内部模型
人工神经网络
非线性系统
非线性模型
控制(管理)
控制理论(社会学)
计算机科学
控制工程
人工智能
工程类
物理
量子力学
作者
E.P. Nahas,Michael A. Henson,Dale E. Seborg
标识
DOI:10.1016/0098-1354(92)80022-2
摘要
Abstract A nonlinear internal model control (NIMC) strategy based on neural network models is proposed for SISO processes. The neural network model is identified from input—output data using a three-layer feedforward network trained with a conjugate gradient algorithm. The NIMC controller consists of a model inverse controller and a robustness filter with a single tuning parameter. The proposed strategy includes time delay compensation in the form of a Smith predictor and ensures offset-free performance. Extensions for measured disturbances are also presented. The NIMC approach is currently restricted to processes with stable inverses. Two alternative implementations of the control law are discussed and simulations results for a continuous stirred tank reactor and pH neutralization process are presented. The results for these two highly-nonlinear processes demonstrate the ability of the new strategy to outperform conventional PID control.
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