Observer-Based Adaptive Optimized Control for Stochastic Nonlinear Systems with State Constraints

This paper investigates an adaptive neural network (NN) optimal control problem for stochastic nonlinear systems. The stochastic systems under consideration contain unknown nonlinearities, immeasurable states and state constraints. The NNs are utilized to approximate the unknown nonlinearities, and a nonlinear state observer is designed and thus the unmeasured states are estimated via it. In the framework of observer-based output feedback control and the backstepping technique, the virtual and actual optimal controllers are developed based on the actor-critic architecture. All the states are confined within the preselected compact sets by developing the tan-type Barrier optimal performance index functions. Furthermore, the stability of the closed-loop systems is proved by using Lyapunov function theory. The simulation results demonstrate the effectiveness of the presented scheme.