LAUSR

In this paper, the tracking control problem of adaptive neural network (NN) reinforcement learning (RL) is investigated for continuous time stochastic nonlinear systems with full state constraints and unmodeled dynamics. First, a state observer is constructed to estimate the unmeasurable states. Subsequently, an adaptive NN‐RL controller is designed by integrating the backstepping method with a critic‐actor RL strategy, thereby significantly enhancing the tracking performance. To address the issue of gradient explosion encountered in gradient descent methods, the projection operator is introduced to design critic‐action NN. Finally, the boundedness of all signals of the closed‐loop system is proven by using time‐varying barrier Lyapunov functions (BLFs), ensuring that all states satisfy predetermined constraints. Simulation results validate the effectiveness of the proposed scheme.