Neural dynamic surface control for stochastic nonlinear systems with unknown control directions and unmodelled dynamics

The control problem for a class of stochastic nonlinear systems with both unknown control directions and unmodelled dynamics is investigated here for the first time. The technique of dynamics signal is adopted to cope with the unmodelled dynamics in the considered system. The unknown control directions problem are addressed by Nussbaum function. RBF neural networks are employed to approximate the lumped unknown functions, and regardless of the number of neural networks used and the order of the system, only one adaptive parameter requires to be adjusted. Dynamic surface control(DSC) is utilized to cope with the complexity explosion of the backstepping design. Hence, a novel neural control scheme is proposed by means of dynamics signal method, DSC technique and Nussbaum function. Stability analysis proves all closed-loop signals are SGUUB by choosing the parameters appropriately, and the simulation results demonstrate the correctness and effectiveness of the proposed scheme.