Abstract: In order to solve the control problem of switched nonlinear systems with nonlinear constraints, a class of switched strict feedback nonlinear systems with asymmetric time varying full state constraints, incomplete state measuability and unknown external disturbances are studied in this paper. State observer, adaptive neural network and dynamic surface control techniques are introduced. An adaptive output feedback control method based on RBF(radial basis function) neural network is designed. By adopting the asymmetric time varying BLF(barrier lyapunov function), all states of the system meet the asymmetric time varying constraints. The Lyapunov method and the average dwell time theory guarantee that all signals in a closed loop system are semi globally consistent and eventually bounded. Finally, under the action of the proposed control law, the output tracking error can be reduced to an arbitrarily small value, and two simulation results also verify the effectiveness of the proposed control algorithm.

Key words: dynamic surface control, full state constrains, nonlinear switched systems, neural network state observer