Adaptive neural data-based compensation control of non-linear systems with dynamic uncertainties and input saturation PROJECT TITLE :Adaptive neural data-based compensation control of non-linear systems with dynamic uncertainties and input saturationABSTRACT:In this study, an adaptive neural backstepping management theme is proposed for a category of strict-feedback non-linear systems with unmodelled dynamics, dynamic disturbances and input saturation. To solve the difficulties from the unmodelled dynamics and input saturation, a dynamic signal and smooth operate in non-affine structure subject to the management input signal are introduced, respectively. Radial basis operate (RBF) neural networks are used to approximate the packaged unknown non-linearities, and an adaptive neural management approach is developed via backstepping, which guarantees that every one the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean sq.. The main contributions of this note lie in that a control strategy is provided for a class of strict-feedback non-linear systems with unmodelled dynamics uncertainties and input saturation, and also the proposed control scheme will not require any info of the certain of input saturation non-linearity. Simulation results are used to indicate the effectiveness of the proposed management theme. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Molecular dynamic simulation of Ca2+-ATPase interacting with lipid bilayer membrane Modelling of concentrating solar power plant for power system reliability studies