Model-Reference Based Adaptive Control for Enhancing Lateral Stability of Car-Trailer Systems

2016 ◽  
Author(s):  
Smitha Vempaty ◽  
Eungkil Lee ◽  
Yuping He
Keyword(s):  
Adaptive Control ◽  
Degrees Of Freedom ◽  
Reference Model ◽  
Model Reference Adaptive Control ◽  
Operating Conditions ◽  
Model Parameters ◽  
Lateral Stability ◽  
Model Reference ◽  
Control Approach ◽  
Model Reference Adaptive

This paper presents a model reference adaptive control (MRAC) approach to enhance the lateral stability of car-trailer systems. To this end, a 3 degrees of freedom (DOF) linear yaw-plane car-trailer model was developed as a “reference model”. The yaw rate of leading and trailing units of the reference model were used as the target states to control and stabilize a virtual vehicle plant represented by a 5 DOF linear yaw-roll car-trailer model. A Lyapunov-based controller was designed to handle the lateral stability of the car-trailer dynamical system. The model parameters and operating conditions of the system were predefined while designing the controller. The effectiveness of the adaptive controller for improving the lateral stability of car-trailer systems was demonstrated under a simulated multiple cycle sine-wave steering input maneuver. It was observed that the lateral stability of car-trailer system was improved by controlling respective yaw rates of the car and the trailer, using model reference adaptive control approach in conjunction with Lyapunov stability criterion.

scholarly journals Speed control of hydraulic turbines for grid synchronization using simple adaptive add-ons

2018 ◽  
Vol 51 (7-8) ◽  
pp. 276-284 ◽  
Author(s):  
Dogan Gezer ◽  
Yiğit Taşcıoğlu ◽  
Kutay Çelebioğlu
Keyword(s):  
Adaptive Control ◽  
Power Plant ◽  
Reference Model ◽  
Gain Scheduling ◽  
Model Reference Adaptive Control ◽  
Operating Conditions ◽  
Hydroelectric Power ◽  
Model Reference ◽  
Grid Synchronization ◽  
Model Reference Adaptive

Background: Parameters of the hydroelectric power plant controllers are typically tuned at the nominal operating conditions such as nominal head and single unit operation. Water level variations in reservoir and/or tailwater, and the presence of other active units sharing the penstock are common disturbances to the nominal assumption. Methods: This article proposes two adaptive add-ons, namely gain scheduling and model reference adaptive control, to the existing speed controllers to improve grid synchronization performance when the site conditions are not nominal. The add-ons were designed and tested on a validated dynamic model of a power plant unit by using a software-in-the-loop simulation setup. An off-season scenario is simulated, in which the original controller of the unit cannot bring the turbine to synchronize with the grid due to low gross head. Then, the add-ons were implemented on-site and experiments were performed under similar conditions. The parameter sets used in gain scheduling for different operation bands are determined off-line with the help of operational experience. The model reference adaptive control add-on requires a reference model and a learning rate. A description of the turbine speed-up profile at nominal operating conditions is sufficient to be used as the reference model. The proposed piecewise linear reference model favors stability over speed in settling to the nominal speed. Results: It is experimentally shown that the proposed add-ons compensate the negative effect of head loss in grid synchronization, and perform similar to the ideal performance at the nominal head. Conclusion: Both add-ons can be implemented on the available off-the-shelf speed governor controllers. They are suitable for use in all hydroelectric power plants, especially in unmanned ones, for automatic synchronization with less waste water.

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.

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.

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