Yousef Alipouri

ORCID: 0000-0003-4781-1137
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About
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Research Areas
  • Fault Detection and Control Systems
  • Advanced Control Systems Optimization
  • Control Systems and Identification
  • Metaheuristic Optimization Algorithms Research
  • Evolutionary Algorithms and Applications
  • Neural Networks and Applications
  • Advanced Multi-Objective Optimization Algorithms
  • Fuzzy Logic and Control Systems
  • Stability and Control of Uncertain Systems
  • Target Tracking and Data Fusion in Sensor Networks
  • Fuzzy Systems and Optimization
  • Adaptive Control of Nonlinear Systems
  • Building Energy and Comfort Optimization
  • Fuel Cells and Related Materials
  • Gaussian Processes and Bayesian Inference
  • Advanced Control Systems Design
  • Neural Networks Stability and Synchronization
  • Indoor Air Quality and Microbial Exposure
  • Vehicle Dynamics and Control Systems
  • Forecasting Techniques and Applications
  • Spectroscopy Techniques in Biomedical and Chemical Research
  • Sensor Technology and Measurement Systems
  • Reservoir Engineering and Simulation Methods
  • Industrial Vision Systems and Defect Detection
  • Mechanical Engineering and Vibrations Research

University of Alberta
2018-2023

University of Zanjan
2016-2017

Iran University of Science and Technology
2010-2016

University of Science and Technology
2014

University of Tabriz
2009

The dual neural extended Kalman filter (DNEKF) is proposed in this article to compensate for model inaccuracies and violations of noise assumption the EKF-based multirate sensor fusion. DNEKF employs two networks improve process state output predictions through simultaneous parameter estimations using vector augmentation. Taking advantage frequent but less accurate measurements infrequent more provides a update strategy training approach. Two numerical examples one industrial application...

10.1109/tim.2020.3041825 article EN IEEE Transactions on Instrumentation and Measurement 2020-12-07

10.1016/j.chemolab.2019.05.001 article EN Chemometrics and Intelligent Laboratory Systems 2019-05-24

Sensor fusion plays a critical role in improving estimation accuracy of process quality variables. In this article, the dual, neural, extended Kalman filter (DNEKF) and state model compensation (SNEKF) are synthesized to compensate for modeling errors (EKF)-based multirate sensor fusion. Specifically, is performed presence irregularly sampled, slow-rate measurements with time-varying time delays. The proposed algorithm estimates neural network parameters simultaneously through vector...

10.1109/tim.2021.3135537 article EN IEEE Transactions on Instrumentation and Measurement 2021-12-14

Genetic algorithms (GAs) are intelligent computational tools which their simplicity, accuracy and adaptable topology cause them to be used in globally minimum or maximum finding problems. Developing the GAs increase speed global of a cost function has been big challenge until now many variants GA evolved accomplish this goal. This paper presents two new sequential mutation method circular gene GA. These methods attain better final answer accompanied by lesser use evaluations comparison with...

10.1109/isda.2009.140 article EN 2009-01-01

In this study, a generalised minimum variance control (GMVC) method using the projection-based recurrent neural network (PRNN) is proposed to minimise error in output of non-linear plant. One main drawbacks conventional GMVC approaches lack systematic procedure deal with input constraints. PRNN employed for incorporating constraints index. This based on optimality conditions constrained problem and designed projection theorem. To formulate approach, by considering an ARMAX model system...

10.1049/iet-cta.2016.0141 article EN IET Control Theory and Applications 2016-09-30

Genetic algorithm and particle swarm optimization are two methods which can be used to find the global extremum of cost functions. The solely performance each method their specific characteristics in finding have been giving idea hybridization these many researchers. In this paper a new hybrid named Serial Algorithm Particle Swarm Optimization (SGAPSO) is introduced configuration discussed details. A set benchmark functions consisted high dimensional, multimodal low dimensional compare...

10.1109/isda.2010.5687252 article EN 2010-11-01

The Minimum Variance Lower Bound (MVLB) represents the best achievable controller capability in a variance sense. Estimation of MVLB for nonlinear systems confronts some difficulties. If one simply ignores these nonlinearities, there is danger over-estimating performance control loop rejecting uncertainties. Assuming that almost all models have uncertainties, this paper, has been estimated considering three types uncertainties: structural, parametric, and algorithmic. To achieve accurate...

10.1002/asjc.1307 article EN Asian Journal of Control 2016-05-13

To assess the performance of a control loop based on minimum variance (MV) benchmark, we need to calculate MV lower bound (MVLB). Even though there is plethora literature available for calculating MVLB linear systems, these methods are not suitable non‐linear systems. Furthermore, almost all real‐world applications have been encountered with input constraints. These constraints limit controllers' abilities in decreasing output variability. Therefore, existing computation methods, which do...

10.1049/iet-cta.2017.0760 article EN IET Control Theory and Applications 2018-01-11

This paper presents a control strategy for achieving robust minimum variance controller (MVC) by modelling uncertainty using type-2 fuzzy set and satisfying H ∞ norm specifications. In this paper, an MVC is designed considering three types of structural, parametric algorithmic uncertainties. Thus, interval utilized. utilizes one method to construct models the uncertain data model error approach. addition, sufficient condition developed that guarantees stability robustness controller. The...

10.1177/0142331215593627 article EN Transactions of the Institute of Measurement and Control 2015-07-10

Abstract This study presents a new fault detection scheme based on the probability density function (PDF) of system output. Unlike classical and diagnosis methods, in proposed method, distribution output is estimated online. To achieve this goal, an algorithm introduced to estimate PDF online using fuzzy logic. Furthermore, convergence investigated. Then, residual constructed that can show existence system. The main advantages method are robustness against measurement noise, even though it...

10.1002/asjc.1314 article EN Asian Journal of Control 2016-05-11

Summary This paper is concerned with distributed data‐driven observer design problem. The existing observers rely on a common assumption that all the information about system, and calculations based upon this are centralized. Therefore resulting algorithms cannot be applied to systems in which each local receives only part of output signal. On other hand, traditional model‐based state estimation methods generally assume processes decomposed according known process models, while approaches...

10.1002/acs.3100 article EN International Journal of Adaptive Control and Signal Processing 2020-02-26

This paper deals with the control performance assessment of distributed networked systems in presence communication delays. The best achievable steady-state framework linear quadratic Gaussian (LQG) is presented where delays serve as most fundamental limitation. In proposed LQG design, controllers and observers are designed simultaneously without using separation principle. And non-applicability principle investigated demonstrated order to handle time-varying delays, we propose use lower...

10.1080/00207179.2020.1775305 article EN International Journal of Control 2020-05-28
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