2024-03-04T22:00:46+03:30
http://joc.kntu.ac.ir/browse.php?mag_id=17&slc_lang=fa&sid=1
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
New Approach for Numerical Solution of Distributed Parameter Systems Optimal Control Problems
Seyed Mehdi
Abasi
gmail.com
Ali
Vahidian Kamyad
avkamyad@yahoo.com
Classical methods are not usually efficient, to solving nonlinear control problems and especially Nonlinear distributed parameter systems Optimal Control Problems (NOCP). In this paper we introduce a new approach for solving this class of problems by using NonLinear Programming Problem (NLPP). First, we transfer the original problem to a new problem in form of calculus of variations. The next step we discrete the new problem and solve it by using NLPP packages. Moreover, a NLPP is transferred to a Linear Programming Problem (LPP) which empowers us to use powerful LP software. Finally, efficiency of our approach is confirmed by some numerical examples.
optimal control
distributed parameter systems
calculus of variations
nonlinear programming
2012
6
01
1
7
http://joc.kntu.ac.ir/article-1-63-en.pdf
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
An Efficient Multiclass Classification Method Based on Classifier Selection Technique
Mohammad Ali
Bagheri
a.bagheri@modares.ac.ir
Gholamali
Montazer
montazer@modares.ac.ir
Individual classification models have recently been challenged by ensemble of classifiers, also known as multiple classifier system, which often shows better classification accuracy. In terms of merging the outputs of an ensemble of classifiers, classifier selection has not attracted as much attention as classifier fusion in the past, mainly because of its higher computational burden. In this paper, we propose a novel technique for improving classifier selection. In our method, the simple divide-and-conquer strategy is adapted in that a complex classification problem is divided into simpler binary sub-classification problems. We conduct extensive experiments on a series of multi-class datasets from the UCI (University of California, Irvine) repository and on odor database. The experimental results demonstrate the advanced performance of the proposed method.
classification
multi-class
ensemble system
classifier selection
odor recognition.
2012
6
01
9
19
http://joc.kntu.ac.ir/article-1-64-en.pdf
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
Direct Piecewise Affine Systems: A New Class of Hybrid Systems with Affine Dynamics and Regulable Switching Boundaries
Hamed
Molla Ahmadian Kaseb
hamed.mollaahmadian@gmail.com
Ali
Karimpour
karimpor@um.ac.ir
Naser
Pariz
n-pariz@um.ac.ir
For many applications, Piece-Wise Affine (PWA) systems are made from approximation of nonlinear dynamics by affine subsystems. Approximation and non-exact stability analysis are the main disadvantages of derived PWA system and as a solution, a new class of hybrid systems are introduced in this article. The proposed class is derived directly from switched model and without averaging then it is named as Direct PWA (DPWA). The new hybrid class has affine subsystems and regulable and/or constant switching boundaries. Many applications such as power electronics and process engineering can be modeled as proposed class. Some theorems are presented for stability analysis that are based on quadratic Lyapunov function. The problem of stability analysis and controller design is redounded to convex optimization in form of linear matrix inequality. The proposed model is compared with conventional hybrid models. Proposed model is used for modeling and stability analysis of a dc-dc resonant power converter.
Piece-Wise Affine System
Regulable Switching Boundary
Quadratic Lyapunov Function
Linear Matrix Inequality
2012
6
01
21
29
http://joc.kntu.ac.ir/article-1-65-en.pdf
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
Cooperative Decentralized Receding Horizon Control of Load-Frequency in a Two- Area Power System
Mohammad
Miranbeigi
m.miranbeigi@gmail.com
Behzad
Moshiri
.ac.ir
Ali
Miranbeigi
amrnbg@gmail.com
In the large scale applications, sometimes it is essential that the control theories be distributed or decentralized. In this paper, regarding large scale of power systems and advantages of model predictive control (MPC), a constraint predictive control method that is called “Receding Horizon Control (RHC)”, used for load frequency control in a two-area power system with two cooperating methods of agents. The main advantage of decentralized control is complexity reduction in computations. Moreover, in the application of centralized control, online computation of inputs (high dimensions) is complex, and non-flexible, and impractical. Finally using of simulations, centralized and decentralized control methods, cooperative methods are compared under a variety of disturbances.
Power systems
Model predictive control
Receding horizon control
Decentralized control
Disturbances.
2012
6
01
31
39
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
Implementation of Rough Neural Networks with Probabilistic Learning for Nonlinear System Identification
Seyyed Mohammad Javad
Alehasher
soheil.alehasher@gmail.com
Mohammad
Teshnehlab
teshnehlab@kntu.ac.ir
In this paper an improved rough neural network is presented for identification of chaotic system. Rough neural networks are a type of neural stractures that they are designed based on rough neurons. A rough neuron is considered as a pair of neurons that called lower boandry neuron and upper boandry neuron. Rough neuron approach, allows use of interval computing in neural networks, therefore it can be considered as a new opinion in designing neural networks. The same as multilayer perceptron, rough neural networks also can be trained using by back propagation algorithm based on gradient descending, however, this algorithm has problems such as local minima. In this paper, a new supervised learning method based on effective error of neuron is presented for training of neural networks, which it is called probabilistic learning. To evaluate this study, performance of rough neural network improved, and proposed learning algorithm have been examined in terms of error detection of chaotic time series.
Rough Neural Networks
Probabilistic Learning
Nonlinear System Identification.
2012
6
01
41
50
http://joc.kntu.ac.ir/article-1-67-en.pdf
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
A Non-PDC H∞ Output Feedback Controller Design for T-S Fuzzy Systems with Unknown Premise Variables and Control Constraints via LMI
Mohammad Hassan
Asemani
Vahid
Johari Majd
majd@modares.ac.ir
In this paper, non-PDC H∞ observer-based controller design for disturbed T-S fuzzy systems with unknown premise variables is addressed for the first time. Unlike the available non-PDC-based approaches, real state variables are not used in the controller equation. Moreover, using the descriptor redundancy approach, the observer and controller gains are calculated by solving some strict linear matrix inequalities (LMIs). A fuzzy Lyapunov function approach is utilized to obtain less conservative design conditions than previous methods. Furthermore, in order to satisfy an arbitrary upper bound on the absolute value of the control signal, additional design conditions are obtained which depend on the upper bounds of the initial states of the observer. The effectiveness of the proposed method is shown via a numerical simulation.
H∞ observer-based control
Descriptor redundancy approach
T-S fuzzy system
fuzzy Lyapunov function
linear matrix inequality (LMI).
2012
6
01
51
60
http://joc.kntu.ac.ir/article-1-68-en.pdf
Journal of Control
JoC
2008-8345
2538-3752
10.52547/joc
2012
6
1
A New Stability Preserving Interpolation Method in Robust Gain Scheduling Autopilot Design
Mohammad Javad
Moafi Madani
javadmadany@yahoo.com
Iman
Mohammadzaman
mohammadzaman@modares.ac.ir
This paper presents a new stability preserving interpolation technique for robust gain scheduling autopilot design. The interpolation method is based on interpolation in the common stability region of local controllers and generates a gain-scheduled controller that is stabilizing at every operating point of a closed loop system. For selection of stability region of local controllers, the notion of the v-gap metric and its connection to robust loop-shaping theory is used. The proposed method facilitates the design of gain-scheduled controllers that preserves stability of the closed loop system. The simulation results given show the generality and effectivneness of the proposed control strategy in terms of the stability, performance and robustness, of the system.
Robust autopilot
stable interpolation
gain scheduling controller
v-gap metric
2012
6
01
61
72
http://joc.kntu.ac.ir/article-1-69-en.pdf