimodal global optimization problems and real-world applications, the standard Teaching Learning Based Optimization still exhibits certain limitations, such as reduced accuracy of the optimal solution due to insufficient initial population diversity, and difficulty in escaping local optima caused by premature convergence. To address these issues, this paper proposes an Improved Teaching Learning Based Optimization algorithm. The improved ITLBO upgrades original TLBO from three perspectives: first, a population interaction strategy combining chaotic disturbance and Gaussian mutation is designed to enrich initial population diversity; second, bipolar cooperative search utilizing dynamic weighting of optimal and worst individuals balances global exploration and local exploitation to avoid premature convergence; third, oscillatory random mapping learning with sinusoidal oscillation factor periodically perturbs individuals to continuously replenish population diversity in iterations. Numerical results show that the proposed method exhibits superior convergence performance and stability on classical global optimization benchmarks. Furthermore, the algorithm is applied to practical cloud resource scheduling problems, and experimental outcomes verify that ITLBO improves solution accuracy by approximately one order of magnitude over original TLBO and reduces small-scale cloud scheduling cost by 12% while achieving preferable robustness. Full article

This paper addresses the finite-time adaptive synchronization control problem for a class of stochastic dynamical complex networks subject to unknown periodic coupling structures and bounded time-varying delays, a combination rarely tackled in the existing literature. To fill this gap, we develop a novel adaptive feedback control framework that integrates finite-time stochastic stability theory, differential inequality techniques, and adaptive learning laws. The paper investigates the concurrent estimation of unknown periodic coupling parameters through a period-based update law principally while enforcing finite-time synchronization in probability without prior knowledge of the coupling structure. The theoretical contributions include sufficient conditions ensuring stochastic finite-time synchronization, accompanied by an explicit upper bound on the expected settling time. Numerical simulations conducted on a five-node Sprott-O chaotic system validate the effectiveness and superiority of the proposed method, demonstrating that synchronization is attained within a time shorter than the theoretical estimate. In this paper, adaptive finite-time synchronization control of dynamical complex network with unknown periodical coupling structure and stochastic disturbances is investigated in detail from the perspective of improving convergence speed and lowering control costs. Basing on finite-time stochastic stability theory, differential inequality technique, and the adaptive feedback strategies, rigorous theoretical analysis establishes sufficient conditions to guarantee finite-time synchronization of the network. Furthermore, the unknown periodical coupling topological elements are estimated by proper adaptive update law simultaneously. Finally, numerical simulations are conducted to demonstrate the validity and superiority of the proposed control methodology. Full article

Flow-induced self-excited vibration may exhibit high-frequency numerical oscillations and chaotic-like responses in long-duration simulations due to strong nonlinearity and multimodal coupling. In this study, a two-node cable finite element model incorporating torsional degrees of freedom, nonlinear aerodynamic forces, and geometric nonlinearity is developed to evaluate the long-term computational performance of the Newmark average acceleration method and the Bathe composite integration scheme. Simulations are conducted for weakly nonlinear, transitional nonlinear, and near 1:1 internal resonance regimes. The results show that, as the degree of nonlinearity increases, the Newmark method produces more pronounced non-principal high-frequency components, a more scattered distribution of Poincaré points, and larger deviations from the expected principal-mode-dominated beating response. These observations indicate that, under the present model and discretization conditions, the chaotic-like response obtained by the Newmark method is strongly affected by non-principal high-frequency contamination. In contrast, the response computed by the Bathe method remains stably governed by the two dominant frequencies associated with the near-resonant beating mechanism. The results indicate that, for the long-duration nonlinear galloping problems considered in this study, appropriate algorithmic dissipation can reduce non-principal high-frequency disturbances and improve the interpretability of the numerical results. Full article

Grid-forming virtual synchronous generator control can improve the frequency-support capability of converter-interfaced systems. However, under successive disturbances and varying operating conditions, fixed inertia and damping settings often struggle to balance inertial response, oscillation suppression, and recovery speed. To address this issue, this paper develops an adaptive parameter coordination strategy for grid-forming virtual synchronous generators by using frequency deviation and rate of change of frequency as dynamic indicators. A piecewise regulation law is established to adjust virtual inertia and damping during different transient stages, while an improved parrot optimization algorithm is introduced for the offline coordinated tuning of the adaptive-law parameters. In the proposed optimizer, SPM-chaotic initialization, adaptive probability adjustment, and Cauchy-Gaussian hybrid mutation are incorporated to improve population diversity, convergence efficiency, and local refinement capability. Simulation results obtained in MATLAB/Simulink under successive disturbance events show that the proposed strategy achieves smaller frequency excursions, weaker secondary oscillations, and shorter settling times than fixed-parameter control and standard PO-based tuning. The results demonstrate that the proposed method can effectively enhance the dynamic support capability and disturbance adaptability of grid-forming virtual synchronous generators under complex operating conditions. Full article

