摘要
基于神经网络的逼近特性,针对一类包含未知函数的串级连续搅拌釜式反应系统,提出了一种自适应控制算法。由于所考虑的反应系统具有非线性特性以及未知函数存在于各子系统的方程中,因此,该系统是复杂和难于控制的。为了克服困难,神经网络逼近系统中的未知函数,新奇的递归设计方法用于消除系统中的互联项,同时,需要定义特殊的被逼近非线性函数。利用李雅普诺夫稳定性分析方法,提出的控制算法保证了闭环系统的所有信号是有界的和系统的输出收敛到零的邻域内。仿真例子表明提出的控制算法是有效的。
An adaptive control algorithm is proposed for continuous stirred tank reactor （CSTR） with unknown functions based on the approximation property of the neural networks. Because the considered reactor contains the nonlinear property and the unknown functions are included in the subsystem, it is a completed system and is very difficult to be controlled. In order to avoid the difficulties, a novel recursive procedure is given to remove the interconnection term and special approximated functions are defined to be approximated by using the neural networks. Using the Lyapunov method, the algorithm ensures that all the signals in the closed-loop are bounded and the output can converge to a neighborhood of zero. A simulation example is given to show effectiveness of the algorithm.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2013年第12期4674-4680,共7页
CIESC Journal
基金
国家自然科学基金项目(61174017)
辽宁省教育厅项目(L2013243)~~
关键词
神经网络
过程控制
化学反应器
非线性系统
neural network
process control
chemical reactors
nonlinear systems
