Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems

摘要

This paper proposes a novel finite-time optimal controlmethod based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feedforward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control lawat each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.