摘要
通过对系统的信号约束，构成有约束多变量广义预测控制问题，并运用T－H优化神经网络来求解这一复杂的优化问题．在求解过程中，有约束广义预测控制的求解被转化为一个T－H优化电路网络的稳态解．因此可以通过硬件电路或龙格-库塔数值方法进行求取．在一个工业过程模型上的仿真研究证明了这一方法是非常有效的．
Through the constrain of the signals of system,this paper drives the constrained multivariablegeneralized predictive control,and solves the complicate optimizing problem with T-H neural network. It istransformed into the solving of a stable state of a T-H optimizing neural networks for the constrained generalized predictive controller. Hence,the solution can be obtained through hardware electricity circuit or RungeKutta numerical algorithm. The simulation research on a process verifies that the method is very effective.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
1998年第6期847-852,共6页
Control Theory & Applications
关键词
神经网络
T-H网络
预测控制
多变量控制
neural network
T-H network
predictive control
multivariable control
quadratic optimization
