Search Results (1,794)

This paper addresses the problem of cooperative coverage control for heterogeneous Autonomous Underwater Vehicle (AUV) swarms operating in complex underwater environments. The objective is to achieve optimal coverage of a target region while simultaneously ensuring collision avoidance—both among AUVs and with static obstacles—and satisfying the inherent dynamic constraints of the AUVs. To this end, we propose a hierarchical control framework that fuses Control Barrier Functions (CBFs) with consensus theory. First, addressing the heterogeneity and limited sensing ranges of the AUVs, a cooperative coverage model based on a modified Voronoi partition is constructed. A nominal controller based on consensus theory is designed to balance the ratio of task workload to individual capability for each AUV. By minimizing a Lyapunov-like function via gradient descent, the swarm achieves self-organized optimal coverage. Second, to guarantee system safety, multiple safety constraints are designed for the AUV double-integrator dynamics, utilizing Zeroing Control Barrier Functions (ZCBFs) and High-Order Control Barrier Functions (HOCBFs). This approach unifies the handling of collision avoidance and velocity limitations. Finally, the nominal coverage controller and safety constraints are integrated into a Quadratic Programming (QP) formulation. This constitutes a safety-critical layer that modifies the control commands in a minimally invasive manner. Theoretical analysis demonstrates the stability of the framework, the forward invariance of the safe set, and the convergence of the coverage task. Simulation experiments verify the effectiveness and robustness of the proposed method in navigating obstacles and efficiently completing heterogeneous cooperative coverage tasks in complex environments. Full article

This paper investigates the robust fault-tolerant control (FTC) problem for interval type-2 fuzzy systems (IT2FS) with simultaneous time-varying input and state delays. In order to more comprehensively capture system uncertainties, an Interval Type-2 (IT2) fuzzy model is constructed, which, compared to the conventional Interval Type-1 model, better captures the uncertainty information of the system. A premise-mismatched fault-tolerant controller is designed to ensure system stability in the presence of actuator faults, while providing greater flexibility in the selection of membership functions. In the stability analysis, a novel Lyapunov–Krasovskii functional is formulated, incorporating membership-dependent matrices and delay-product terms, leading to sufficient conditions for closed-loop stability based on linear matrix inequalities (LMIs). A numerical simulation and a practical physical model are used, respectively, to illustrate the effectiveness of the proposed method. Comparative experiments further reveal the impact of input delays and actuator faults on closed-loop performance, verifying the effectiveness and robustness of the designed controller, as well as the superiority of interval type-2 over interval type-1. Full article

This study addresses the issue of nonfragile state estimation for fractional-order memristive neural networks with time-varying delays under an adaptive event-triggered mechanism. Possible gain perturbations of the estimator are considered. A Bernoulli-distributed random variable is introduced to model the stochastic nature of gain fluctuations. The primary objective is to develop a nonfragile estimator that accurately estimates the network states. By means of Lyapunov functionals and fractional-order Lyapunov methods, two delay and order-dependent sufficient criteria are established to guarantee the mean-square stability of the augmented system. Finally, the effectiveness of the proposed estimation scheme is demonstrated through two simulation examples. Full article

An adaptive control strategy is developed and analyzed for trajectory tracking of mechanical systems subject to simultaneous model uncertainties and full-state constraints. To overcome the significant hurdle of guaranteeing both transient and steady-state performance within a user-defined time, a novel predefined-time adaptive neural network (NN) control scheme is proposed. By integrating predefined-time stability theory with a nonlinear mapping framework, a control scheme is developed to rigorously enforce full-state constraints while achieving predefined-time convergence. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown system dynamics, with adaptive laws designed for online learning. The nonlinear mapping is strategically incorporated to ensure that the full-state constraints are never violated throughout the entire operation. Furthermore, through Lyapunov stability theory, it is proved that all signals of the resulting closed-loop system are uniformly ultimately bounded, and most importantly, the trajectory tracking error converges to a small neighborhood of zero within a predefined time, which can be explicitly set regardless of initial conditions. Comparative simulation results on a representative mechanical system are provided to demonstrate the superiority of the proposed controller, showcasing its faster convergence, higher tracking accuracy, and guaranteed constraint satisfaction compared to conventional finite-time and adaptive NN control methods. Full article

A stability analysis of linear discrete-time neutral systems with both discrete and distributed delays is examined. To address this problem with accuracy, Lyapunov–Krasovskii candidates (LKCs) are formulated by heterogeneously splitting the whole delay interval into various parts; then, each part is assigned functionals with different weighting matrices. Then, new stability criteria are established and expressed in the form of linear matrix inequalities (LMIs) by combining a delay decomposition approach with an auxiliary function-based summation inequality method. These criteria provide a computationally efficient framework. Finally, several numerical examples are presented to confirm the validity and expanded feasibility region of our results when compared to existing approaches. Full article

