Modelling Free Response of a Solar Plant for Predictive Control

IFAC System Identification, Kitakyushu, Fukuoka,Japan,1997This paper deals with the identification of a nonlinear plant by means of a neural network (NN) modelling approximation. The problem of neural identification is tackled using a static NN in a NARX configuration. A method is proposed to obtain the number of past values needed to feed the network. The on-line adaptation of the model and other issues are discussed. In order to show the benefits that can be achieved with the proposed methods, the NN model is used within a Model Predictive Control (MPC) framework. The MPC scheme uses the prediction of the output of the system calculated as the sum of the free response (obtained using the nonlinear NN model) and the forced response (obtained linearizing around the current operating point) to optimize a performance index. The control scheme has been applied and tested in a solar power plant

Application of Wilcoxon Norm for increased Outlier Insensitivity in Function Approximation Problems

In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) (like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) ) or Radial Basis Functions(RBF) using some learning rules such as the back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, for the use of a variety of approaches to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The first aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network or a radial basis function neural network, the designer is often faced with the problem of choosing a network of the right size for the task. Using a smaller neural network decreases the cost of computation and increases generalization ability. However, a network which is too small may never solve the problem, while a larger network might be able to. Transmission bandwidth being one of the most precious resources in digital communication, Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response

Neural network based adaptive PID controller of nonlinear heat exchanger

This research presents the design and simulation of nonlinear adaptive control system on the heating process of shell-and-tube heat exchanger model BDT921. Shell-and-tube heat exchanger is a nonlinear process and change in process dynamics cause instability of the PID controller parameters i.e proportional gain, integral time and derivative time. Thus, the PID controller parameters need to be repeatedly retuned. In this study, neural network approach was introduced to auto-tune the controller parameters. The dynamic data from the BDT921 plant was collected to formulate the mathematical model of the process using MATLAB System Identification Toolbox. NARX model was used to represent the heat exchanger. Neural network was used as adaptive system to the PID controller. The neural network model consists of 4 input variables and 4 output variables. Single hidden layer feed forward neural networks with 20 neurons in hidden layer is the optimum topology of the network. The effectiveness of the controller was evaluated based on the set point tracking only. Simulation result proved that the adaptive PID controller was more effective in tracking the set point with faster settling time and lower or no overshoot respond compared to conventional PID controller

An Intelligent Nonlinear System Identification Method with an Application to Condition Monitoring

Neural networks are black-box model structures that map inputs to outputs and do not require underlying mathematical models between the two. They are frequently used in the field of system identification, the area that deals with the development of system models based on input-output data. In this work, a hybrid system identification method is implemented with neural networks (NN) and the Minimum Model Error estimator (MME) on different benchmark experimental setups, as well as simulations. The MME algorithm uses a cost function with a covariance constraint to determine smooth state estimates of a system given noisy measurement data and an assumed model. As a byproduct, it generates a vector of unmodeled nonlinear (or linear) system dynamics, which can then be modeled by a neural network. Combining this neural network with the assumed model from MME, a system plant model is obtained. The purpose of neural networks in this research is two-fold: to demonstrate the advantages of combined MME/NN models over some common system identification methods and to investigate the feasibility of using the data stored in the network structure of those models to develop a classification scheme for condition monitoring. The approach to classification that is used in this research does not lead to successful implementation of such a scheme

Nonlinear adaptive internal model control using neural networks

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 49-51).Issued also on microfiche from Lange Micrographics.The IMC structure, where the controller implementation includes an explicit model of the plant, has been shown to be very effective for the control of the stable plants typically encountered in process control. A nonlinear internal model control(NIMC) strategy based on neural network models is presented for SISO processes. The nonlinearities of the dynamical system are modelled by neural network architectures. Recurrent neural networks can be used for both the identification and control of nonlinear systems. Identification schemes based on neural network models are developed using two different techniques, namely, the Lyapunov synthesis approach and the gradient method. Both identification schemes are shown to guarantee stability, even in the presence of modelling errors. The NIMC controller consists of a model inverse controller and a robust filter with single adjustable parameter. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant,without the need to train an additional network to perform the inverse control. This NIMC approach is currently restricted to processes with stable inverses and with relative degree equal to one. Computer simulations demonstrate the proposed design procedure

