Adaptive third-order integral sliding mode control for the mobile petroleum crane-form pipelines system with input bounded

Abstract

The mobile petroleum crane-form pipelines (MPCFP) system is one of the most important mechanical equipment in the petrochemical industry, consisting of rigid pipelines and rotating elbows, mainly used for loading and unloading petrochemical fluids. This paper proposes a novel neural network adaptive third-order integral sliding mode tracking control strategy with bounded inputs to achieve automatic alignment between the MPCFP system and the inlet of the tank truck. The proposed strategy replaces normal system inputs with the time derivative of the control torque. Although there are discontinuous terms in the derivative of the control torque, the integrated control torque is continuous. This unique feature ensures finite-time convergence of system state errors and eliminates sliding mode chattering. To address the issues of bounded input and modeling error, a state compensation method is introduced, and a neural network adaptive algorithm is utilized to estimate the modeling error. The stability proof process employs the Lyapunov function method, demonstrating that the system state error can converge to zero. Numerous simulation experiments validate the effectiveness of this control strategy.