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This paper addresses the problem of maximum power point tracking of photovoltaic (PV) systems in the presence of model uncertainty as well as varying load and atmospheric conditions using techniques based on a Takagi–Sugeno fuzzy model. The proposed approach relies on the linear matrix inequality tool, Lambert W function, and the Newton–Raphson method. First, adopting a quadratic Lyapunov function, an active observer-based fuzzy non-parallel distributed compensation (non-PDC) controller is designed for asymptotic tracking of the desired reference input. Next, to subdue the impact of uncertainty on the PV system, the closed-loop nominal system is regarded as a reference model, and then the main control law is developed using an online lumped uncertainty estimator and keeping the nominal control law within the staple controller. This control law does not require that the bounds on uncertainties be known. The reference voltage is determined by a novel maximum power point-seeking algorithm that is organized based on the one-diode model of PV panel, Lambert W function, and Newton–Raphson method. Finally, simulations are performed for three scenarios to point out the merits and effectiveness of the proposed methodology in the presence of system uncertainties, environmental changes, and load variations. [ABSTRACT FROM AUTHOR]

In this paper, novel practical finite-time robust adaptive controllers are investigated for a 5-link single support phase (SSP) lower limb biped robot subjected to unknown physical and dynamical parameters, disturbances, and unknown input saturation. The developed adaptive approaches are based on a terminal sliding surface and a finite-time stability analysis to design controller and adaptation laws. The adaptive mechanisms deal with estimating unknown terms, the upper bound of the unknown term vector, or estimating the unknown limits of input saturation. Consequently, the proposed approach is applicable when the physical and dynamical parameters of the robot and the saturation input parameters are not known. In addition, the Lyapunov stability approach warranties that the tracking errors are robust finite-time uniformly ultimately bounded and provides the practical finite-time stability of the closed-loop system. Finally, numerical simulations are provided to show the feasibility, effectiveness, and performance enhancement of the proposed methods over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) is one of the controversial topics in power systems, especially in shipboard microgrids (SMG). In such applications, renewable energies such as wind, solar, or wave energies provide the power supply. However, changes in the weather conditions or loads can disrupt the frequency. To have a constant frequency in the presence of such turbulence in SMG, a fractional‐order dynamic output feedback controller (FDOFC) is proposed. As a result, the closed‐loop system is extended to a non‐identical fractional‐order system in the state space model. First, the non‐identical fractional‐order system is transformed into an identical one using the least common multiple techniques to determine control parameters. Subsequently, using a direct search algorithm, a considered initial design space is divided into smaller totally stable (TS) sub‐spaces. Then, among all TS controller parameters, the H∞$H_{\infty }$ controller is designed to reduce the effects of disturbances. The study is carried out on a practical SMG system to show the practicality of the proposed FDOFC. Finally, to evaluate the proposed method's efficiency, the SMG results are compared with the results obtained with μ synthesis, H∞$H_{\infty }$, Bilinear Matrix Inequality (BMI)‐based, and fuzzy fractional optimization methods. Compared to the state‐of‐the‐art approaches, more promising results are obtained for H∞$H_{\infty }$‐norm of the closed‐loop system about ten times smaller, that is, ||Δf||∞=0.00026$||\Delta f||_\infty = 0.00026$ which is such a negligible norm that concludes perfect disturbance rejection. Moreover, in more scenarios, the robustness of the designed framework is evaluated under parameter variations and time delays in communication. The results show that the maximum frequency deviation is 2.5×10−3$2.5 \times 10^{-3}$, which is very small. Therefore, the controller also works impressively under deviations of SMG system parameters, and communication time delays. [ABSTRACT FROM AUTHOR]