Multilayer neuroadaptive force control of electro-hydraulic load simulators with uncertainty rejection

In this paper, an innovative multilayer neuroadaptive controller is firstly proposed to further improve the output force tracking performance of electro-hydraulic load simulators with uncertainty rejection. Significantly, a highly nonlinear load simulator driven by the single-rod electro-hydraulic actuators is considered, which has universal research significance in relation to the relevant studies. Moreover, smooth endogenous uncertainties, non-smooth endogenous uncertainties in the load pressure dynamics, and exogenous disturbances can all be effectively compensated in a feedforward way, which makes it possess higher load simulation accuracy and wider applicability to working conditions. In detail, multilayer neural networks and extended state observer are employed to estimate endogenous uncertainties and exogenous disturbances, respectively. Specially, the neural network weights are on-line updated via the composite errors comprised of the force tracking and estimate errors, which can increase the resulting learning ability. The closed-loop stability is guaranteed via the strict Lyapunov stability analysis and extensive comparative application results are achieved to validate the high-performance efficacy of the proposed intelligent learning algorithm. • The highly nonlinear load simulator driven by single-rod hydraulic actuators is considered. • Smooth and non-smooth endogenous uncertainties, and exogenous disturbances can be addressed. • The learning ability of neural networks can be increased.