A Dynamic Model and Control Method for a Two-Axis Inertially Stabilized Platform

To realize high-performance control for a two-axis inertially stabilized platform (ISP), a nonlinear dynamic model based on the geographic coordinate and a compound control method based on the back-stepping sliding mode control and adaptive radial basis function neural network (RBFNN) are proposed. Compared with the traditional dynamic model based on the inertial coordinate, the nonlinear dynamic model based on the geographic coordinate constructs the direct relationship among the control inputs and criteria of the ISP. Moreover, the back-stepping sliding mode control method is proposed to handle the system nonlinearity, parameter variations, and disturbances. Furthermore, the adaptive RBFNN is constructed and optimized to estimate the upper bound of the residual error on line to reduce the chatting phenomenon. The asymptotic stability of the proposed control method has been proven by the Lyapunov stability theory. The effectiveness of the proposed method is validated by a series of simulations and flight tests.