Search Results (1,200)

To achieve precise trajectory tracking for surface vessels subject to unknown dynamics, strict physical limitations, and external disturbances, this paper proposes an Adaptive Fixed-Time Control Framework that ensures a deterministic response under full constraints. First, navigation safety is guaranteed by employing a Barrier Lyapunov Function (BLF) to strictly confine vessel position states, enabling constrained position tracking without requiring prior knowledge of the desired trajectory. Second, addressing the input constraint aspect of the “full constraints” problem, a fixed-time auxiliary system is introduced to compensate for nonlinearities induced by actuator saturation, thereby maintaining control feasibility. Central to this framework is the realization of a deterministic response; by incorporating fixed-time convergence theory, the controller guarantees that velocity tracking errors converge within a predefined time bound independent of initial conditions. Furthermore, an RBF neural network combined with adaptive techniques is utilized to estimate unknown dynamics and external disturbance bounds online, enhancing robustness and safety in realistic marine environments. Full article

The proposed methodology combines analytical control laws with adaptive mechanisms and machine-learning-assisted modules based on regression trees, random forests, and extreme gradient boosting (XGBoost). Machine learning models are employed to approximate unknown nonlinear dynamics, compensate disturbances, and adjust controller parameters, while the overall control structure is constrained by Lyapunov stability conditions. This ensures that the inclusion of data-driven components does not violate the fundamental requirement of system stability. The effectiveness of the proposed approach is evaluated through simulation experiments across three operating modes with varying degrees of nonlinearity and dynamic complexity. The results show that hybrid models incorporating ensemble machine learning methods improved performance compared with the analytical and adaptive baselines examined. XGBoost-based control achieves the lowest error values and the highest level of Lyapunov stability compliance (up to 99.3%). The main contribution of this study lies in the development of a unified synthesis framework in which machine learning is not used as a standalone control strategy but as a machine-learning-assisted support mechanism integrated into a theoretically grounded control architecture. The proposed approach provides a balance between adaptability, accuracy, and rigorous stability guarantees, suggesting potential applicability to simulation-based and offline-assisted control design tasks, while real-time embedded implementation requires additional computational optimization and validation. Full article

In order to improve the speed regulation accuracy, dynamic response and operation robustness of an automatic guided vehicle (AGV) in a complex road disturbance environment, this paper studies an adaptive AGV speed regulation system based on EKF state estimation on the basis of AGV dynamics modeling and adaptive control. Firstly, through the electrical-mechanical coupling modeling of the AGV drive system, state space construction and external unknown disturbance equivalent design, a unified electromechanical coupling simulation and physical verification environment is built, which lays a model foundation for the research of the speed control algorithm. Secondly, based on the optimal control model of PID and LQR with first-order lead compensation, an EKF adaptive speed regulation model is constructed by combining the extended Kalman filter and adaptive control to realize the online estimation and dynamic compensation of unknown disturbances. Finally, based on MATLAB/Simulink simulation platform and the STM32 embedded experimental platform, the rationality and robustness of the proposed speed control strategy are verified by speed-mutation conditions, load-disturbance condition and a physical verification experiment. The results show that the overshoot of the EKF adaptive control strategy is only 1.8%, which is 84.1% lower than that of PID control and 61.7% lower than that of LQR control. The rise time is 42% shorter than PID and 23% shorter than LQR. The recovery time under load disturbance is 58% shorter than that of PID and 31% shorter than that of LQR. EKF adaptive control is significantly better than PID and LQR in overshoot, rise time and control stability. The disturbance rejection ability and dynamic recovery speed are greatly improved, which can ensure the high robustness and smooth operation of the AGV speed control system under complex working conditions, effectively enhance the response and compensation ability of the system to sudden disturbances, and better meet the actual needs of AGV speed control in complex engineering scenarios. Full article

