Identification and Adaptive Neural Control of Time-Delayed Multivariable Plant
نویسنده
چکیده
A direct adaptive neural control scheme with single and double I-term is proposed to be applied for multivariable plant. The control scheme contains two Recurrent Trainable Neural Network (RTNN) models. The first RTNN is a plants parameter identifier and state estimator. The second RTNN is a feedback/feed-forward controller with I-terms. The good performance of the adaptive neural control with I-terms is confirmed by closed-loop systems analysis, and by simulation results, obtained with simple effect evaporator multivariable plant, corrupted by noise and affected by small unknown input time delay.
منابع مشابه
Adaptive Predictive Controllers Using a Growing and Pruning RBF Neural Network
An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...
متن کاملMultivariable Adaptive Control Using an Observer Based on a Recurrent Neural Network
A real time learning control technique for a general non-linear multivariable process is presented and applied to a laboratory plant. The proposed technique is a hybrid approach, which combines the ability of a recurrent neural network for modelling purposes and a linear pole placement control law to design the controller, providing a bridge between the eld of neural networks and the well known...
متن کاملAdaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks
This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the contro...
متن کاملIdentification and Adaptive Position and Speed Control of Permanent Magnet DC Motor with Dead Zone Characteristics Based on Support Vector Machines
In this paper a new type of neural networks known as Least Squares Support Vector Machines which gained a huge fame during the recent years for identification of nonlinear systems has been used to identify DC motor with nonlinear dead zone characteristics. The identified system after linearization in each time span, in an online manner provide the model data for Model Predictive Controller of p...
متن کاملIntelligent Gain-scheduling Control for Multivariable Discrete Linear Time Varying Systems
This paper proposes an Intelligent Linear Parameter Varying (ILPV) control approach for multivariable discrete Linear Time Varying (LTV) systems. An optimal linear quadratic closed loop control law is developed for each identified multivariable model so that the controlled system tracks a desired trajectory over the entire time interval. A gain scheduling adaptive control scheme based on neural...
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 2014