In modern power-system load frequency control (LFC), proportional–integral–derivative (PID) controllers are widely used because of their simple structure and ease of implementation. However, the combined effects of communication delay and nonlinear constraints can degrade control performance. To address this issue, this paper proposes a model-constraint-aware optimal PID tuning method based on a Hybrid Differential Evolution–Chaotic Grey Wolf Optimizer (HDE-CGWO). First, a nonlinear LFC model incorporating data sampling, communication delay, governor deadband (GDB), and generation rate constraint (GRC) is established, and a PID-based LFC model is formulated. Next, an objective function based on the integral of time-weighted absolute area control error (ACE), namely ACE-based integral of time-weighted absolute error (ITAE), is constructed. Accordingly, quasi-opposition-based learning (QOBL), chaotic warm-up, Lévy flight, and differential evolution (DE) are incorporated into the standard Grey Wolf Optimizer (GWO) to develop an HDE-CGWO-based PID design scheme for LFC under sampled-data delay and nonlinear unit constraints. Finally, simulation studies are carried out on a multi-area LFC system. The resulting time-domain responses and statistical results show that, compared with standard GWO in the single-area test, HDE-CGWO reduces the ACE-based ITAE by about 43.3%. In the three-area system, the ACE-based ITAE is reduced by about 3.0% under step disturbances and about 1.4% under random disturbances compared with the warm-up Grey Wolf Optimizer (WGWO), indicating that the proposed method can reduce frequency deviations, attenuate post-disturbance oscillations, and accelerate the dynamic recovery process under the considered disturbance conditions. Full article

Aiming at the inherent limitations of the basic White Shark Optimizer (WSO), such as insufficient population diversity, unbalanced global and local search mechanisms, and weak convergence in the later stage, this paper proposes an Improved White Shark Optimizer (IWSO). The algorithm is improved from the following three aspects: Firstly, the Tent chaotic map is introduced to replace the traditional random initialization in the population initialization stage. Secondly, an adaptive nonlinear convergence factor and a dynamic inertia weight adjustment strategy are designed to focus on the fine search in the neighborhood of the optimal solution. Thirdly, the Levy flight perturbation mechanism and the elite opposition-based learning strategy are integrated to expand the search range and further accelerate the convergence speed. To verify the effectiveness and superiority of the IWSO algorithm, the CEC2017 test suite is selected for simulation experiments, and the IWSO is systematically compared with seven other representative swarm intelligence algorithms. The experimental results show that the IWSO is significantly superior to all comparison algorithms in multiple evaluation indicators, including minimum makespan, average convergence value, standard deviation, and successful convergence rate, on scheduling instances of different scales and difficulties. The convergence curve remains leading throughout the iteration process and shows a smoother convergence trend. The multi-strategy enhanced white shark optimizer proposed in this paper effectively overcomes the inherent defects of the basic algorithm, significantly improves the solution accuracy and convergence efficiency of the job shop scheduling problem, and has high theoretical research value and practical engineering application prospects. In the future, the multi-strategy improved White Shark Optimizer will be extended to multi-objective job shop scheduling, dynamic disturbance job shop scheduling, and large-scale production scheduling scenarios with numerous workpieces and machines. Full article

