A novel RNN terminal sliding mode controller design based on barrier function for uncertain quad-rotor UAV systems

A new triple-loop recurrent neural network (TLRNN) super-twisting terminal sliding mode control (TSMC) scheme based on the barrier function is proposed for the attitude tracking control of flight systems with unknown disturbances and parameter uncertainties. Compared with the traditional recurrent neural network (RNN), the newly designed TLRNN incorporates three different feedback loops, which improves the performance of approximation and enhances the capacity to save information. Additionally, the barrier function-based variable gain super-twisting algorithm is used to reduce chattering and dynamically adjust control gain. The finite-time convergence of a quad-rotor UAV system is derived using the Lyapunov function technique. The experiments, compared with existing methods, demonstrate the effectiveness of the proposed control strategy.