scholarly journals Self-Tuning Predictive Control of Nonlinear Servo-Motor

2010 ◽  
Vol 61 (6) ◽  
pp. 365-372 ◽  
Author(s):  
Vladimír Bobál ◽  
Petr Chalupa ◽  
Marek Kubalčík ◽  
Petr Dostál
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Laboratory Model ◽  
Servo Motor ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Model Control ◽  
Self Tuning

Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

A new robust non-linear self-tuning predictive control using neural networks

1997 ◽  
Vol 211 (6) ◽  
pp. 439-446 ◽  
Author(s):  
K Y Zhu ◽  
X F Qin ◽  
T Y Chai
Keyword(s):  
Predictive Control ◽  
Feedback Linearization ◽  
Control Algorithm ◽  
Linear Part ◽  
Taylor Series Expansion ◽  
Recursive Least Squares ◽  
Non Linear ◽  
Self Tuning ◽  
Non Linear System ◽  
Robust Predictive Control

An adaptive version of a novel robust predictive control for a class of non-linear systems is presented. The non-linear system is separated into linear and non-linear parts by Taylor series expansion and then the latter part is identified by a neural network, which is then compensated in the control algorithm such that feedback linearization can be achieved. Thus the influence of the non-linearity and model uncertainties may be eliminated or reduced. In the case of time-varying or unknown systems the linear part of the system model is estimated by an RLS (recursive least-squares) algorithm. Simulation results show that the proposed scheme may improve the system performance.

Identification and Control of Nonlinear Laboratory Model Amira DR 300

2014 ◽  
Vol 611 ◽  
pp. 284-293
Author(s):  
Petr Navrátil
Keyword(s):  
Least Squares ◽  
Recursive Least Squares ◽  
Laboratory Model ◽  
Adaptive Controllers ◽  
Real Model ◽  
Model Control ◽  
Simulation Testing ◽  
Self Tuning ◽  
Adaptive Forgetting ◽  
And Control

This article deals with identification and a control design of nonlinear laboratory model Amira DR 300 in a real time. It mentions a mathematical description of the model, its static and dynamical characteristics. Using an experimental method of identification a model has been created, which is suitable for the purpose of simulation testing of non-adaptive as well as adaptive controllers. These were tested as simple adaptive, i.e. non-adaptive controllers, namely in both simulation ways and at a real model control. For the purpose of a non-adaptive control, system parameters were determined by off-line method of the least squares. In an identification part of self-tuning controllers a method of recursive least squares with directional adaptive forgetting factor has been used.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

scholarly journals Automatic Control of the Middle Route Project for South-to-North Water Transfer Based on Linear Model Predictive Control Algorithm

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1873 ◽  
Author(s):  
Kong ◽  
Quan ◽  
Yang ◽  
Song ◽  
Zhu
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Automatic Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Water Transfer ◽  
Water Delivery ◽  
Real Time Control ◽  
Time Control ◽  
North Water

The application of automatic control to irrigation canals is an important means of improving the efficiency of water delivery. The Middle Route Project (MRP) for South-to-North Water Transfer, the largest water transfer project in China, is currently under manual control. Given the complexity of the MRP, there is an urgent need to adopt some form of automatic control. This paper describes the application of model predictive control (MPC), a popular real time control algorithm particularly suited to the automatic control of multi-pool irrigation water delivery systems, to the MRP using a linear control model. This control system is tested in part of the MRP by means of numerical simulations. The results show that the control system can deal with both known and unknown disturbances, albeit with a degree of resonance in some short pools. However, it takes a long time for the MRP to reach a stable state under the MPC system and the calculation time for the whole MRP network would be too long to satisfy the requirements of real-time control. Suggestions are presented for the construction of an automatic control system for the MRP.

