Robust backstepping sliding mode aircraft attitude and altitude control based on adaptive neural network using symmetric BLF

In this paper, asymptotic control of attitude and altitude of the aircraft is proposed by employing robust backstepping sliding mode control (BSMC) in conjunction with adaptive radial basis function neural network (RBFNN). Accurate knowledge of the non-linear aerodynamic forces and moments, particularly high-fidelity models, is of paramount importance to arrive at such a control strategy under a continuous dynamic environment. Adaptive RBFNN is used to approximate such an unknown non-linear function by continuously updating the network weights in rapidly varying conditions. Further, adaptation laws are used concurrently with neural networks to update the control power derivatives. These adaptive neural networks are used within the architecture of backstepping, integrated with the sliding surfaces where angular rates act as the virtual controller. The postulation of such law requires only minimal information about the aerodynamic model beyond well-known physical features. Moreover, Barrier Lyapunov Function (BLF) candidate is employed to constrain the state of the plant from transgressing a specific limit. Closed-loop signals are theoretically proved to be semi-globally uniformly ultimately bounded in the sense of Lyapunov. Finally, the robustness of the designed flight control law is explored by appending uncertainties and bounded exogenous disturbances in the plant. The results obtained in the present study signify good control performance where output tracks the reference signals by forcing the system states to remain in the designed sliding surfaces.