Voltage source converter-based–high voltage direct current (VSC-HVDC) systems exhibit strong nonlinear characteristics that dominate their dynamic behavior under large disturbances, making large-signal stability assessment essential for secure operation. This paper proposes a large-signal stability analysis framework for VSC-HVDC systems. The framework combines a unified Takagi–Sugeno (T–S) fuzzy model with a model-free predictive control (MFPC) scheme to enlarge the estimated domain of attraction (DOA) and bring it closer to the true stability region. The global nonlinear dynamics are captured by integrating local linear sub-models corresponding to different operating regions into a single T–S fuzzy representation. A Lyapunov function is then constructed, and associated linear matrix inequality (LMI) conditions are derived to certify large-signal stability and estimate the DOA. To further reduce the conservatism of the LMI-based iterative search, we embed a genetic-algorithm-based optimizer into the model-free predictive controller. The optimizer guides the improved LMI iteration paths and enhances the DOA estimation. Simulation studies in MATLAB 2023b/Simulink on a benchmark VSC-HVDC system confirm the feasibility of the proposed approach and show a less conservative DOA estimate compared with conventional methods. Full article

We propose a novel design approach for pinning control of a dynamical network that achieves synchronization despite switching between arbitrary topologies. Unlike existing approaches, we consider weighted, directed, and even unconnected topologies as admissible connections that can be switched instantly. We present a selection algorithm that uses the current topology to identify a suitable set of nodes for control. Additionally, we consider a fixed pinning strategy to activate the required controllers to achieve synchronization, with their gains computed via adaptation laws based only on the neighbors of each pinned node. We derive sufficient conditions for the emergence of a stable synchronous state using common Lyapunov function theory and illustrate their efficacy through numerical simulations of networks that can switch instantaneously between arbitrary topologies. Full article

This paper addresses a recursive adaptive anti-lock braking (AB) control design problem for electro-hydraulic brake (EHB) systems subject to unknown tire–road-friction coefficients and disturbances. Compared with the relevant literature, the primary contributions are (i) the development of a novel nonlinear AB model integrated with a bond-graph-based EHB (BGEHB) system, and (ii) the proposal of an adaptive neural AB control approach incorporating a nonlinear disturbance observer (NDO). The AB and BGEHB models are unified into a single nonlinear braking model, with the wheel speed as the system output and the duty ratios of the BGEHB inlet and outlet valves as control inputs. To maintain an optimal slip ratio during braking, we design the NDO-based adaptive AB controller to regulate the wheel speed in a recursive manner. The designed controller incorporates a delay-compensation term to address the time-delay characteristics of the hydraulic system, employs a neural-network function approximator in the NDO and controller to compensate for the unknown tire–road-friction coefficient, and applies NDOs to compensate for disturbances due to the vehicle motion and BGEHB dynamics. The stability of the proposed control scheme is established via the Lyapunov theory, and a simulation comparison is presented to demonstrate the effectiveness of the proposed design approach. Full article

This work explores the qualitative dynamics of the modified unstable time-fractional nonlinear Schrödinger equation (mUNLSE), a model applicable to nonlinear wave propagation in plasma and optical fiber media. By transforming the governing equation into a planar conservative Hamiltonian system, a detailed bifurcation study is carried out, and the associated equilibrium points are classified using Lagrange’s theorem and phase-plane analysis. A family of exact wave solutions is then constructed in terms of both trigonometric and Jacobi elliptic functions, with solitary, kink/anti-kink, periodic, and super-periodic profiles emerging under suitable parameter regimes and linked directly to the type of the phase plane orbits. The validity of the solutions is discussed through the degeneracy property which is equivalent to the transmission between the phase orbits. The influence of the fractional derivative order on amplitude, localization, and dispersion is illustrated through graphical simulations, exploring the memory impacts in the wave evolution. In addition, an externally periodic force is allowed to act on the mUNLSE model, which is reduced to a perturbed non-autonomous dynamical system. The response to periodic driving is examined, showing transitions from periodic motion to quasi-periodic and chaotic regimes, which are further confirmed by Lyapunov exponent calculations. These findings deepen the theoretical understanding of fractional Schrödinger-type models and offer new insight into complex nonlinear wave phenomena in plasma physics and optical fiber systems. Full article

This paper investigates the global polynomial synchronization (GPS) problem for quaternion-valued neural networks (QVNNs) featuring proportional delay, parameter uncertainty, and external disturbance. A combined approach of sliding mode control (SMC) and a non-separation strategy is adopted to achieve this goal. First, an integral-type sliding surface is designed for the system. Then, by constructing a delay-free Lyapunov functional and leveraging the properties of the quaternion vector norm and inequality techniques, sufficient conditions are derived to achieve GPS for the sliding mode dynamics. Furthermore, both a SMC law and an adaptive SMC law are designed, with a reachability analysis confirming that the system trajectories reach the predefined sliding surface in finite time. Finally, numerical examples with graphical analysis are provided to verify the obtained results, along with their application in color image pattern storage and retrieval. Full article

