Finite-time adaptive neural dynamic surface control for non-linear systems with unknown dead zone

This paper proposes a new adaptive neural network finite-time dynamic surface control scheme for inaccurate non-linear systems subjected to unknown dead zones. The ‘explosion of differentiation’ is eliminated by the first-order filter in backstepping design. The parameter normalization scheme updates the coefficients of activation functions in the radial basis function neural networks. The dead zone inverse method estimates the dead zone parameters. It is proved that the proposed controller achieves the faster tracking performance and the boundedness of all signals in finite time. The contribution of this paper is that the presented controller not only has the advantages of transient performance and program execution time in comparison with traditional backstepping methods, but also its computational cost is lower than command filtered methods. Simulation experiment results are included to illustrate the effectiveness of the proposed scheme.