Adaptive neural finite-time stabilisation for a class of p-normal form nonlinear systems with unknown virtual control coefficients

Abstract

The adaptive neural finite-time stabilisation problem is studied for a class of p-normal form nonlinear systems in this paper. An adaptive neural finite-time controller is designed by combining the adding a power integrator technique with Lyapunov finite-time stability theory. And the stability of the closed-loop system is analysed. The designed controller can guarantee that all the states in the closed-loop system are finite-time asymptotic stable. In the design, a new assumption for unknown nonlinear terms is proposed, which is looser than the existed works. A combination vector method is adopted to avoid the effect of the approximation errors of neural networks. The maximal value of norms of the combination vectors is taken as an adaptive parameter, so that the complexity of the design method is reduced. And the settling-time can be adjusted by changing a design parameter. Finally, three simulation examples demonstrate the effectiveness of the proposed control method.