摘要
为提高多步预测控制的计算效率 ,提出一种基于时间差分算法的Elman网络多步预测控制器的设计方法 .用Elman网络对非线性系统输出值进行直接多步预估 ,并针对BP算法无法对网络权值的实时调整进行渐进计算的缺点 ,提出了将时间差分法和BP算法相结合的新的网络学习算法 ;为简化计算 ,采用单值预测控制算法对非线性系统进行滚动优化以实现对下一步控制量的优化计算 .理论分析与仿真结果表明 ,该方法具有结构简单、运算量小、速度快的特点 ,可应用于实时快速系统 ,并且对系统参数的变化具有一定的自适应性 .
In order to improve the computation efficiency of multi-step predictive control, a predictive control system based on Elman network with temporal differences (TD) method is proposed. Elman network is used to predict the system output of multi-step ahead directly and a new hybrid learning algorithm combining the TD method with standard back propagation (BP) algorithm to train the Elman network is put forward according to the intrinsic disadvantages of BP algorithm ,which can not update network weights incrementally. To simplify computation, a single-value predictive control algorithm is used to realize the optimization of control input of the next step. Theoretical analysis and simulation results demonstrate that this method is suitable for fast real-time systems because of its characteristics of simple structure, small calculating amount and fast speed, and that it has some self-adaptability against changeable parameters of the system.
出处
《信息与控制》
CSCD
北大核心
2004年第5期531-535,共5页
Information and Control
关键词
时间差分法
BP算法
神经网络预测控制
单值预测控制算法
temporal differences method
BP algorithm
neural network based(NN-based) predictive control
single-value predictive control algorithm
