Neural-Network-Based Approach for Finite-Time Optimal Control
This chapter proposes a novel finite-time optimal control method based on input-output data for unknown nonlinear systems using ADP algorithm. In this method, the single-hidden layer feed-forward 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 law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.
- 15.Huang, G., Siew, C.: Extreme learning machine: RBF network case. In: Proceedings of the Eighth International Conference on Control, Automation, Robotics and Vision (ICARCV 2004), Dec 6–9, Kunming, China, vol. 2, pp. 1029–1036. (2004)Google Scholar