Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

In this study, an Adaptive Backstepping Sliding Mode Controller (ABSMC) is introduced based on the Radial Basis Function (RBF) neural network and a fuzzy logic modifier. The proposed method is used to control a Dual-Arm Robot (DAR) – a nonlinear structure with unstable parameters and external disturbances. The control aims to track the motion trajectory of both arms in the flat surface coordinate within a short time, maintaining stability, and ensuring that the tracking error converges in finite time, especially when influenced by unforeseen external disturbances. The nonlinear Backstepping Sliding Mode Control (BSMC) is effective in trajectory tracking control; however, undesired phenomena may occur if there are uncertain disturbances affecting the system or model parameters change. It is proposed to use a neural network to estimate a nonlinear function to handle unknown uncertainties of the system. The neural network parameters can be adaptively adjusted to optimal values through adaptation rules derived from Lyapunov's theorem. Additionally, fuzzy logic theory is also employed to adjust the controller parameters to accommodate changes or unexpected impacts. The performance of the Fuzzy Neural Network Backstepping Sliding Mode Control (FNN-BSMC) is evaluated through simulation results using Matlab/Simulink software. Two simulation cases are conducted: the first case assumes stable model parameters without uncertain disturbances affecting the joints, while the second case considers a model with changing parameters and disturbances. Simulation results demonstrate the effective adaptability of the proposed method when the system model is affected by various types of uncertainties from the environmen

Preparation, Characterization and Photocatalytic Activity of La-Doped Zinc Oxide Nanoparticles

Lanthanum (La)-doped zinc oxide nanoparticles were synthesized with different La concentrations by employing a gel combustion method using poly(vinyl alcohol) (PVA). The as-synthesized photocatalysts were characterized using various techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), photoluminescence (PL) spectroscopy, and UV–visible absorption spectroscopy. The average size of ZnO nanoparticles decreased from 34.3 to 10.3 nm with increasing concentrations of La, and the band gap, as evaluated by linear fitting, decreased from 3.10 to 2.78 eV. Additionally, it was found that the photocatalytic activity of doped samples, as investigated by using methyl orange dye under visible lights, improved in response to the increase in La concentration. The decomposition of methyl orange reached 85.86% after 150 min in visible light using La0.1Zn0.9O as the photocatalyst