Neural-Network-Based Modeling and Dynamic Policy Synthesis for Model Predictive Control of Nonlinear Systems

被引：0

作者：

Gautam, Ajay ^{[1
]}

Soh, Yeng Chai ^{[1
]}

Wu, Xiaoping ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

2014 13TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION (ICARCV) | 2014年

基金：

新加坡国家研究基金会;

关键词：

STABILITY; MPC;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A dynamic control policy with optimized dynamics is explored for its use in a model predictive control (MPC) algorithm for a nonlinear system modeled with a feedforward neural network. The nonlinear system is expressed as a polytopic quasi-linear-parameter-varying (quasi-LPV) system over a region of the state-input space and the dynamics of the policy are allowed to depend on the time-varying parameter of the quasi-LPV model. The policy dynamics are optimized off-line to obtain an enlarged domain of attraction which matches with the state-input region over which the polytopic approximation of the system holds good. A complete MPC algorithm using the dynamic policy as the terminal policy ensures stabilization and improved performance over a larger domain without a larger horizon length.

引用

页码：259 / 264

页数：6

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