摘要
运用非线性系统的线性化方法与神经网络在线辨识技术 ,提出了一种基于神经网络的多变量自适应控制策略 .提出的控制策略 ,当过程模型缺乏足够的先验知识时 ,对多变量非线性连续发酵过程取得了良好的控制性能 .仿真结果表明 ,提出的自适应控制方法能够适应过程模型的不确定性和参数的时变性 ,具有较强的鲁棒性 .并且通过对比分析得出 ,基于微分几何理论的输入输出线性化解耦控制方案 ,由于控制器的设计依赖于过程模型 ,对模型参数的变化很敏感 ,应用在发酵过程的非线性控制中 ,控制精度较低 ,鲁棒性较差 .
A neural network based adaptive controller for a multivariable fermentation process is proposed, in which the linearizing method of the nonlinear systems is combined with the on line identifying technology of the neural networks. When adequate prior information concerning the dynamics of the fermentation processes is not available and parameters of the processes changes with time, the simulation experiments demonstrate that the presented approach proves more effective than the input/output linearization method based on differential geometry. Moreover, because in the input/output linearization method the design of the controller relies on the exact process model and its control performance is sensitive to the parameters of the model, the method results in low accuracy and poor robust.
出处
《信息与控制》
CSCD
北大核心
2002年第4期371-374,共4页
Information and Control
基金
山东省自然科学基金资助 (Q99B0 1)
