This paper proposes a novel nonlinear speed control method for permanent magnet synchronous motors that enhances their robustness and tracking performance. This technique integrates a sliding-mode disturbance observer and variable-gain fractional-order super-twisting sliding-mode control within a vector-control framework. The proposed control scheme employs a sliding-mode control method to mitigate chattering and improve dynamics by implementing fractional-order theory with a variable-gain super-twisting sliding manifold design while regulating the speed of the considered motor system. The aforementioned observer is suggested to enhance the control accuracy by estimating and compensating for the lumped disturbances. The proposed methodology demonstrates its superiority over other control schemes such as traditional sliding-mode control, super-twisting sliding-mode control, and the proposed technique. MATLAB/Simulink simulations and real-time implementation validate its performance, showing its potential as a reliable and efficient control approach for the system under study in practical applications.

his paper addresses the challenge of optimizing photovoltaic (PV) system performance under rapidly changing environmental conditions, such as fluctuations in solar irradiance and temperature. To enhance energy extraction and regulate power delivery, a non-inverting DC-DC buck-boost converter is employed. Motivated by the nonlinear behavior and uncertainty inherent in PV systems, we propose a novel control strategy—Neuro-Adaptive Third-Order Sliding Mode Control (NATOSMC)—that ensures accurate and robust reference tracking. The control scheme integrates a Radial Basis Function Neural Network (RBFNN) for generating the reference signal, and a High-Gain Differentiator (HGD) to estimate system states using differential flatness theory. To ensure optimal performance, adaptive laws adjust the control gains in real-time, with parameter optimization performed via the Grey Wolf Optimizer (GWO). Stability is rigorously guaranteed through Lyapunov analysis. Both simulation and experimental results validate the proposed method, demonstrating superior performance in terms of tracking precision, dynamic response, and robustness compared to conventional controllers. Key results include a settling time of 0.014 s, rise time of 0.012 s, overshoot of 0.05%, and tracking efficiency of 98.71%. The controller also achieves improved performance indices: Integral Absolute Error (IAE) of 111.04, Integral Square Error (ISE) ofhis paper addresses the challenge of optimizing photovoltaic (PV) system performance under rapidly changing environmental conditions, such as fluctuations in solar irradiance and temperature. To enhance energy extraction and regulate power delivery, a non-inverting DC-DC buck-boost converter is employed. Motivated by the nonlinear behavior and uncertainty inherent in PV systems, we propose a novel control strategy—Neuro-Adaptive Third-Order Sliding Mode Control (NATOSMC)—that ensures accurate and robust reference tracking. The control scheme integrates a Radial Basis Function Neural Network (RBFNN) for generating the reference signal, and a High-Gain Differentiator (HGD) to estimate system states using differential flatness theory. To ensure optimal performance, adaptive laws adjust the control gains in real-time, with parameter optimization performed via the Grey Wolf Optimizer (GWO). Stability is rigorously guaranteed through Lyapunov analysis. Both simulation and experimental results validate the proposed method, demonstrating superior performance in terms of tracking precision, dynamic response, and robustness compared to conventional controllers. Key results include a settling time of 0.014 s, rise time of 0.012 s, overshoot of 0.05%, and tracking efficiency of 98.71%. The controller also achieves improved performance indices: Integral Absolute Error (IAE) of 111.04, Integral Square Error (ISE) ofT 11.42×10>⁵, Integral Time Absolute Error (ITAE) of 3.19, and Integral Time Square Error (ITSE) of 1.21×10⁴. These results highlight the efficacy of NATOSMC in enhancing the control and reliability of PV energy systems.

Introducing an innovative approach, this study presents the Neuro-Adaptive Terminal Sliding Mode Control (NATSMC) for achieving Maximum Power Point Tracking (MPPT) within Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Conversion Systems (WECS). The proposed strategy effectively addresses performance challenges in the presence of uncertain disturbances, aligning seamlessly with the inherent characteristics of WECS. To realize this approach, we integrate an enhanced Finite-Time Performance Function (FTPF) with a hyperbolic tangent function, forming a Fast Terminal Sliding Mode (FTSM) surface. This configuration ensures rapid convergence with minimal overshoot. Notably, the advanced control mechanisms embedded in this technique contribute to the optimization of power generation and system stability in the dynamic context of wind dynamics. The methodology unfolds through several key steps. Initially, we transform the system model into an input-output format through coordinate transformation, enhancing its suitability for control applications. Next, we introduce a Self-Recurrent Wavelet Neural Network (SRWNN) for Lie derivative estimation, offering a robust and dynamic approach to harness wind energy effectively. Further enhancing precision and robustness, the proposed control law integrates a Variable-Gain Robust Exact Differentiator (VG-RED) to estimate derivatives under uncertain bounded disturbances. This observer minimizes high-frequency chattering, thereby improving control stability. A rigorous Lyapunov stability analysis confirms the uniform boundedness of closed-loop signals and strengthens the overall dynamic control system. Finally, extensive simulations are conducted within the WECS framework to validate the proposed approach. These simulations demonstrate significantly improved MPPT accuracy, reduced response times, and enhanced control efficiency when compared favorably with conventional control schemes found in existing literature.

This paper presents a Robust Fractional-Order Sliding Mode Control (FOSMC) with Nonsingular Integral Terminal Dynamics, integrated with Densely Connected Convolutional Networks (DenseNet) for Lie Derivatives Estimation, to achieve Maximum Power Point Tracking (MPPT) in Wind Energy Conversion Systems (WECS) based on Permanent Magnet Synchronous Generators (PMSG). The proposed method effectively addresses the challenges of nonlinear dynamics, uncertain wind conditions, and chattering effects, which are common in traditional control approaches. The core innovation lies in fractional-order sliding mode control, which enhances convergence speed and robustness while ensuring finite-time stability. Unlike classical Sliding Mode Control (SMC), the proposed Nonsingular Terminal Sliding Mode (NTSM) formulation eliminates singularities and improves tracking accuracy. Additionally, to overcome inaccuracies in numerical differentiation, a Densely Connected Convolutional Neural Network (DenseNet) is employed to estimate higher-order Lie derivatives, providing real-time system state approximation and improving control precision. A rigorous Lyapunov stability theorem guarantees the finite-time convergence of the considered system. Extensive MATLAB/Simulink simulations validate the effectiveness of the proposed control law by comparing it with the existing classical controllers. The results demonstrate superior MPPT efficiency, faster transient response, reduced chattering, and enhanced robustness under varying wind conditions.