Flexible joint mechanical arm neural network integral sliding mode controller design method based on disturbance observer

The invention discloses a flexible joint mechanical arm neural network integral sliding mode controller design method based on a disturbance observer. The advantages of the neural network, the adaptive disturbance observer and the integral sliding mode are integrated. Considering that a radial basis function neural network (RBFNN) has the characteristics of high learning convergence speed and strong approximation capability, two radial basis function neural network matrixes are adopted to estimate dynamic parameters of a mechanical arm-actuator. Aiming at the characteristics that an estimation error exists when the RBFNN is used and a mechanical arm system has external disturbance in actual work, the invention provides a new disturbance observer to estimate the system lumped uncertainty composed of the estimation error of the RBFNN and the time-varying external disturbance. Moreover, in order to further eliminate steady-state errors, an integral sliding surface is introduced. In addition, for security conside