A Novel Online Adaptive Dynamic Programming Algorithm With Adjustable Convergence Rate

This article develops a novel online adaptive dynamic programming algorithm with adjustable convergence rate to address the optimal control problem of nonlinear systems. Relaxation factors are introduced to tune the convergence rate of value function sequence online. A novel update law based on recursive least squares is developed to adjust the weight of critic neural network at the sampling instant. The uniform ultimate boundedness of the neural network estimation error and the closed-loop system state are analyzed by utilizing the Lyapunov technique. Finally, the effectiveness of the present algorithm is demonstrated by executing three simulation examples.