Direct heuristic dynamic programming based on an improved PID neural network |
| |
Authors: | Jian SUN Feng LIU Jennie SI and Shengwei MEI |
| |
Affiliation: | Department of Electrical Engineering, Tsinghua University,Department of Electrical Engineering, Tsinghua University,Department of Electrical Engineering, Arizona State University,Department of Electrical Engineering, Tsinghua University |
| |
Abstract: | In this paper, an improved PID-neural network (IPIDNN) structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming (DHDP). As one of online learning algorithm of approximate dynamic programming (ADP), DHDP has demonstrated its applicability to large state and control problems. Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately, it is not always possible to access all the states in a real system. This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control. Since this structure can estimate the integrals and derivatives of measurable outputs, more system states are utilized and thus better control performance are expected. Compared with traditional PIDNN, this configuration is flexible and easy to expand. Based on this structure, a gradient decent algorithm for this IPIDNN-based DHDP is presented. Convergence issues are addressed within a single learning time step and for the entire learning process. Some important insights are provided to guide the implementation of the algorithm. The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm. |
| |
Keywords: | Approximate dynamic programming (ADP) Direct heuristic dynamic programming (DHDP) Improved PID neural network (IPIDNN) |
本文献已被 CNKI 维普 SpringerLink 等数据库收录! |
| 点击此处可从《控制理论与应用(英文版)》浏览原始摘要信息 |
|
点击此处可从《控制理论与应用(英文版)》下载全文 |
|