Adaptive neural control for a class of stochastic nonlinear time-delay systems with unknown dead zone using dynamic surface technique

Summary This paper investigates the problem of adaptive control for a class of stochastic nonlinear time-delay systems with unknown dead zone. A neural network-based adaptive control scheme is developed by using the dynamic surface control (DSC) technique and the minimal learning parameters algorithm. The dynamic surface control technique, which can avoid the problem of "explosion of complexity" inherent in the conventional backstepping design procedure, is first extended to the stochastic nonlinear time-delay system with unknown dead zone. The unknown nonlinearities are approximated by the function approximation technique using the radial basis function neural network. For the purpose of reducing the numbers of parameters, which are updated online for each subsystem in the process of approximating the unknown functions, the minimal learning parameters algorithm is then introduced. Also, the adverse effects of unknown time-delay are removed by using the appropriate Lyapunov-Krasovskii functionals. In addition, the proposed control scheme is systematically derived without requiring any information on the boundedness of the dead zone parameters and avoids the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique. It is shown that the proposed control approach can guarantee that all the signals of the closed-loop system are bounded in probability, and the tracking errors can be made arbitrary small by choosing the suitable design parameters. Finally, a simulation example is provided to illustrate the performance of the proposed control scheme.