In this paper, an adaptive tracking control technique is proposed to stabilize the yaw angle of an unmanned surface vessel (USV) system subject to input saturation and external disturbances. Unlike existing saturation systems and disturbance rejection methods, the proposed control scheme simultaneously considers input saturation and unknown disturbances in a unified adaptive framework. First, an adaptive second-order system is presented to compensate for control input saturation. Such a saturated system can effectively reduce the amplitude of controller output. Then, an adaptive learning law is designed to estimate online the unknown upper bound of an external disturbance, removing the requirement for prior information on the disturbance. Based on Lyapunov stability theory, the convergence of the tracking error of closed-loop system and the uniform boundedness of all closed-loop signals are strictly proved. Furthermore, the transient performance of the real yaw tracking error is analyzed by considering the selected Lyapunov function, and an explicit upper bound of the transient error is derived for the first time in USV yaw control. Finally, simulations are carried out to illustrate the effectiveness of the proposed controller. 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

Since an unmanned aerial vehicle (UAV) system is nonlinear, underactuated, and strongly coupled, controlling UAVs is not a trivial task. Furthermore, when actuator faults and the transient performance when disturbances change are taken into account, the control problem becomes even more challenging. This paper inserts a nonlinear control law into the conventional equivalent-input-disturbance (EID) approach for reducing the fluctuation of the system output when disturbances change. Combing the nonlinear control law with the EID approach yields an adaptive EID approach. This paper applies the adaptive EID approach to address actuator faults and improve the transient performance in nonlinear UAVs for the first time, and proposes an adaptive EID-based fault-tolerant control strategy to tackle the aforementioned challenges. Actuator faults are transformed into parameter uncertainties. Four EID estimators are then designed to suppress nonlinearities, disturbances, and actuator faults simultaneously. Furthermore, exact linearization is employed to compensate for the nonlinearities inherent in quadrotor unmanned aerial vehicle (QUAV) systems. As a result, the stability conditions are analyzed using the concept of globally uniformly ultimate boundedness (GUUB), which simplifies the tuning of controller parameters. In addition, exact linearization helps enlarge the stability region. The effectiveness of the proposed approach is demonstrated through simulation results in both way-point tracking and periodic-trajectory tracking control. Full article

This paper extends the active disturbance rejection control (ADRC) approach to a class of nonlinear hybrid systems with uncertain fractional disturbances and unknown parameters, aiming to investigate the applicability of the ADRC approach in the presence of abrupt state changes and complex noise structures. By employing the fractional Wick–Itô–Skorohod (fWIS) integral, an extended state observer (ESO) together with an ESO-based control strategy is developed. It is shown that the resulting closed-loop hybrid stochastic system achieves mean-square stability under fractional noise. Furthermore, the proposed approach is generalized to enable the observation, tracking, and compensation of white noise disturbances with abrupt changes. Numerical simulations are presented to demonstrate the effectiveness of the proposed method. Full article

This article addresses the trajectory tracking control problem for quadrotor unmanned aerial vehicles (UAVs) subject to complex external disturbances and parameter uncertainties. To balance disturbance rejection with control signal smoothness, a practical predefined-time control scheme incorporating variable exponent coefficients (VEC) is proposed. First, a variable exponent practical predefined-time disturbance observer (VEC-PPTDO) is designed to dynamically estimate and compensate for unknown aerodynamic disturbances. Additionally, a practical predefined-time fractional-order sliding mode control (VEC-PPTFOSMC) scheme is developed, which fuses fractional-order calculus with VEC reaching laws to accelerate convergence and mitigate high-frequency chattering. Based on Lyapunov stability theory, the practical predefined-time stability of the entire closed-loop system is rigorously proven. Finally, comparative simulations under severe stochastic disturbances validate the proposed framework. Quantitative results demonstrate that the proposed scheme achieves a steady-state convergence time of 0.95 s. Compared to the integer-order benchmarks, the proposed method reduces the convergence time by an average of 15.2%, while decreasing the root mean square error (RMSE) and integral absolute error (IAE) by an average of 13.4% and 14.5%, respectively. Consequently, the proposed architecture enhances the dynamic tracking precision, control efficiency, and operational robustness of the quadrotor system. Full article

