Self-learning optimal control for uncertain nonlinear systems via online updated cost function

This paper presents an online updated cost function based self-learning optimal control scheme for uncertain nonlinear systems. By establishing an online updated cost function with the help of disturbance observer, the Hamilton-Jacobi-Bellman equation is solved by constructing a critic neural network, whose weight vector is tuned by self-learning algorithm. And then, the optimal control scheme is derived indirectly. Based on Lyapunov stability analysis, the closed-loop system with the proposed scheme is guaranteed to be stable. The simulation results show the effectiveness of the developed self-learning optimal control scheme. The cost function reflects the system uncertainties in real time, which implies that this method relaxes the assumptions on available upper-bounds and matching condition for system dynamics in compared with many existing methods.