This study explores finite-time synchronization (FTS) in fractional-order quaternion-valued neural networks (FQVNNs) characterized by discontinuous activation functions and uncertainties in parameters. Initially, leveraging the properties of the Mittag-Leffler function along with fractional-order (F-O) delayed differential inequalities, a novel finite-time stability theorem for F-O systems is established, building upon previous research findings. Next, based on norm definitions, two state feedback controllers employing quaternion 1-norm and quaternion 2-norm are devised to ensure FTS for the system under consideration. Following this, by utilizing differential inclusion theory, examining the quaternion sign function, employing advanced inequality methods, applying principles of F-O differential equations, and using the Lyapunov functional approach, new criteria for achieving FTS in FQVNNs are formulated. Additionally, precise estimates for the settling time are presented. In conclusion, two carefully designed numerical examples are included to corroborate the theoretical results derived. Full article

Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To address this, this paper proposes a safety-critical control framework built on a Hamiltonian neural differential model (HDM). The HDM formulates the quadrotor dynamics under a Hamiltonian structure over the $\mathit{SE}\left(3\right)$ manifold, with explicitly optimizable inertia parameters and a neural network-approximated control input matrix. This yields a neural ordinary differential equation (ODE) that is solved numerically for state prediction, while all parameters are trained jointly from data via gradient descent. Unlike black-box models, the HDM incorporates physical priors—such as $\mathit{SE}\left(3\right)$ constraints and energy conservation—ensuring a physically plausible and interpretable dynamics representation. Furthermore, the HDM is reformulated into a control-affine form, enabling controller synthesis via control Lyapunov functions (CLFs) for stability and exponential control barrier functions (ECBFs) for rigorous safety guarantees. Simulations validate the framework’s effectiveness in achieving safe and stable tracking control. Full article

Multi-UAV (unmanned aerial vehicle) target enclosing control is one of the key technologies for achieving cooperative tasks. It faces limitations in communication resources and task framework separation. To address this, a distributed cooperative control strategy is proposed based on dynamic time-varying formation description and event-triggering mechanism. Firstly, a formation description method based on a geometric transformation parameter set is established to uniformly describe the translation, rotation, and scaling movements of the formation, providing a foundation for time-varying formation control. Secondly, a cooperative architecture for adaptive target enclosing tasks is designed. This architecture achieves an organic combination of formation control and target enclosing in a unified framework, thereby meeting flexible transitions between multiple formation patterns such as equidistant surrounding and variable-distance enclosing. Thirdly, a distributed dynamic event-triggered cooperative enclosing controller is developed. This strategy achieves online adjustment of communication thresholds through internal dynamic variables, significantly reducing communication while strictly ensuring system performance. By constructing a Lyapunov function, the stability and Zeno free behavior of the closed-loop system are proven. The simulation results verify this strategy, showing that this strategy can significantly reduce communication frequency while ensuring enclosing accuracy and formation consistency and effectively adapt to uniform and maneuvering target scenarios. Full article

This paper considers the solution behavior and dynamical properties of the variable-order fractional Newton–Leipnik system defined via Liouville–Caputo derivatives of variable order. In contrast to integer-order models, the presence of variable-order fractional operators in the Newton–Leipnik structure enriches the model by providing memory-dependent effects that vary with time; hence, it is capable of a broader and more flexible range of nonlinear responses. Numerical simulations have been conducted to study how different order functions influence the trajectory and qualitative dynamics: clear transitions in oscillatory patterns have been identified by phase portraits, time-series profiles, and three-dimensional state evolution. The work goes further by considering the development of bifurcations and chaotic regimes and stability shifts and confirms the occurrence of several phenomena unattainable in fixed-order and/or integer-order formulations. Analysis of Lyapunov exponents confirms strong sensitivity to the initial conditions and further details how the memory effects either reinforce or prevent chaotic oscillations according to the type of order function. The results, in fact, show that the variable-order fractional Newton–Leipnik framework allows for more expressive and realistic modeling of complex nonlinear phenomena and points out the crucial role played by evolving memory in controlling how the system moves between periodic, quasi-periodic, and chaotic states. Full article

Nonlinear single-input single-output (SISO) systems operating under parametric uncertainty often exhibit bifurcations, multistability, and deterministic chaos, which significantly limit the effectiveness of classical linear, adaptive, and switching control methods. This paper proposes a novel synthesis framework for self-organizing control systems based on catastrophe theory, specifically within the class of elliptic catastrophes. Unlike conventional approaches that stabilize a predefined system structure, the proposed method embeds the control law directly into a structurally stable catastrophe model, enabling autonomous bifurcation-driven transitions between stable equilibria. The synthesis procedure is formulated using a Lyapunov vector-function gradient–velocity method, which guarantees aperiodic robust stability under parametric uncertainty. The definiteness of the Lyapunov functions is established using Morse’s lemma, providing a rigorous stability foundation. To support practical implementation, a data-driven parameter tuning mechanism based on self-organizing maps (SOM) is integrated, allowing adaptive adjustment of controller coefficients while preserving Lyapunov stability conditions. Simulation results demonstrate suppression of chaotic regimes, smooth bifurcation-induced transitions between stable operating modes, and improved transient performance compared to benchmark adaptive control schemes. The proposed framework provides a structurally robust alternative for controlling nonlinear systems in uncertain and dynamically changing environments. Full article