Nonlinear vector model control of underactuated air cushion vehicle based on parameter reduction algorithm

Abstract

Due to the serious drift phenomenon of underactuated air cushion vehicle, the actual trajectory is determined by the total speed and course angle. In this paper, the course angle and total speed model are derived from the general four degrees of freedom air cushion vehicle model and named nonlinear vector model. Nonlinear vector model can be used to directly design the course and total speed controllers for underactuated air cushion vehicle. Adaptive radial basis function neural network is introduced to deal with the strong nonlinearity and uncertainty of air cushion vehicle’s complex dynamics. However, the adaptive weights to be calculated and updated may be too many in each sampling period. For the relief of the burden caused by the online computing, parameter reduction algorithm is designed in this paper. It gives us a power to choose the number of online update parameters freely. Then the new trajectory tracking control method with independent total speed and course controller is designed based on nonlinear vector model and parameter reduction algorithm. The designed controller ensures that the tracking errors are uniformly ultimately bounded. Also, only a few weights need to be updated online. The effectiveness and superiority of the designed controller is verified by simulation results.

Keywords

Underactuated air cushion vehicle trajectory tracking radial basis function neural network online computation burden uniformly ultimately bounded

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