All real control systems are subject to uncertainties and disturbances, so feedback controllers have to be designed such that closed-loop systems possess the desired dynamic and steady-state responses in the presence of any allowable uncertainties and disturbances. It is common practice to address the effects of uncertainties and disturbances via bounding techniques and the almost disturbance decoupling approach, respectively. Linear, affine, and power growth conditions on uncertainties are required for almost disturbance decoupling. However, when these conditions are not satisfied, almost disturbance decoupling is not possible. A new concept called weak disturbance decoupling is introduced to mitigate the effects of disturbances. A weak disturbance decoupling problem is to find a feedback controller so that the close-loop system has the weak disturbance decoupling performance, that is, the closed-loop system is stable and the norm of the output is not greater than the sum of a positive constant and the product of the norm of the disturbance and a positive constant. Full article

Uncertain dynamic games have recently emerged as a rigorous and versatile framework for the analysis and synthesis of multi-agent decision-making processes in complex, stochastic, and dynamically evolving environments. By integrating foundational concepts from dynamic game theory with neural network-based function approximation techniques, these methodologies facilitate the development of adaptive, data-driven strategies for agents whose interactions unfold over time and are subject to both state and control constraints. Notwithstanding these advances, practical implementations are invariably influenced by model inaccuracies, exogenous disturbances, and parametric uncertainties, all of which may substantially impair system performance and jeopardize stability if left unmitigated. In this context, the present study examines dynamic game formulations defined on perturbed and uncertain system models, explicitly incorporating state and control constraints, with the objective of ensuring robustness and reliability in both competitive and cooperative settings. We consider a broad class of nonlinear dynamic games characterized by system dynamics affected by unknown disturbances and uncertain parameters. Within this framework, Dynamic Neural Networks (DNNs) are employed to approximate feasible solutions to the associated robust control problem, thereby enabling the characterization of $\epsilon$-Nash equilibria through learning mechanisms driven by worst-case trajectory realizations. A comprehensive theoretical analysis is developed to elucidate the effects of perturbations and uncertainties on equilibrium existence, convergence behavior, and closed-loop stability properties. Furthermore, sufficient conditions are established under which the neural learning dynamics ensure boundedness and convergence to approximate Nash or saddle-point equilibria, despite the presence of modeling imperfections. The proposed framework effectively synthesizes principles from robust control theory and learning-based game-theoretic approaches, yielding formal guarantees that are often absent in purely data-driven methodologies. Finally, numerical simulations conducted on representative dynamic game scenarios substantiate the efficacy of the proposed approach, demonstrating enhanced robustness relative to nominal neural game formulations. These findings contribute to the advancement of dependable dynamic game architectures, with potential applications spanning autonomous systems, robotics, and networked control systems operating under uncertainty. Full article

The increasing integration of photovoltaic (PV) systems and nonlinear loads intensifies harmonic distortion and reactive power imbalance in modern power networks. Conventional shunt active power filters (SAPFs) often employ control strategies that perform poorly under uncertain and dynamic grid conditions. This paper develops a hybrid sliding mode control with disturbance observer (SMC+DOB) technique for a PV-integrated SAPF to achieve effective harmonic mitigation, reactive power compensation, and enhanced system robustness. The study models the PV-SAPF system in MATLAB/Simulink (R2025b), where the SMC ensures robust current tracking, while the DOB estimates and suppresses unknown disturbances in real-time. The controller’s performance is evaluated under varying nonlinear and reactive load conditions, as per IEEE 519-2014 standards. Simulation results show that the proposed SMC+DOB scheme reduces total harmonic distortion (THD) by 96.7%—from 31.45% to 1.05%—while maintaining DC-link voltage stability and unity power factor. The integrated control architecture enhances the dynamic performance of SAPF, providing superior harmonic suppression, fast transient recovery, and improved grid stability for PV-connected systems. Full article

