Search Results (235)

This paper addresses the challenge of maximizing power extraction in offshore wind energy systems through the development of an enhanced maximum power point tracking (MPPT) control strategy. Offshore wind energy is inherently intermittent, leading to discrepancies between power generation and electricity demand. To address this issue, we propose three advanced control algorithms to perform a comparative analysis: sliding mode control (SMC), the Integral Backstepping-Based Real-Twisting Algorithm (IBRTA), and Feed-Back Linearization (FBL). These algorithms are designed to handle the nonlinear dynamics and aerodynamic uncertainties associated with offshore wind turbines. Given the practical limitations in acquiring accurate nonlinear terms and aerodynamic forces, our approach focuses on ensuring the adaptability and robustness of the control algorithms under varying operational conditions. The proposed strategies are rigorously evaluated through MATLAB/Simulink 2024 A simulations across multiple wind speed scenarios. Our comparative analysis demonstrates the superior performance of the proposed methods in optimizing power extraction under diverse conditions, contributing to the advancement of MPPT techniques for offshore wind energy systems. Full article

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

This paper focuses on the integrated modeling and disturbance rejection of the aerial work quadrotor with a robotic arm. First, to address the issues of model incompleteness and parameter uncertainty commonly encountered in traditional Newton–Euler-based modeling approaches for such a system, the Lagrangian energy conservation principle is adopted. By treating the quadrotor and robotic arm as a unified system, an integrated dynamic model is developed, which accurately captures the coupled dynamics between the aerial platform and the manipulator. The innovative approach fills the gap in existing research where model expressions are incomplete and parameters are ambiguous. Next, to reduce the adverse effects of the robotic arm’s motion on the entire system stability, a finite-time disturbance observer and a fast non-singular terminal sliding mode controller (FNTSMC) are designed. Lyapunov theory is used to prove the finite-time stability of the closed-loop system. It breaks through the limitations of the traditional Lipschitz framework and, for the first time at both the theoretical and methodological levels, achieves finite-time convergence control for the aerial work quadrotor with a robotic arm system. Finally, comparative simulations with the integral sliding mode controller (ISMC), sliding mode controller (SMC), and PID controller demonstrate that the proposed algorithm reduces the regulation time by more than 45% compared to ISMC and SMC, and decreases the overshoot by at least 68% compared to the PID controller, which improves the convergence performance and disturbance rejection capability of the closed-loop system. Full article

The quality of a grid-injected current in LCL-type grid-connected inverters (GCI) degrades under ultra-weak grid conditions, posing serious challenges to the stability of the GCI system. For this purpose, the sliding mode control (SMC) approach has been utilized to integrate DC energy seamlessly into the grid. The control performance of a GCI equipped with an LCL filter is greatly reduced when it is operating in a power grid with varying impedance and fluctuating grid voltages, which may result in poor current quality and possible instability in the system. A non-singular double integral terminal sliding mode (DIT-SMC) control is presented in this paper for a three-phase GCI with an LCL filter. The proposed method is presented in the α, β frame of reference without adopting an active or passive damping approach, reducing the computational burden. MATLAB/Simulink Version R2023b is leveraged to simulate the mathematical model of the proposed control system. The capability of the DIT-SMC method is validated through the OPAL-RT hardware-in-loop (HIL) platform. The effectiveness of the proposed method is first compared with SMC and integral terminal SMC, and then the DIT-SMC method is rigorously analyzed under resonance frequency events, grid impedance variation, and grid voltage distortions. It is demonstrated by the experimental results that the proposed control is highly effective in delivering a high-quality current into the grid, in spite of the simultaneous occurrence of power grid impedance variations in 6 mH and large voltage distortions. Full article

