This paper presents a hybrid method for accurately predicting Global Horizontal Irradiance (GHI) over the following 24 hours to forecast energy production from a photo-voltaic system in a positive energy building. The input data is preprocessed using the Variational Mode Decomposition (VMD) method to extract wide-bandwidth features and decompose them into smooth modes focused on specific frequency ranges. The Salp Swarm Algorithm (SSA) is utilized to identify the optimal VMD parameters for accurate extraction. The data analysis is employed to identify the most critical modes of input features. The model's efficiency is further enhanced by performing a residual preprocessing step between the observed solar radiance data and the decomposed modes. The Stacking technique (ST) is employed to predict the 24-hour GHI modes and the residual, which are summed to reconstruct the final signal. The proposed method's performance is evaluated using the Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) metrics on three years of available data (2019–2022) in Rabat, and compared with the model based on raw data. The results show that the proposed method achieved promising results with an NRMSE of 1.35% and NMAE of 0.82% on a cloudy day.

This article deals with the problem of wheel slip control for 4-Wheel Drive (4WD) Electric Vehicles under significant uncertainties and disturbances. Based on a longitudinal four wheels model of the vehicle and the LuGre tire/road friction model, an observer-based integral sliding mode control (ISMC) is proposed. The stability analysis using the Lyapunov function of both the ISMC control law and the nonlinear observer is proved. The theoretical results validation and proposed controller robustness and practicability have been performed by CarSim-Simulink co-simulation. Finally, a comparison with traditional controllers revealed the supremacy of the offered controller.

In this paper an adaptive control scheme for antilock braking system (ABS) is developed based on LuGre tire-road friction model. ABS is the most important safety system during hard braking. So, it must be robust against the external disturbances, in particular the tire-road friction force variations caused by the road characteristics changes. For this aim, three main objectives are fixed ① Reference signal real time generation of the slip coefficient by using a neural network ② Estimation of the road conditions ③ Backstepping adaptive control law design to track the optimal slip value. The stability of the closed loop system is analyzed by using Lyapunov theory. The theoretical study results are evaluated by simulation under Matlab/Simulink, in different road conditions. The proposed controller supremacy is highlighted by comparing its performance to that of conventional ABS regulators.