Adaptive neural dynamic surface control for a missile with input and output constraints

In this paper, adaptive neural dynamic surface control is applied to design a missile autopilot considering input and output constraints. A gaussian error function based input saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control. A barrier Lyapunov function (BLF) is employed in the control design in order to meet the output constraint requirement. Radius basis function (RBF) neural network based adaptive dynamic control is developed to guarantee that all the signals in the closed-loop systems are globally bounded, with arbitrary small tracking error by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the proposed approach.