This paper addresses the trajectory tracking control problem of a six-degree-of-freedom Gough–Stewart Platform (GSP) by proposing a control strategy that combines a sliding mode (SM) controller with a rapid forward kinematics solution algorithm. The study first develops an efficient forward kinematics method that integrates Support Vector Regression (SVR) with the Levenberg–Marquardt algorithm, effectively resolving issues related to multiple solutions and local optima encountered in traditional iterative approaches. Subsequently, a SM controller based on the GSP’s dynamic model is designed to achieve high-precision trajectory tracking. The proposed control strategy’s robustness and effectiveness are validated through simulation experiments, demonstrating superior performance in the presence of model discrepancies and external disturbances. Comparative analysis with traditional PD controllers and linear SM controllers shows that the proposed method offers significant advantages in both tracking accuracy and control response speed. This research provides a novel solution for high-precision control in GSP applications. Full article

Underactuated systems, such as the Cart–Pole and Acrobot, pose significant control challenges due to their inherent nonlinearity and limited actuation. Traditional control methods often struggle to achieve stable and optimal performance in these complex scenarios. This paper presents a novel stable reinforcement learning (RL) approach for underactuated systems, integrating advanced exploration–exploitation mechanisms and a refined policy optimization framework to address instability issues in RL-based control. The proposed method is validated through extensive experiments on two benchmark underactuated systems: the Cart–Pole and Acrobot. In the Cart–Pole task, the method achieves long-term balance with high stability, outperforming traditional RL algorithms such as the Proximal Policy Optimization (PPO) in average episode length and robustness to environmental disturbances. For the Acrobot, the approach enables reliable swing-up and near-vertical stabilization but cannot achieve sustained balance control beyond short time intervals due to residual dynamics and control limitations. A key contribution is the development of a hybrid PPO–sliding mode control strategy that enhances learning efficiency and stabilities for underactuated systems. Full article

In this paper, a robust tracking control problem for underactuated underwater vehicles in horizontal motion is investigated. The presented control scheme that performs the trajectory tracking task is a combination of the backstepping technique and the integral sliding mode control method using the inertial quasi velocities (IQVs) resulting from the inertia matrix decomposition. Unlike many known solutions, the proposed approach allows not only trajectory tracking, but also, due to the fact that IQV includes dynamic and geometric model parameters, allows us to obtain additional information about changes in vehicle behavior during movement. In this way, some insight into its dynamics is obtained. Moreover, the control strategy takes into account model inaccuracies and external disturbances, which makes it more useful from a technical point of view. Another advantage of this work is to indicate problems occurring during the implementation of trajectory tracking in algorithms with a dynamics model containing a diagonal inertia matrix, i.e., without inertial couplings. The theoretical results are illustrated by simulation tests conducted on two models of underwater vehicles with three degrees of freedom (DOF). Full article

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

High-speed aircraft actuators are critical for precise control of aerodynamic surfaces, demanding fast response, accuracy, and robustness against uncertainties and disturbances. However, the complex nonlinear dynamics of these systems pose significant challenges for conventional control methods. Sliding mode control (SMC) offers robust performance and rapid transient response but is hindered by chattering, which can degrade performance. To address this, this paper proposes an innovative nonlinear control strategy that integrates global nonsingular terminal sliding mode control (NTSMC) for finite-time convergence with fuzzy logic-based adaptive gain tuning to mitigate chattering and suppress oscillations. A prototype actuator and experimental platform were developed to validate the approach. Experimental results demonstrate superior dynamic response and disturbance rejection compared to traditional methods, highlighting the effectiveness of the proposed control strategy. Full article

This paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, this design’s continuous structural changes introduce significant complexities in modeling its time-varying moment of inertia. To address this, we propose a control strategy that decouples dynamic motion from the evolving geometry, allowing for the development of a robust control model. A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. Extensive MATLAB 2016 simulations validate the proposed approach, demonstrating superior tracking accuracy in both fixed and variable arm-length configurations, achieving root mean square error values of 0.05 m (fixed arms), 0.06 m (variable arms, path 1), and 0.03 m (variable arms, path 2). Notably, the PSO-tuned SMC controller reduces tracking error by 30% (0.07 m vs. 0.10 m for PID) and achieves a 40% faster settling time during structural transitions. This improvement is attributed to the PSO-optimized SMC parameters that effectively adapt to the continuously changing inertia, concurrently minimizing chattering by 10%. This research advances the field of morphing UAVs by integrating continuous geometric adaptability with precise and robust control, offering significant potential for energy-efficient flight and navigation in confined spaces, as well as applications in autonomous navigation and industrial inspection. Full article