Adaptive non linear system identification and channel equalization usinf functional link artificial neural network

In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) using some learning rules such as back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The primary aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network the designer is often faced with the problem of choosing a network of the right size for the task. The advantages of using a smaller neural network are cheaper cost of computation and better generalization ability. However, a network which is too small may never solve the problem, while a larger network may even have the advantage of a faster learning rate. Thus it makes sense to start with a large network and then reduce its size. For this reason a Genetic Algorithm (GA) based pruning strategy is reported. GA is based upon the process of natural selection and does not require error gradient statistics. As a consequence, a GA is able to find a global error minimum. Transmission bandwidth is one of the most precious resources in digital communication systems. Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response. When the amplitude and the envelope delay response are not constant within the bandwidth of the filter, the channel distorts the transmitted signal causing intersymbol interference (ISI). The addition of noise during propagation also degrades the quality of the received signal. All the signal processing methods used at the receiver's end to compensate the introduced channel distortion and recover the transmitted symbols are referred as channel equalization techniques.When the nonlinearity associated with the system or the channel is more the number of branches in FLANN increases even some cases give poor performance. To decrease the number of branches and increase the performance a two stage FLANN called cascaded FLANN (CFLANN) is proposed.This thesis presents a comprehensive study covering artificial neural network (ANN) implementation for nonlinear system identification and channel equalization. Three ANN structures, MLP, FLANN, CFLANN and their conventional gradient-descent training methods are extensively studied. Simulation results demonstrate that FLANN and CFLANN methods are directly applicable for a large class of nonlinear control systems and communication problems

[MODELING OF PH NEUTRALIZATION PROCESS PILOT PLANT]

System Identification is an art of constructing a mathematical model for a dynamic response system. The modeling process is based on the observed input and output data for a system. To start a modeling process, a good understanding of process behavior is required as it will determine the important parameters and characteristics to be analyzed. pH neutralization is a very nonlinear process. It is not easy to get an accurate model as compared to the actual model. Modeling using conventional methods does not seem to give a reliable model for this process. Thus, for wide a range of neutralization pH values, conventional modeling methods are not sufficient. Therefore, for this project, intelligent approaches are considered. The conventional methods that are used by the Author are mathematical modeling, empirical modeling and statistical modeling. Mathematical modeling is done to see the relation of inputs and output. Empirical modeling is the common method used for plant modeling. Statistical modeling is more a to computerized modeling where it requires a good computer configuration basic in order to achieve the desired output. Neural Network is used for the intelligent method. Neural network is an intelligent approach that has the capability to predict future plant performance by training several datasets. These conventional and intelligent methods are compared between each other in term of the model accuracy, model reliability and flexibility. Modeling using mathematical modeling is tedious and requires more effort on the block diagram configuration in order to get an accurate result. Empirical modeling is basically good enough for plant identification, unfortunately for a highly nonlinear system, the method does not seem reliable. Statistical modeling has the ability to predict an acceptable higher order model. On top of that, neural network could give a more reliable and accurate result

Nonlinear autoregressive moving average-L2 model based adaptive control of nonlinear arm nerve simulator system

This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems. In this paper, we introduce the nonlinear autoregressive moving average (NARMA-L2) model which might be approximations to the NARMA model. The nonlinear autoregressive moving average (NARMA-L2) model is an precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it isn't always handy for purposes of neural networks due to its nonlinear dependence on the manipulate input. In this paper, nerves system based arm position sensor device is used to degree the precise arm function for nerve patients the use of the proposed systems. In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite regularly, approximate techniques are used for figuring out the neural controllers to conquer computational complexity. Comparison were made among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model reference based adaptive control for the preferred input arm function (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMA-L2 model based model reference adaptive control system. Index Terms--- Nonlinear autoregressive moving average, neural network, Model reference adaptive control, Predictive controller DOI: 10.7176/JIEA/10-3-03 Publication date: April 30th 202

Identification of Nonlinear Systems Using Radial Basis Function Neural Network

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function