基于神经动力学优化和有监督学习的非线性鲁棒预测控制方法及其应用

项目名称： 基于神经动力学优化和有监督学习的非线性鲁棒预测控制方法及其应用

项目编号： No.61273307

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 王钧

作者单位： 大连理工大学

项目金额： 84万元

中文摘要： 模型预测控制是一种基于时域模型和实时优化的拥有广阔应用前景的先进控制方法。该领域当前的主要挑战在于非线性系统建模和优化的实现。本项目旨在开发多种基于神经网络的自主学习和实时优化等固有功能的非线性和鲁棒模型预测控制方法。针对系统未知或含有未建模动态的情况，应用学习速度快和泛化能力强的新型神经网络，建立高精度预测模型。针对非线性模型预测控制中普遍存在的非凸优化难题，通过构造凸或泛凸性能指标，确定一类弱非线性系统的凸化范围，进而设计收敛快和结构简单的递归神经网络作为并行计算模型实现实时优化。针对强非线性模型导致的非凸优化问题，通过线性化分解和监督学习等逼近方法凸化原问题，设计递归神经网络实时求解。针对多种系统不确定性，建立基于神经网络的鲁棒模型预测控制方法。并采用目标规划价值函数等标量化方法，建立多目标神经网络模型预测控制方法。进而探索基于神经网络的机器人和水面及水下载体的模型预测控制方案。

中文关键词： 神经网络；神经动力学优化；模型预测控制；监督学习；

英文摘要： Model predictive control (MPC) is an optimization-based advanced control strategy which has been widely recognized in academia and industries. MPC generates control actions by means of real-time optimization of a performance index over a ?nite moving horizon of predicted future, subject to system constraints. MPC has several desirable features from theoretical and practical viewpoints; e.g., it handles multivariable control problems naturally, it optimizes its dynamic performance over a prediction horizon, it takes account of input and output constraints, and it takes account of structural changes. A major challenge of the MPC research and development lies in the realization of nonlinear and robust MPC methods. This project aims at addressing several critical issues for analysis and design of nonlinear and robust MPC based on neural networks. First, in the cases of unknown dynamic systems or partially known dynamic systems with unmodeled dynamics, new types of neural networks with fast learning efficiency (e.g., echo state network, extreme learning machine) can be applied to build accurate prediction models. Secondly, with nonlinear models to predict dynamic behaviors, real-time optimization of nonconvex optimization problems have to be performed for nonlinear MPC, which is extreme demanding in terms of solution

英文关键词： Neural Networks；Neurodynamic Optimization；Model Predictive Control；Supervised Learning；