To address the uncertainty of the measurement noise covariance matrix in vehicle state estimation, this paper proposes a symmetry-aware extended Kalman filter optimized by a chaotic-gradient strategy. The symmetry-aware concept is introduced from the approximate mirror symmetry of vehicle lateral dynamics under left and right steering excitations. Under identical road adhesion and vehicle operating conditions, the yaw-rate and sideslip-angle responses should exhibit balanced statistical characteristics for positive and negative lateral motions. However, a fixed measurement noise covariance matrix may break this balance and lead to direction-dependent estimation bias or delayed convergence. To improve the statistical consistency of the estimation process, the proposed method adaptively tunes the measurement noise covariance matrix according to the innovation covariance mismatch. A chaotic search mechanism is first used to enhance global exploration, and a variable-step gradient method is then applied to refine the local optimal solution. Through the iterative combination of chaotic traversal and gradient-based refinement, the proposed observer improves the balance between model prediction and measurement correction under stochastic disturbances. The effectiveness of the proposed method is verified through CarSim and MATLAB/Simulink co-simulation. The results show that, compared with EKF, UKF, and AEKF benchmark observers, the proposed CG_EKF provides more accurate estimation of vehicle yaw rate and sideslip angle. Full article

In this article, we use truncated M-fractional derivatives to analyze the bifurcation and chaotic behavior of and traveling-wave solutions to the Zhiber–Shabat equation. By introducing truncated M-fractional derivatives, the equation exhibits richer dynamic properties. Based on phase diagram analysis and dynamical system theory, the bifurcation behavior of the equilibrium point of a two-dimensional dynamical system is discussed. At the same time, the dynamical behavior of a two-dimensional dynamical system with periodic disturbances is considered, revealing the complex chaotic phenomena of the system under specific parameters. A planar phase diagram, a three-dimensional phase diagram, a sensitivity analysis, and a maximum Lyapunov exponent diagram of the perturbed two-dimensional dynamical system were employed. Furthermore, various forms of accurate analytical solutions were obtained through traveling-wave transformation and numerical simulation. The three-dimensional, two-dimensional, density, and polar coordinates of the solutions were plotted using mathematical software. The results indicate that the fractional order and system parameters have a significant impact on the morphology and chaotic characteristics of the solution. This study provides new theoretical insights into the nonlinear dynamics of fractional-order Zhiber–Shabat equations. Full article

Landslides are widespread and highly destructive geological hazards that pose serious threats to infrastructure and densely populated areas. Conducting scientific and accurate predictions of landslide spatial distribution is therefore of great practical importance for supporting landslide prevention, risk management, and the reduction in casualties and economic losses. Landslides are driven by multiple variables, including elevation, road distance, river distance, slope and land use, with complex nonlinear interactions that traditional linear models cannot accurately capture. This study adopts a Categorical Boosting model (CatBoost) as the base prediction model, which demonstrates strong performance in capturing interactions among multiple variables and achieves relatively robust landslide spatial distribution predictions without complex feature engineering. However, CatBoost is highly sensitive to hyperparameters and difficult to manually optimize. Based on the Nutcracker Optimization Algorithm (NOA), which features an efficient search strategy, a multi-level improved Nutcracker Optimization Algorithm (COLNOA) is proposed to optimize its hyperparameters. The proposed algorithm integrates Circle Chaotic Mapping into the initial population construction of the NOA to generate two distinct populations and enables information exchange between them during the evolutionary process, thereby enhancing global search capability. In addition, Opposition-Based Learning and lateral mutation strategies are introduced to update inferior individuals in each iteration, improving their search capability. Based on these improvements, a COLNOA-CatBoost prediction model is developed. The proposed model is applied to a case study in Wanzhou District, Chongqing, China. The results show that the proposed model achieves a recall of 0.863, an F1-score of 0.860, and an accuracy of 0.865, outperforming baseline models such as decision trees. Compared with the original CatBoost model, recall, F1-score, and accuracy are improved by 34.8%, 35.0%, and 35.1%, respectively. The spatial prediction results indicate that high-risk landslide areas in Wanzhou District are mainly concentrated in regions such as Zouma Town, medium-risk areas in Xintian Town, low-risk areas in Fenshui Town, and very low-risk areas in Longju Town. Further analysis of terrain and landforms indicates that the high-risk areas for landslides in Wanzhou District are mainly related to steep slopes, deep river valleys, exposed or cut slopes at the foot of the slope, runoff convergence, and road excavation slopes. The extremely low and low-risk areas are mostly distributed in the middle and low mountain and hilly areas with relatively flat terrain, weak river cutting and engineering disturbance. This is consistent with the previous correlation analysis that the number of landslides increases with increasing slope and decreases with increasing elevation, distance from rivers, and distance from roads. Overall, the proposed model provides an effective approach for landslide spatial distribution prediction. Full article