Real-time implementation of self-tuning regulator control technique for coupled tank industrial process system

2018 ◽  
Vol 232 (8) ◽  
pp. 1039-1052 ◽  
Author(s):  
Kagan Koray Ayten ◽  
Ahmet Dumlu ◽  
Aliriza Kaleli
Keyword(s):  
Real Time ◽  
Process Model ◽  
Sliding Mode ◽  
Industrial Process ◽  
Least Square ◽  
The Self ◽  
Control Technique ◽  
Recursive Least Square ◽  
Parameter Variations ◽  
Self Tuning

This article presents the self-tuning regulator control technique for a coupled tank liquid level system that often used in industry. An autoregressive with exogenous model has been used as the liquid process model with the self-tuning control implementation in order to track the desired tank level trajectories with disturbances and uncertainties of the system dynamics. The designed self-tuning controller has been sensitive to parameter variations of the nonlinear coupled tank system. The parameters of the proposed controller are periodically updated themselves during the process by means of online recursive least square method with the forgetting factor algorithm. In this way, the parameter variations and unwanted disturbances of the system are eliminated in real-time application. In order to demonstrate the efficiency of the self-tuning regulator control technique, the real-time studies have been executed. The obtained experimental results demonstrated that the proposed controller gives the better trajectory tracking performance and smaller magnitude in overshot and undershot than the designed classical proportional–integral and sliding mode controllers.

Simulation and Research for Generalized Predictive Control

2013 ◽  
Vol 694-697 ◽  
pp. 2205-2210
Author(s):  
Xiao Li Yu
Keyword(s):  
Predictive Control ◽  
Least Square ◽  
The Self ◽  
Dynamic Responses ◽  
Generalized Predictive Control ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Input Signals ◽  
Self Tuning ◽  
Time Invariant

This paper presents analysis and experiments for Generalized Predictive Control (GPC) algorithm based on software simulation. First, we illustrate the time invariant GPC algorithm in detail. Then, we describe the principle for the control parameter selection of GPC based on empirical results. The Recursive Least Square (RLS) algorithm will be used to identify model parameters in the self-tuning GPC. The performance of GPC algorithm is validated by simulation results, which show that the algorithm has rapid and accurate dynamic responses for input signals, such as step signal and square wave. When the model parameters are unknown, with the assistance of RLS, the self-tuning GPC algorithm also presents good performance and robustness capability, even when white Gaussian noise exists.

Application of an Adaptive Forecast Algorithm to the River Västerdalälven

1988 ◽  
Vol 19 (5) ◽  
pp. 293-302 ◽  
Author(s):  
László Iritz
Keyword(s):  
Parameter Estimation ◽  
Systems Theory ◽  
Least Square ◽  
The Self ◽  
Type Model ◽  
Recursive Least Square ◽  
Electronic Engineering ◽  
Self Tuning ◽  
Recursive Least Square Algorithm ◽  
Hydrological Forecast

During the last two decades, advances in electronic engineering, hydrological modelling and systems theory have given considerable benefits to the hydrological forecast developments. Today several powerful adaptive techniques are available, which can improve the reliability of hydrological forecasting. One of these techniques is the self-tuning predictor based on an ARMA type model using direct parameter estimation by recursive least square algorithm. The selftuning predictor has been tested on the River Västerdalälven in Sweden.

Resolved Motion Adaptive Control for Mechanical Manipulators

1984 ◽  
Vol 106 (2) ◽  
pp. 134-142 ◽  
Author(s):  
C. S. G. Lee ◽  
B. H. Lee
Keyword(s):  
Adaptive Control ◽  
Low Cost ◽  
Equations Of Motion ◽  
Least Square ◽  
Hand Position ◽  
Recursive Least Square ◽  
Joint Torques ◽  
Hand Trajectory ◽  
Self Tuning ◽  
Mechanical Manipulators

This paper presents the development of a resolved motion adaptive control which adopts the ideas of “resolved motion rate control” [8] and “resolved motion acceleration control” [10] to control a manipulator in Cartesian coordinates for various loading conditions. The proposed adaptive control is performed at the hand level and is based on the linearized perturbation system along a desired hand trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component resolves the specified positions, velocities, and accelerations of the hand into a set of values of joint positions, velocities, and accelerations from which the nominal joint torques are computed using the Newton-Euler equations of motion to compensate all the interaction forces among the various joints. The feedback component consisting of recursive least square identification scheme and an optimal adaptive self-tuning controller for the linearized system computes the perturbation torques which reduce the manipulator hand position and velocity errors along the nominal hand trajectory. The feasibility of implementing the proposed adaptive control using present day low-cost microprocessors is explored.

REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH

2004 ◽  
Vol 04 (02) ◽  
pp. 193-206 ◽  
Author(s):  
SHUXUE DING ◽  
JIE HUANG ◽  
DAMING WEI
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Lower Cost ◽  
Source Separation ◽  
Least Square ◽  
Gradient Algorithm ◽  
Normal Equation ◽  
Recursive Least Square ◽  
Second Order Statistics

We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

scholarly journals Discrete Second-Order Sliding Mode Adaptive Controller Based on Characteristic Model for Servo Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhihong Wang ◽  
Yifei Wu ◽  
Wei Chen ◽  
Xiang Wang ◽  
Jian Guo ◽  
...  
Keyword(s):  
High Speed ◽  
Sliding Mode ◽  
Adaptive Controller ◽  
Second Order ◽  
Least Square ◽  
Recursive Least Square ◽  
Load Torque ◽  
Servo Systems ◽  
Characteristic Model ◽  
Second Order Sliding Mode

Considering the varying inertia and load torque in high speed and high accuracy servo systems, a novel discrete second-order sliding mode adaptive controller (DSSMAC) based on characteristic model is proposed, and a command observer is also designed. Firstly, the discrete characteristic model of servo systems is established. Secondly, the recursive least square algorithm is adopted to identify time-varying parameters in characteristic model, and the observer is applied to predict the command value of next sample time. Furthermore, the stability of the closed-loop system and the convergence of the observer are analyzed. The experimental results show that the proposed method not only can adapt to varying inertia and load torque, but also has good disturbance rejection ability and robustness to uncertainties.

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