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The sun serves as the primary energy source, providing our planet with the essential energy for sustaining life. To efficiently harness this energy, photovoltaic cells, commonly known as PV cells, are employed. These cells convert the solar energy they receive into electrical energy. The operational point of the solar cell, delivering maximum output power, is referred to as the maximum power point (MPP). However, as light availability and temperature fluctuate throughout the day, the MPP also varies accordingly. To maintain constant operation at the MPP, Maximum Power Point Tracking (MPPT) algorithms are employed to trace the MPP during module operation. These algorithms can be categorized into four groups: classical, intelligent, optimization, and hybrid, based on the tracking algorithm utilized. Each MPPT algorithm, existing in these categories, comes with its own set of advantages and limitations. This paper extensively reviews fifteen algorithms categorized under different groups. The review concludes with a comparative analysis of these algorithms, considering various parameters such as cost, complexity, tracking accuracy, and sensed parameters in a succinct manner. The paper focuses on elucidating the necessity of MPPT algorithms, their classification as per existing literature, and a comparative assessment of the studied MPPT algorithms. This comprehensive review aims to address advancements in this field, paving the way for further research. [ABSTRACT FROM AUTHOR]

In this article, the issue of finite-time boundedness is investigated for a class of uncertain periodic piecewise systems subject to time-varying input/state delays and external disturbances. The main aim of this article is to design an H ∞ observer-based control protocol for guaranteeing the resulting closed-loop system to be finite-time bounded. The state dynamics of the system are not always directly measurable, so that a system states are reconstructed by constructing a periodic piecewise observer system to get the required result. Subsequently, the Lyapunov–Krasovskii functional by the decomposition of periodic positive-definite matrices is built and in conjunction with convex optimisation technique and extended Wirtinger's integral inequality, the sufficient conditions are derived to assure the finite-time boundedness of the closed-loop system. In particular, the corresponding periodic piecewise time-varying controller and observer gain matrices can be obtained as solutions to a set of derived sufficient linear matrix inequality-based conditions. Finally, the efficacy of the proposed control design is examined by utilizing two numerical examples with simulation results including a vibration system model. [ABSTRACT FROM AUTHOR]

This paper is focused on designing observer‐based decentralized memory feedback controller for ensuring the asymptotic mean square stability of the large‐scale systems with minimum H∞ performance index. Precisely, the unknown interconnection between each subsystems of a large‐scale system is assumed to satisfy quadratic bounds, and measured output is quantized by a logarithmic quantizer. Also, the signals are transmitted through the actuator component wherein the occurrence of fault is indispensable. Thus, the impact of faults in actuator is considered in control design to tolerate the fault effects and also for ensuring robust performance. A state space representation of the system is formulated to reconstruct the unmeasurable states via the available informations of input/output dynamics. Based on the designed observer, a decentralized memory feedback controller is developed. Specifically, in terms of linear matrix inequalities, the stability conditions are derived and which are sufficient to guarantee the desired result. At last, simulations are carried out for two numerical examples to validate the potential of the theoretical result. [ABSTRACT FROM AUTHOR]