The permanent magnet synchronous motor (PMSM) is a critical device that converts kinetic energy into mechanical energy. However, it faces issues such as nonlinearity, time-varying uncertainties, and external disturbances, which may degrade the system control performance. To address these challenges, this paper proposes a prescribed performance model-free adaptive fast integral terminal sliding mode control (PP-MFA-FITSMC) method. This approach replaces conventional techniques such as parameter identification, function approximation, and model reduction, offering advantages such as quantitative constraints on the PMSM tracking error, reduced chattering, strong disturbance rejection, and ease of engineering implementation. The method establishes a compact dynamic linearized data model for the PMSM system. Then, it uses a discrete small-gain extended state observer to estimate the composite disturbances in the PMSM online, effectively compensating for their adverse effects. Meanwhile, an improved prescribed performance function and error transformation function are designed, and a fast integral terminal sliding surface is constructed along with a discrete approach law that adaptively adjusts the switching gain. This ensures finite-time convergence of the control system, forming a model-free, low-complexity, high-performance control approach. Finally, response surface methodology is applied to conduct a sensitivity analysis of the controller’s critical parameters. Finally, controller parameter sensitivity experiments and comparative experiments were conducted. In the parameter sensitivity experiments, the response surface methodology was employed to design the tests, revealing the impact of individual parameters and parameter interactions on system performance. In the comparative experiments, under various operating conditions, the proposed strategy consistently constrained the tracking error within ±0.0028 rad, demonstrating superior robustness compared to other control methods. Full article

Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching ${10}^{-4}$ rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection. Full article

This study investigates interface sliding behavior in composite I-steel–concrete beams reinforced with a composite material plate by analyzing various connection configurations combining shear stud connectors and adhesive bonding. The degree of composite action, governed by the shear stiffness at the steel–concrete interface, plays a critical role in structural performance. An analytical model was developed based on the elasticity theory and the strain compatibility approach, assuming constant shear and normal stress across the interface. Five connection modes were considered, ranging from fully mechanical (100% shear studs) to fully adhesive (100% bonding), as well as mixed configurations. The model was validated against finite element simulations, demonstrating strong agreement with relative differences between 0.3% and 10.7% across all cases. A parametric study explored the influence of key factors such as interface layer stiffness and composite plate reinforcement material on the overall interface behavior. The results showed that adhesive bonding significantly reduces slippage at the steel–concrete interface, enhancing bond integrity, while purely mechanical connections tend to increase interface slippage. The findings provide valuable guidance for designing hybrid connection systems in composite structures to optimize performance, durability, and construction efficiency. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

The conventional-speed Sliding Mode Controller (SMC) for ship PM propulsion motors, which employs exponential reaching laws and linear sliding surface functions, demonstrates susceptibility to oscillatory phenomena. To solve this problem, this paper proposes a saturation adaptive nonlinear integral sliding mode controller (SANI-SMC) which combines a nonlinear integral sliding surface function with an adaptive saturation gain reaching rate. The nonlinear integral sliding surface function improves the system responsiveness, and then enhances the stability and robustness of the system. The adaptive saturation gain reaching rate not only mitigates the chattering effect induced by the sign function in traditional exponential reaching rates, but also weakens the underlying oscillations. This approach effectively solves the overshoot problem inherent in traditional PI controllers, and has better anti-interference ability under sudden load variations. Finally, the proposed controller is experimentally verified based on an electric propulsion semi-physical experimental platform consisting of Rapid Control Prototyping (RCP), and compared with a Proportional–Integral (PI) controller and an SMC. Moreover, the integral absolute error (IAE), integral time-weighted absolute error (ITAE), and integral of the square value (ISV) metrics are calculated for the PI controller, SMC, and SANI-SMC based on experimental data collection. The results demonstrate that the SANI-SMC exhibits superior stability and robustness compared to both the PI controller and SMC. Full article