Tank Gun Elevation Control Under Uncertainties Using Adaptive Sliding Mode Approach

Abstract

This article presents the adaptive sliding mode method for the gun elevation system of the tank operating under model uncertainties and external disturbances. The proposed controller combines optimal control design, the adaptive compensation mechanism using the radial basis function (RBF) neural network, while integrating the sliding mode control (SMC) law to enhance robustness and trajectory tracking accuracy. The RBF network is used to estimate and compensate for unknown nonlinear components and dynamic uncertainties in real time, and the SMC law is incorporated to ensure robustness and force the system output to accurately follow the desired trajectory. The control strategy is synthesized to meet strict performance requirements under complex real-world operating conditions. Simulation studies conducted in Matlab evaluate the controller's effectiveness. The results demonstrate that the proposed method achieves accurate trajectory tracking, strong disturbance rejection, and improved robustness, confirming its potential for practical military applications.