Real-Time Self-Tuning Speed Controller: Performance Comparison between Engine Fuelled with Palm Methyl Esters and Petroleum Diesel

2015 ◽  
Vol 815 ◽  
pp. 408-412
Author(s):  
M.N. Azuwir ◽  
Mohd Sazli Saad ◽  
Mohd Zakimi Zakaria
Keyword(s):  
Real Time ◽  
Least Squares Method ◽  
Methyl Esters ◽  
Performance Comparison ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Automotive Engine ◽  
Speed Controller ◽  
Self Tuning

This paper investigates the performance of a real-time self-tuning speed controller designed to track and regulate at various engine speeds. The controller was tested with an automotive engine fuelled with petroleum diesel and and palm oil biodiesel (Palm Methyl Esters) within speed range of 1800 rpm to 2400 rpm. A self-tuning control algorithm based on pole assignment method together with on-line model parameters estimation strategy based on the recursive least squares method are adopted. The ability of the controller to track, regulate at various engine speed and also to reject disturbances applied for both type of fuel are compared and presented. The results confirmed that the controller performed very satisfactorily.

A new simulation scheme for self-tuning adaptive control of robot manipulators

Robotica ◽  
1991 ◽  
Vol 9 (3) ◽  
pp. 335-339 ◽  
Author(s):  
Q. Wang ◽  
D. R. Broome
Keyword(s):  
Adaptive Control ◽  
Controller Design ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Degree Of Freedom ◽  
Dynamic Equations ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Self Tuning ◽  
Simulation Scheme

SUMMARYIn most dynamic adaptive control simulation of robotic manipulators, the Langrange–Euler (L–E) dynamic equations are first piecewise linearized about the desired reference and then discretized and rewritten in a state space form. This makes things very complicated and it is easy to make errors. What is more is that with a different reference this work must be done again. A new simulation scheme – Backward Recursive Self-Tuning Adaptive (BRSTA) – as it will be called, is suggested in this paper for adaptive controller design of robot manipulators. A two degree of freedom robot manipulator is used to verify the scheme in the condition of highly nonlinear and highly coupled system. A one degree of freedom robot manipulator is used for comparing both the forward and backward methods. The main advantages of this scheme include that it can be used for evaluating the self-tuning adaptive control laws and provide the initial process parameters for real-time control. And it is concluded here that the Newton–Euler (N–E) dynamic equations are equally well qualified as the Langrange–Euler (L-E) equations for the simulation of self-tuning adaptive control of robot manipulators.

scholarly journals Intelligent Positioning Plate Predictive Control and Concept of Diagnosis System Design

2017 ◽  
Vol 15 (1-2) ◽  
Author(s):  
Matej Oravec ◽  
Anna Jadlovská
Keyword(s):  
Artificial Intelligence ◽  
Distributed Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Control Algorithms ◽  
Laboratory Model ◽  
Distributed Control System ◽  
Diagnosis System ◽  
Intelligent Positioning ◽  
Algorithm Verification

<span lang="EN-US">This paper presents design of the predictive control algorithms, which are verified using the simulation and laboratory model of the Intelligent Positionig Plate. The results of the predictive control algorithm verification are presented also in this article. The created tool called the <em>IPPtools</em> is based on the designed predictive control algorithms and it is shortly presented. A part of the paper is dedicated to the concept of the diagnosis system, which is designed and implemented into the 5-level Distributed Control System of the Department of the Cybernetics and Artificial Intelligence. Possibility how to modify the predictive control algorithms using diagnosis system is also stated in the last section of this paper.</span>

Self-Tuning Control of a Coal-Fired Fluidized Bed Combustor

1992 ◽  
Vol 114 (1) ◽  
pp. 139-144
Author(s):  
K. W. Junk ◽  
R. R. Fullmer ◽  
R. C. Brown
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Design Procedure ◽  
Gain Control ◽  
Recursive Least Squares ◽  
Self Tuning ◽  
Improved Performance ◽  
Secondary Air ◽  
Classical Control ◽  
Fluidized Bed Combustor

This paper investigates the temperature control of a two-bed fluidized combustor using a self-tuning control algorithm to vary secondary air flow rate. The controller consists of a recursive least-squares parameter estimator, an observer, and a linear optimal control design procedure. This combination enables the controller to estimate the system parameters and update the feedback gains when necessary. Further, this study addresses the tracking form of optimal control, accomplished by augmenting the state vector with an integrator. The self-tuning control algorithm was compared with a simple PI controller, which was tuned using the Ziegler-Nichols method. In this study, self-tuning control provided improved performance over classical control. Compared with conventional, constant-gain control, self-tuning control reduced steady-state variance by a factor of 6.67 while maintaining good tracking characteristics.

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