Bounded-time system identification under neuro-sliding training

A neural network training method for identification in bounded time of nonlinear systems is presented in this paper. A sliding mode surface drives the adalines, perceptrons and multilayer perceptrons so as to a new second order sliding mode is enforced for all time. This neural network-based sliding mode enforces an invariant differential manifold, with a time-varying feedback gain to give rise to finite-time convergence, consequently, the chattering free sliding mode allows identification of the underlying system in finite-time, with zero error. Convergence characteristics of the algorithm are proven with Lyapunov stability theory and concepts drawn from variable structure systems. Numerical simulations for a full nonlinear nonlinear robot arm, subject to noise, show the validity of the proposed approach.