Direct heuristic dynamic programming design with extreme learning machine

Extreme learning machine (ELM) as a learning algorithm for neural networks (NN) could provide the best generalization performance at extremely fast leaning speed. Through the use of ELM, it is thus possible to improve the existing schemes especially the ones whose learning speed is not fast enough while addressing control problems. As a popular NN-based approach for control applications, direct heuristic dynamic programming (DHDP) with a good capability of adaptive learning has been successfully applied to solve control problems. But limited by slow learning algorithms in NN, it imposes very challenging obstacles to the real-time controller design of DHDP, which keeps it from widely applied. In this paper, driven by the interest of improving learning speed of DHDP while maintaining its good approximation performance, we employ ELM as a learning algorithm in DHDP. The proposed ELM-based DHDP learning scheme is tested on a cart-pole balancing control problem. The simulation results show the proposed scheme has better learning performance than traditional DHDP. Furthermore, this paper provides a novel idea of applying ELM in control problems.