scholarly journals Support vector regression and model reference adaptive control for optimum control of nonlinear drying process

TAPPI Journal ◽  
2016 ◽  
Vol 15 (2) ◽  
pp. 111-126 ◽  
Author(s):  
C. Karthik ◽  
K. Valarmathi ◽  
M. Rajalakshmi
Keyword(s):  
Adaptive Control ◽  
Support Vector Regression ◽  
Reference Model ◽  
Design Procedure ◽  
Model Reference Adaptive Control ◽  
Support Vector ◽  
System Control ◽  
Paper Machine ◽  
Model Reference ◽  
Model Reference Adaptive

In this paper, a support vector regression (SVR)-based system identification and model reference adaptive control (MRAC) strategy for stable nonlinear process input-output form is designed. In order to implement the proposed control structure, SVR-based identification methods are clearly addressed. The control of a moisture process on the paper machine illustrates the proposed design procedure and the properties of the SVR-based model identification-adaptive reference model for the nonlinear system. MRAC is widely used in linear system control areas, and neural networks (NN) are often used to extend MRAC to nonlinear areas. Some drawbacks of NN with MRAC are slow speed in learning, weak generalization ability, and a local minima tendency. To overcome this problem, SVR is used instead of NN. With the support vector regressor, a stable controller-parameter adjustment mechanism is constructed by using the model reference adaptive theory. Simulation results show that the proposed approach could reach desired performance.

Support Vector Regression Based Nonlinear Model Reference Adaptive Control

2012 ◽  
Vol 601 ◽  
pp. 289-293
Author(s):  
Xin Xin Ye ◽  
Jian Gang Lu ◽  
Qin Min Yang
Keyword(s):  
Adaptive Control ◽  
Support Vector Regression ◽  
Model Reference Adaptive Control ◽  
Support Vector ◽  
System Control ◽  
Model Reference ◽  
Nonlinear Part ◽  
Adjustment Mechanism ◽  
Learning Speed ◽  
Model Reference Adaptive

Model reference adaptive control (MRAC) is widely used in linear system control areas, and Neural Networks (NN) is often used to extend MRAC to nonlinear areas. However, this kind of solution inherits some drawbacks of NN, including slow learning speed, weak generalization ability, local minima tendency, etc. Given these drawbacks, this paper attempts to use support vector regression (SVR) as a substitute of NN. In this approach, SVR is employed to compensate the nonlinear part of the plant. A stable controller-parameter adjustment mechanism is constructed by using the practical stability theory. Simulation results show that the proposed approach could reach desired performance.

Enhanced Model Reference Adaptive Control Using Smith Predictor

2013 ◽  
Vol 367 ◽  
pp. 363-368
Author(s):  
R. Karthikeyan ◽  
C. Bhargav ◽  
Karthik Koneru ◽  
G. Syam ◽  
Shikha Tripathi
Keyword(s):  
Adaptive Control ◽  
Fuzzy Logic Controller ◽  
Dead Time ◽  
Reference Model ◽  
Model Reference Adaptive Control ◽  
Vital Role ◽  
Smith Predictor ◽  
Model Reference ◽  
Time Effects ◽  
Model Reference Adaptive

The main aim of a control system is to repress the instabilities caused by nonlinearities of the system. Dead time is considered to be one of the most significant nonlinearities of a system. Dead time compensators play a vital role in reducing the dead time effects on the processes only to a minute extent. This paper proposes a method to overcome this problem by using Enhanced Model Reference Adaptive Control (MRAC) incorporating Smith Predictor. MRAC belongs to class of adaptive servo system in which desired performance is expressed with the help of a reference model. Enhanced MRAC consists of a fuzzy logic controller which provides adaptation gain to MRAC without human interference. A dead time compensator incorporated in the enhanced MRAC solves the problem of instabilities caused by dead time to a greater extent.

Model-Reference Adaptive Control System Design Technique

1973 ◽  
Vol 95 (4) ◽  
pp. 374-379 ◽  
Author(s):  
D. W. Sutherlin ◽  
J. S. Boland
Keyword(s):  
Adaptive Control ◽  
Liapunov Function ◽  
Design Procedure ◽  
Adaptive Methods ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
The Difference ◽  
The One ◽  
Relationship Of ◽  
Model Reference Adaptive

This paper considers the model-reference adaptive control problem which has received considerable attention in the literature in the last few years. An adaptive control scheme is proposed which has terms in the Liapunov function used in the design procedure which are not included in previously proposed schemes. The relationship of this new scheme to existing schemes is shown by considering the root-loci of the linearized error equations between plant and model. Finally, a second order example is given which illustrates the difference between the two previously proposed model-reference adaptive methods and the one proposed in this paper.

