摘要
针对多变量的生物发酵系统,为提高神经网络逆解耦控制性能,提出一种基于神经网络逆解耦的自适应补偿控制方法。首先,基于逆系统理论构造神经网络近似被控系统的逆系统,并将神经网络逆系统与被控系统串联构成伪线性复合系统;然后,对解耦后的伪线性复合系统设计自适应补偿控制器,实现系统的跟踪控制;最后,基于Lyapunov稳定性理论设计控制器参数的自适应律,保证了控制系统的稳定性。将提出的控制方法应用于生物发酵过程的菌丝浓度、基质浓度的解耦控制,数值仿真结果表明,所提出的控制方法较能有效提高普通的神经网络逆系统解耦控制性能。
To improve the performance of neural network inverse deeoupling control, adaptive compensation control method is proposed for multi-variable fermentation system. First, according to inverse system theory, neural network is constructed to approximate the inverse system of the original system. And a pseudo-linear composite system can be gotten by cascading neural network inverse system and the original system; then, adaptive compensation controller is designed for the pseudo-linear composite system to achieve the control goal; last, based on Lyapunov stability theory, the parameter adaptive law is designed to assure that the system is stable. The proposed control method is applied in a fermentation process to achieve the deeoupling control of mycelia concentration and substrate concentration. Numerical simulations show that the proposed control method has the higher performance comparing with the general neural network inverse decoupling control method.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2013年第10期1188-1192,共5页
Computers and Applied Chemistry
基金
国家中小型创新基金项目(12C26213202207)
江苏高校优势学科建设工程资助项目([2011]6)
关键词
神经网络逆系统
伪线性复合系统
动态误差
自适应补偿控制
neural network inverse system
pseudo-linear composite system
dynamic error
adaptive compensation
