Anti-Disturbance Neural-Sliding Mode Control for Inertially Stabilized Platform With Actuator Saturation

Abstract

To consider the environment during ground vehicle driving, the inertially stabilized platform (ISP) can be used for electro-optical tracking instruments to isolate the senor's line of sight (LOS) from the carrier's vibrations with high precision and stability. This paper proposes the combination of a backstepping sliding mode controller with the adaptive neural networks approach (BSMC-NN) for ISP that achieves output torque saturation and considers parametric uncertainties, friction, and gimbal mass imbalance. An adaptive radial basis function neural network is adopted to approximate uncertain disturbances in this dynamic system. In contrast to the existing saturated control structures, an auxiliary function is designed to compensate for any error between the designed and the actual control torque. The closed-loop stability and asymptotic convergence performance are guaranteed based on the Lyapunov stability theory. Finally, the simulation and experimental results demonstrate that this proposed controller can effectively regulate the gimbal rotation angle under different external disturbances. This offers superior control performance despite the existence of the nonlinear dynamics and control input constraints.

Keywords

• Backstepping
• adaptive RBFNN
• mass imbalance
• actuator saturation