摘要
将预测控制的优化思想与神经网络精确描述非线性和不确定性动态过程的特性有机结合，提出了可用于非线性加热炉的直接优化的神经网络预测控制方法，该方法是采用离线训练的神经网络，通过在线反馈校正，分段优化控制量，可使最优预测输出逼近参考轨迹；基于李雅普诺夫方法，讨论了闭环系统的稳定性，得到闭环系统局部渐进稳定的一个充分条件；讨论了加权系数h与λ对系统的影响；仿真结果表明了该方法的有效性、准确性和鲁棒性。
In this paper, the optimization methods of neural network predictive control is put forward, in witch neural network is used to be as a predictive mo del , and LM methods and data measured practically are adopted to train the neural n etwork off-line. As a result,optimized predictive output can approach the refer e nce track through on-line feedback correction and piecewise optimizing control. Further more, a Lyapunov based closed-loop stability analysis is discussed and a sufficient condition for local asymptotic stability is derived.Weight h and λ are discussed .The results of simulation show that this method is simple, effi cient, precise and robust.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2001年第2期216-220,共5页
China Mechanical Engineering
基金
国家自然科学基金(69974107)
湖北省自然科学基金(99JJ015)
国家留学基金委员会回国人员科研启动基金
高等学校国家重点实验