This paper investigates trajectory tracking and collision-avoidance problems for unmanned surface vehicles (USVs) in maritime sports support scenarios. These tasks require accurate tracking, disturbance rejection, safe motion around static and moving obstacles, and predictable transient performance within task-level time constraints. To address these requirements, an adaptive predefined-time sliding mode control (APTSMC) strategy is formulated for the considered CyberShip II-based USV tracking error system. A predefined-time sliding surface and reaching law are used to provide an explicit convergence-time design parameter for the nominal tracking subsystem, while an adaptive compensation mechanism estimates the unknown bound of lumped disturbances without requiring prior knowledge. To support collision avoidance, a velocity-modulated artificial potential field correction is incorporated as a reactive avoidance layer. The modulation term strengthens repulsion when the USV approaches an obstacle and reduces unnecessary deviation when the relative motion is safe. Numerical results in a constructed maritime sports boundary-tracking simulation scenario with multiple static and moving obstacles further demonstrate the potential effectiveness of the integrated framework in balancing tracking accuracy and collision avoidance safety. Full article

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment. Full article

To address the rigid temporal constraints and high-precision trajectory tracking requirements in modern industrial automation (e.g., high-speed pick-and-place or collaborative assembly), this paper proposes a novel composite control strategy for robotic manipulators that integrates Actor–Critic reinforcement learning with predefined-time sliding mode control (PTC-RLC). Existing predefined-time control (PTC) schemes usually rely on excessively large switching gains when dealing with strong disturbances, which easily triggers severe chattering in the system’s actuators and degrades dynamic performance. To this end, a novel predefined-time sliding surface based on artificial delay feedback is designed, ensuring that the position tracking error can strictly converge within a user-explicitly set time $T_c$ regardless of the system’s initial states, thereby significantly enhancing temporal determinism. Meanwhile, a reinforcement learning agent based on the Actor–Critic architecture is constructed to approximate and dynamically compensate for the system’s lumped unknown dynamics and external disturbances online, minimizing the control law’s reliance on large robust gains. Based on Lyapunov stability theory, the semi-global uniform ultimate boundedness of the closed-loop system is strictly proved. Numerical simulation results demonstrate that under severe operating conditions with parameter mismatches and time-varying disturbances, the proposed control strategy not only achieves high-precision and singularity-free trajectory tracking within the predefined time, but also effectively suppresses high-frequency chattering phenomena compared to the traditional non-singular terminal sliding mode control (NTSMC), outputting a smoother control torque and demonstrating strong potential for practical engineering implementations. Full article

Background/Objectives: The etiology of oral lichen planus (OLP) is unknown, and the treatment is palliative. Considering the possible influence of factors related to lifestyle on the etiopathogenesis and behavior of OLP, the aim of the present study was to investigate the association of sleep quality, nutritional profile, and salivary melatonin and cortisol levels with OLP. Methods: Thirty-two OLP patients and 31 controls completed the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale, and 24 h dietary recall survey. Saliva was collected to determine melatonin and cortisol levels by liquid chromatography coupled to mass spectrometry. Results: The OLP patients showed higher scores in the sleep disturbances component of PSQI (p = 0.021) and lower salivary melatonin levels (p = 0.015), whereas salivary cortisol did not differ between the groups (p = 0.402). The Control group had higher prevalence of coffee drinkers (p = 0.045), whereas the OLP group had higher consumption of protein (p = 0.011), lipids (p = 0.043), calories (p = 0.022), monounsaturated fat (p = 0.030), polyunsaturated fat (p = 0.007), saturated fat (p = 0.027), cholesterol (p = 0.041), iron (p = 0.014), zinc (p = 0.048), magnesium (p = 0.025), sodium (p = 0.008), and vitamin E (p = 0.016) compared to controls. Conclusions: The results suggest that OLP is associated with lifestyle factors related to sleep and diet, as well as with lower levels of salivary melatonin. Given the exploratory nature of the study, further research is needed to better understand these findings. Full article