safe controller for uncertain nonlinear systems using model-based reinforcement learning

In this paper a reinforcement learning algorithm is presented to solve an infinite horizon optimal control problem for uncertain control-affine nonlinear systems under state constraint. we first develop a safe controller using time derivative of barrier lyapunov function and then we use this to modify an actor-critic-identifier approach to safely learn optimal control policy. furthermore, the safety analysis is studied. in the end, simulation example is given to prove the efficacy of the proposed method.