Research Article | 27-December-2017
The problem of multi-objective robust feedback linearization controller design of nonlinear system with parametric uncertainties is solved in this paper. The main objective of this paper is to propose an optimal technique to design a robust feedback linearization controller with multi-objective genetic algorithm. A nonlinear system is considered as a benchmark and feedback linearization controller is designed for deterministic and probabilistic model of the benchmark. Three and four conflicting
A. Hajiloo,
M. samadi,
N. Nariman-Zadeh
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 214–237
research-article | 23-August-2019
materials have a nonlinear response. A typical nonlinear response curve of a sensor can be represented by nth-order polynomial function, the order of which depends on the nonlinearity value. A typical third-order response (y) can be represented by:
(1)
y
=
a
3
x
3
+
a
2
x
2
+
a
1
x
+
a
0
,
where x is the measurement parameter; and a
3, a
2, a
1, and a
0 are the characteristic coefficients of the sensor.
By the linearization circuit, the nonlinear response curve
Tarikul Islam,
S. C. Mukhopadhyay
International Journal on Smart Sensing and Intelligent Systems
, Volume 12 , ISSUE 1, 1–21
Article | 01-June-2016
system and a thermoelectric cooling device to control the room temperature and the temperature that is read out using the thermopile, respectively. This is based on the automation of the data collection procedure and the characterization of the thermistor that is used to measure the temperature of the thermopile. The result is an experimental operating surface, from which a linearization model was derived.
J-S. Botero V,
A. Salazar,
L-J. Morantes G.
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 637–650
Research Article | 01-September-2011
Precise control of electro-hydraulic actuator (EHA) system has been an interesting subject due to its nonlinearities and uncertainties characteristics. Good control can be designed when precise model of the system is available. Linear ARX modelling has widely been applied and satisfying result has been obtained, through linearization process. The objective of this paper is to compare ARX model with nonlinear ANFIS (Adaptive Neuro-Fuzzy Inference System) model, which can represent the real EHA
M. F. Rahmat,
T. G. Ling,
A. R. Husain,
K. Jusoff
International Journal on Smart Sensing and Intelligent Systems, Volume 4 , ISSUE 3, 440–453
research-article | 01-July-2020
linear model (Ataka et al., 2013; Al-Younes et al. 2010). The dynamics of quadrotor is divided into three subsystems: attitude, altitude, and positions to design the backstepping and augmented backstepping controller (Madani and Benallegue, 2006; Behnamgol et al., 2016; Zhang et al., 2017). The direct feedback linearization and adaptive feedback linearization for quadrotor are designed (Lee et al., 2009; Mukherjee and Waslander, 2012). The attitude controller is designed based on quantitative
Ahmed Eltayeb,
Mohd Fua’ad Rahmat,
Mohd Ariffanan Mohd Basri
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–13
Research Article | 02-November-2017
Almost all type of moisture sensors has a non-linear response. With out linearization it is difficult to apply such a non-linear sensor in electronics circuits, specially in analog electronics. Non linear sensor and transducers characteristic can be linearized using analog electronics or digital electronics. In this paper a method of linearization of such non-linear sensors characteristics using analog electronics is described. Theoretical explanation of the methods and its verification by
Dilip Kumar Ghara,
Debdulal Saha,
Kamalendu Sengupta
International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 4, 955–969
Research Article | 13-December-2017
M. NADI,
C. MARGO,
M. KOUIDER,
J. PRADO,
D. KOURTICHE
International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 1, 21–33
Research Article | 12-December-2017
Aman Tyagi,
Arrabothu Apoorv Reddy,
Jasmeet Singh,
Shubhajit Roy Chowdhury
International Journal on Smart Sensing and Intelligent Systems, Volume 4 , ISSUE 1, 94–111
Research Article | 13-December-2017
mathematical modeling that includes the linearization of the model in order to be used with the linear controllers. The works followed with designing those controllers and simulating it in MATLAB. Each controller performance will be analyzed and compared which is based on common criteria’s of the step response. An appropriate graphic user interface (GUI) has been developed to view the animation of the ball and beam system.
Mohd Fuaad Rahmat,
Herman Wahid,
Norhaliza Abdul Wahab
International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 1, 45–60
Research Article | 13-December-2017
Dilip Kumar Ghara,
Debdulal Saha,
Kamalendu Sengupta
International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 3, 784–798
Article | 01-September-2014
Chen Jun,
Su Kaixiong,
Huang Xiyuan,
Yan Lirong
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1421–1435
Article | 06-November-2017
The ball and beam system is a laboratory equipment with high nonlinearity in its dynamics. The aims of this research are to model the ball and beam system considering nonlinear factors and coupling effect and to design controllers to control the ball position. The LQR is designed considering two Degrees-of-Freedom and coupling dynamics. The parameters of the LQR are tuned using Genetic Algorithm (GA). Jacobian linearization method is used to linearize the system around operating-point. Due to
Mohammad Keshmiri,
Ali Fellah Jahromi,
Abolfazl Mohebbi,
Mohammad Hadi Amoozgar,
Wen-Fang Xie
International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 1, 14–35
Article | 31-December-2020
Piotr SZABLATA,
Paweł ŁĄKOWSKI,
Janusz POCHMARA
Transport Problems, Volume 15 , ISSUE 4, Part 1, 105–116