Fixed-time adaptive neural control of electro-hydraulic system with model uncertainties: Theory and experiments

Abstract

Nonlinearities, uncertainties and unknown external loads decline tracking dynamic performance and steady-state accuracy of electro-hydraulic systems. Most existing electro-hydraulic controllers have well guaranteed both the cylinder position and the load pressure asymptotic stable or uniformly ultimately bounded. However, the corresponding output response is relatively slow and achieves the desired performance in an infinite time, which cannot meet the requirements of fast convergence in some engineering applications. In this study, a fixed-time adaptive neural control (FTANC) is proposed for an electro-hydraulic system to improve the fast convergence performance and steady-state accuracy of the cylinder position in case of hydraulic model uncertainties A rigorous theoretical derivation proves that the electro-hydraulic system obtains practically fixed-time stability, such that all system state errors converge to the zero neighborhood in a fixed time without relation to the initial conditions of electro-hydraulic systems. Finally, both the simulation and experimental results verify the effectiveness of the proposed control method.