Nonlinear Maneuvering Control for a Class of MIMO Uncertain Systems Based on Neural Predictor and Optimized Path Update

To reduce the control effort and improve the transient performance of multi-input and multi-output (MIMO) uncertain systems, we develop a nonlinear maneuvering control method based on the neural predictor and optimized path update law in this article. First of all, a neural predictor is designed by using a minimal number of learning parameters approach to identify the uncertain nonlinear term in the system dynamics. Then, a nonlinear maneuvering controller is developed based on the backstepping approach and nonlinear maneuvering theory. To reduce the control effort, we construct a path parameter update law. Furthermore, all error signals are proved to be uniformly ultimately bounded. Simulation results are carried out to demonstrate the effectiveness of the proposed nonlinear maneuvering control method for MIMO uncertain systems.