Output-Feedback Model Reference Adaptive Control for Continuous State Delay Systems

2003 ◽  
Vol 125 (2) ◽  
pp. 257-261 ◽  
Author(s):  
Boris M. Mirkin ◽  
Per-Olof Gutman
Keyword(s):  
Adaptive Control ◽  
Output Feedback ◽  
Reference Model ◽  
Main Idea ◽  
Functional Method ◽  
Model Reference Adaptive Control ◽  
Delay Systems ◽  
Model Reference ◽  
Continuous State ◽  
Model Reference Adaptive

This paper develops a new approach for the output model reference adaptive control of linear continuous-time plants with state delays. The main idea is to include into the control law a feedforward component that compensates for the delayed states, in addition to output feedback. The feedforward is formed by special adaptively adjusted prefilters as a function of the delayed state of the reference model. The output feedback component is designed as for a plant without delay, but applied to the time-delay plant. Such a controller structure containing adaptive output feedback and adaptive prefilters from the delayed reference model makes it possible to solve the problem of adaptive exact asymptotic output tracking under parametric uncertainties. The stability is analyzed using the Lyapunov-Krasovskii functional method. A simulated example illustrates the new controller.

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.

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.

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.

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