Data-Based Robust Optimal Control Of Discrete-Time Systems With Uncertainties Via Adaptive Dynamic Programming

In this article, a new data-based adaptive dynamic programming algorithm is proposed to solve the optimal control policy for discrete-time systems with uncertainties. Firstly, for uncertain systems, the corresponding Hamiltonian function is designed, and then the robust adaptive dynamic programming algorithm is obtained. Next, by using the input and output data of the system, the data-based Bellman equation is constructed, and the data-based robust adaptive dynamic programming algorithm is derived, which does not require the accurate model of the system. Finally, a simulation example shows the effectiveness of the proposed algorithm.