PV (Photovoltaic) cells have nonlinear current-voltage (I − V) and power-voltage (P − V) characteristics with a distinct maximum power point (MPP) that entirely depends on the ambient meteorological conditions (i.e. solar irradiance and temperature). Hence, to continuously extract and deliver the maximum possible power from the PV system, under given meteorological conditions, the maximum power point tracking (MPPT) control strategy needs to be formulated that continuously operates the PV system at its MPP. To achieve this goal, a hybrid nonlinear, very fast and efficient MPPT control strategy, based on the robust integral backstepping (RIB) control, is formulated in this research article. The simulation testbed comprises a standalone PV array, a non-inverting buck-boost (NIBB) DC-DC power converter, a purely resistive and a dynamic load (sound system). The proposed MPPT control scheme consists of two loops, where the first loop generates the real-time offline reference peak power voltage through an adaptive neuro-fuzzy inference system (ANFIS) network, which is then utilized in the second loop as a set-point value for generating a control signal and then forcing the PV system to be operated at this set-point by continuously adjusting the duty ratio of the power converter. This control strategy exhibits no overshoot, fast convergence, good transient response, fast rising and settling times and minimum output tracking error. The MATLAB/Simulink platform is used to test the performance of the proposed MPPT strategy against varying meteorological conditions, plant current and voltage faults and plant parametric uncertainties. To validate the superiority of the proposed control strategy, a comparative analysis of the proposed control strategy is presented with the nonlinear backstepping (B), integral backstepping controller (IB) and conventional PID and P&O based MPPT controllers.

In this research article, a fast and efficient hybrid Maximum Power Point Tracking (MPPT) control technique is proposed for photovoltaic (PV) systems. The method combines two phases—offline and online—to estimate the appropriate duty cycle for operating the converter at the maximum power point (MPP). In the offline phase, temperature and irradiance inputs are used to compute the real-time reference peak power voltage through an Adaptive Neuro-Fuzzy Inference System (ANFIS). This estimated reference is then utilized in the online phase, where the Robust Backstepping Super-Twisting (RBST) controller treats it as a set-point to generate the control signal and continuously adjust the converter’s duty cycle, driving the PV system to operate near the MPP. The proposed RBST control scheme offers a fast transient response, reduced rise and settling times, low tracking error, enhanced voltage stability, and quick adaptation to changing environmental conditions. The technique is tested in MATLAB/Simulink under three different scenarios: continuous variation in meteorological parameters, sudden step changes, and partial shading. To demonstrate the superiority of the RBST method, its performance is compared with classical backstepping and integral backstepping controllers. The results show that the RBST-based MPPT controller achieves the minimum rise time of $0.018$$\mathrm{s}$, the lowest squared error of $0.3015$$\mathrm{V}$, the minimum steady-state error of $0.29$%, and the highest efficiency of $99.16$%.

Land Surface Temperature (LST) is a critical parameter for urban climate analysis, photovoltaic (PV) planning, and environmental monitoring, yet its effective use is hindered by the coarse spatial resolution of thermal sensors like MODIS. This study introduces a two-stage hierarchical Convolutional Neural Network (CNN) framework that integrates multi-sensor satellite data (MODIS, ASTER, Sentinel-2) to generate fine-scale LST products and quantitatively assess scaling effects across heterogeneous landscapes. In the first stage, MODIS LST (1 km) was downscaled to 360 m, 270 m, and 90 m using Sentinel-2–derived scaling factors and validated against ASTER LST. In the second stage, ASTER LST (90 m) was downscaled to 60 m and 30 m and validated with Landsat-based LST. The framework employed spectral indices, topographic parameters, texture features, and sub-pixel land-cover fractions as scaling factors, capturing both spatial and spectral heterogeneity. Comparative evaluation against XGBoost and DisTrad revealed that CNN consistently achieved the highest determination coefficients (R2 = 0.69–0.87) and the lowest RMSE (1.94–2.34 K) and MAE (1.49–1.8 K) values, confirming its superior capacity to model complex nonlinear thermal relationships. Scaling-effect analysis demonstrated that while accuracy naturally decreases with finer resolutions, the CNN model exhibits strong scale stability and resilience to error propagation, outperforming traditional regression and machine-learning approaches. This hierarchical deep-learning design establishes a new paradigm for multi-sensor LST reconstruction, enabling accurate, scalable, and spatially coherent thermal mapping across diverse terrains. The proposed framework offers a generalizable solution for high-resolution thermal monitoring, PV site optimization, and climate-adaptive urban planning.