Observer-Based Adaptive Optimized Control for Stochastic Nonlinear Systems With Input and State Constraints

In this work, an adaptive neural network (NN) optimized output-feedback control problem is studied for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. A nonlinear state observer is designed to estimate the unmeasured states, and the NNs are used to approximate the unknown nonlinear functions. Under the framework of the backstepping technique, the virtual and actual optimal controllers are developed by employing the actor-critic architecture. Meanwhile, the tan-type Barrier optimal performance index functions are developed to prevent the nonlinear systems from the state constraints, and all the states are confined within the preselected compact sets all the time. It is worth mentioning that the proposed optimized control is clearly simple since the reinforcement learning (RL) algorithm is derived based on the negative gradient of a simple positive function. Furthermore, the proposed optimal control strategy ensures that all the signals in the closed-loop system are bounded. Finally, a practical simulation example is carried out to further illustrate the effectiveness of the proposed optimal control method.

Automated summary

This work investigates an adaptive neural network optimized output-feedback control problem for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. It proposes an optimized control strategy based on the backstepping technique and actor-critic architecture to prevent system violations of state constraints and ensure bounded signals in the closed-loop system.