摘要
针对非线性模型预测控制(NMPC)中滚动优化环节所需解决的非线性优化问题,研究了用和声退火算法(HAA)解决此类问题所需的算法参数设置及其具体应用措施。算例利用了神经网络作为预测模型,采用了控制增量约束。仿真结果表明,对所涉系统,用HAA在每个采样周期内于小范围内搜索最优控制序列可获得较好的实时性和较为准确的设定值跟踪性。
For nonlinear optimization required in nonlinear model predictive control,the application of the harmony annealing algorithm (HAA) with proper parameter setting and other manipulations are explored.The neural networks and the increment constraints are employed in simulations.The results verify that the good capabilities of real-time and the good set points tracking can be obtained for the concerned systems if the optional control sequences are searched in a smaller range within each sample period.
出处
《石油化工自动化》
CAS
2005年第2期39-42,共4页
Automation in Petro-chemical Industry
关键词
和声退火
非线性系统
预测控制
神经网络模型
harmony annealing algorithm
nonlinear system,predictive control
neural network model
