Prescribed performance control of morphing aircraft based on switched nonlinear systems and reinforcement learning

The prescribed performance control of a morphing aircraft with variable sweep wings is investigated based on switched nonlinear systems and reinforcement learning. Switched nonlinear systems in lower triangular form are first adopted to describe the longitudinal altitude motion, and an error transformation is applied to handle the prescribed performance bound. Then, the designed controller is divided into the basic part and supplementary part. For the basic part, the backstepping method with involvement of the modified dynamic surface control technique is utilized to avoid the “explosion of complexity” problem. Improved disturbance observers inspired from the idea of extended state observer are then designed to estimate the disturbances and combined with radial basis function neural networks to develop the common virtual control laws. Moreover, by using the error variables defined in the backstepping design, the reinforcement learning–based supplementary part controller is devised with the critic-action neural network structure, which can adjust its parameters online and further decrease the altitude tracking error. It is proved that all signals of the closed-loop system are uniformly ultimately bounded, and the prescribed performance bound for convergence of the altitude tracking error can be satisfied. Finally, comparative simulations demonstrate the effectiveness of the proposed control approach.