Neural Network Sliding Mode Control of Airborne Photoelectric Stabilized Sighting Platform

Aiming at the problem of closed-loop performance degradation caused by actuator saturation of airborne photoelectric stabilized aiming platform actuator in the application of traditional sliding mode variable structure control, radial basis function (RBF) neural network (NN) was used as compensator to improve the sliding mode variable structure control law, and a neural network sliding mode control (SMC) method considering the input limitation of actuator was proposed. Simulation results show that the application of the controller can compensate for the limited control input and improve the visual axis stability of airborne photoelectric stabilized sighting platform.