LAUSR

In practical applications, for highly nonlinear systems, how to implement control tasks for dynamic systems with uncertain parameters is still a hot research issue. Aiming at the internal parameter fluctuations and external unknown disturbances in nonlinear system, this paper proposes an adaptive dynamic terminal sliding mode control (ADTSMC) based on a finite-time disturbance observer (FTDO) for nonlinear systems. A finite-time disturbance observer is designed to compensate for the unknown uncertainties and a dynamic terminal sliding mode control (DTSMC) method is developed to achieve finite time convergence and weaken system chattering. Moreover, a dual hidden layer recurrent neural network (DHLRNN) estimator is proposed to approximate the sliding mode gain, so that the switching item gain is not overestimated and optimal value is obtained. Finally, simulation experiments of an active power filter model verify the designed ADTSMC method has better steady-state and dynamic-steady compensation effects with at least 1% THD reduction in the presence of nonlinear load and disturbances compared with the simple adaptive DTSMC law.