Additive Feedforward Control of a Kiln Using Neural Networks
نویسندگان
چکیده
This paper presents an application of Neural Networks to the control of a real system with measurement noise. The details of the system and the implementation of sensor, controller and actuator are described. Saturation in the actuator is present and dealt with. The results of controlling the kiln with Direct Inverse Control and Additive Feedforward strategies are presented and compared. Problems arising within noisy systems and differences comparing with noise free systems are discussed. The results achieved show that the Additive Feedforward Control strategy with a nonoptimized PI Controller perform better than the Direct Inverse Control.
منابع مشابه
Application of Neural Networks for Diagnosing and Predicting the Condition of an Industrial Furnace
(Draft Paper) This paper discusses an industrial application of a neural network based automatic scheme for fault detection and diagnosis. Faults in a lime kiln are isolated and detected, using two multilayer feedforward networks. One is used to model the industrial process according to its nonlinear structure. The fault detection method is based on the output prediction error between the real ...
متن کاملIdentification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network
Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...
متن کاملDirect Inverse Control of a Kiln
This paper presents an application of Neural Networks to the control of a real system with measurement noise. The system is a kiln and the steps taken to arrive at direct and inverse models are described. The results of controlling the kiln with a Direct Inverse Control strategy are presented. Copyright CONTROLO 2000
متن کاملA Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers
o enhance the performances of rough-neural networks (R-NNs) in the system identification, on the base of emotional learning, a new stable learning algorithm is developed for them. This algorithm facilitates the error convergence by increasing the memory depth of R-NNs. To this end, an emotional signal as a linear combination of identification error and its differences is used to achie...
متن کاملUniversal approximation using incremental constructive feedforward networks with random hidden nodes
According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2000