The Model Reference Adaptive Control of the DC Electric Drive System

2014 ◽  
Vol 875-877 ◽  
pp. 2030-2035 ◽  
Author(s):  
Marian Gaiceanu ◽  
Cristian Eni ◽  
Mihaita Coman ◽  
Romeo Paduraru
Keyword(s):  
Adaptive Control ◽  
Reference Model ◽  
Sliding Mode ◽  
Tracking Error ◽  
Model Reference Adaptive Control ◽  
Drive System ◽  
Mode Switching ◽  
Sigmoid Function ◽  
Model Reference ◽  
Model Reference Adaptive

Due to the parametric and structural uncertainty of the DC drive system, an adaptive control method is necessary. Therefore, an original model reference adaptive control (MRAC) for DC drives is proposed in this paper. MRAC ensures on-line adjustment of the control parameters with DC machine parameter variation. The proposed adaptive control structure provides regulating advantages: asymptotic cancellation of the tracking error, fast and smooth evolution towards the origin of the phase plan due to a sliding mode switching k-sigmoid function. The reference model can be a real strictly positive function (the tracking error is also the identification error) as its order is relatively higher than one degree. For this reason, the synthesis of the adaptive control will use a different type of error called augmented or enhanced error. The DC machine with separate excitation is fed at a constant flux. This adaptive control law assures robustness to external perturbations and to unmodelled dynamics.

Switched Fractional Order Model Reference Adaptive Control for First Order Plants: a Simulation-Based Study

2021 ◽  
Author(s):  
Norelys Aguila Camacho ◽  
JorgeE García Bustos ◽  
EduardoI Castillo López ◽  
Javier A. Gallego ◽  
JuanC TraviesoTorres
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Reference Model ◽  
Model Reference Adaptive Control ◽  
System Behavior ◽  
Order Model ◽  
Model Reference ◽  
First Order ◽  
Fractional Order Model ◽  
Model Reference Adaptive

Abstract This paper presents the results and analysis of an exhaustive simulation study where Switched Fractional Order Model Reference Adaptive Control (SFOMRAC) is used for first order plants, along with the analytical proof of boundedness and convergence of the scheme. The analysis is focused on the controlled system behavior through the integral of the timed squared control error (ITSE) and on the control energy though the integral of the squared control signal (ISI). Controller parameters such as fractional order, adaptive gain and switching time are varied along the simulation studies, as well as plant parameters and reference models. The results show that SFOMRAC controllers can be found for every plant and reference model used, such that both system behavior and control energy can be improved, compared to equivalent non switched fractional order and integer order control strategies.

scholarly journals On Fractional Model Reference Adaptive Control

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bao Shi ◽  
Jian Yuan ◽  
Chao Dong
Keyword(s):  
Adaptive Control ◽  
Reference Model ◽  
Model Reference Adaptive Control ◽  
Lyapunov Theory ◽  
Model Reference ◽  
Fractional Integrator ◽  
The Stability ◽  
Adaptation Law ◽  
Integrator Model ◽  
Model Reference Adaptive

This paper extends the conventional Model Reference Adaptive Control systems to fractional ones based on the theory of fractional calculus. A control law and an incommensurate fractional adaptation law are designed for the fractional plant and the fractional reference model. The stability and tracking convergence are analyzed using the frequency distributed fractional integrator model and Lyapunov theory. Moreover, numerical simulations of both linear and nonlinear systems are performed to exhibit the viability and effectiveness of the proposed methodology.

A New Model Reference Adaptive Control Using Stacked Identifiers

2012 ◽  
Vol 488-489 ◽  
pp. 1767-1771
Author(s):  
Wei Der Chung ◽  
Xiao Hu ◽  
Woon Ki Na ◽  
Hsin Pei Chen ◽  
Yun Zhi Cheng ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Reference Model ◽  
Linear Time ◽  
Model Simulation ◽  
Design Method ◽  
Model Reference Adaptive Control ◽  
Primary Objective ◽  
Model Reference ◽  
Single Input Single Output ◽  
Model Reference Adaptive

Model reference adaptive control is a major design method for controlling plants with uncertain parameters. The primary objective of this paper is to develop a new design approach for the model reference adaptive control of a single-input single-output linear time-invariant plant. The proposed method, called the “Model reference adaptive control using stacked identifiers, “uses a stacked identifier structure that is new to the field of adaptive control. The goal is to make the output of the plant asymptotically track the output of the first identifier, and then driving the output of the first identifier to track that of the second identifier, and so forth, up to the q-th identifier where q is the relative degree of the plant. Lastly, the output of the q-th identifier is forced to converge to that of the reference model. Simulation results shown our paper provides a new adaptive scheme which may give a better transient performance. No state measurement of the plant is required in our method. Since the resulting control systems are nonlinear and time-varying, the stability analysis of the overall system plays a central role in developing the theory.

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