Adaptive neural network command filtered backstepping control of pure-feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure-feedback system subjected to time-varying output/stated constraints. By introducing a one-to-one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small-gain theorem shows that all the signals in closed-loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.