A novel finite-time robust dynamic neural identifier for a class of nonlinear systems: real-time experiments using an unmanned underwater vehicle

Abstract A novel structure for a Robust Dynamic Neural Network Identifier (RDNNI) based on sliding modes is proposed for the online identification of nonlinear second-order systems. A stability analysis using Lyapunov theory demonstrates that both the neural weight estimation errors and the sliding surface converge to zero in finite. Most of the identification schemes based on dynamic neural networks reported in the literature only assure the asymptotic convergence of the synaptic weights and the identification process. There exist some works which modifies the identifier structure to guarantee the finite time convergence of the identification process. However, all these schemes have the limitation that identification errors converge to a region around the origin because of the upper boundaries introduced by the system disturbances. Compared these types of dynamic neural network identifiers found in the literature, the proposed identifier combines the non-singular terminal sliding mode surface with the synaptic weights’ adaptation laws to guarantee that the identification errors converge to zero in a finite time. To evaluate the performance of the developed structure, a simulation is conducted for the identification of a Duffing oscillator, together with a series of experimental tests to identify the dynamics of depth and yaw in an unmanned underwater vehicle. Finally, a quantitative comparison of the identification performance with a recently introduced State-Input Affine Differential Neural Network (SIADNN) identifier demonstrates the superior performance of the proposed design, both in simulations and in real-time experiments.