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Approximation of titration curves by artificial neural networks and its application to pH control
, Article Scientia Iranica ; Volume 6, Issue 5 , 2000 , Pages 82-91 ; 10263098 (ISSN) ; Shahrokhi, M ; Sharif University of Technology
Sharif University of Technology
2000
Abstract
Advanced model-based control of pH processes is noticeably a chemical modeling issue, because it can have a profound effect on the attainable control quality. This is especially the case when the pH regulation of streams, consisting of hundreds of constituents with varying concentrations, is encountered. The severe non-linear behavior of pH processes is reflected in the titration curve of the process stream. The performances of all model-based controllers are highly dependent on the accuracy of the model. Considering a great number of parameters such as dissociation constants, solubility products and characteristic concentrations places the designer in a dilemma of choosing between...
Modeling and velocity control of a-shape microrobot with adaptive neural network controller
, Article ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) ; Vol. 4A, issue , 2014 ; Shirazi, M. J ; Vossoughi, G. R ; Salarieh, H ; Sharif University of Technology
Abstract
Design and control of micro robots have been one of the interesting fields in robotics in recent years. One class of these micro robots is the legged robots. Various designs of legged robots have been proposed in the literature. All designs rely on friction for locomotion. In this paper dynamic model of a planar two-legged micro robot is presented using Luger friction model, and an adaptive neural controller used to control the robot to improve robustness and velocity of the robot. As mentioned earlier, friction plays an important role in locomotion of the legged robots. However, especially in legged micro robots, it is difficult to model the frictional force correctly since environmental...
Position control of an ultrasonic motor using generalized predictive control
, Article 8th International Conference on Control, Automation, Robotics and Vision (ICARCV), Kunming, 6 December 2004 through 9 December 2004 ; Volume 3 , 2004 , Pages 1957-1962 ; 0780386531 (ISBN) ; Haeri, M ; Sharif University of Technology
2004
Abstract
Ultrasonic motors (USM) possess heavy nonlinear, and load dependent characteristics such as dead-zone and saturation reverse effects, which vary with driving conditions. These properties have made the position/velocity control of USM a difficult and challenging task. In this paper, a generalized model predictive (GPC) controller for precise USM position control is suggested. Simulation results indicate improved performance of the motor for both set point tracking and disturbance rejection. Since the motor and the controller both are of type one, the applied saturation would cause in wind up phenomenon. This drawback is removed by implementing the Quadratic GPC controller. © 2004 IEEE
Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
, Article Chemometrics and Intelligent Laboratory Systems ; Volume 217 , 2021 ; 01697439 (ISSN) ; Pourdarbani, R ; Rohban, M. H ; García Mateos, G ; Arribas, J. I ; Sharif University of Technology
Elsevier B.V
2021
Abstract
In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content (mg l−1) in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and...
Variable speed wind turbine power control: A comparison between multiple MPPT based methods
, Article International Journal of Dynamics and Control ; Volume 10, Issue 2 , 2022 , Pages 654-667 ; 2195268X (ISSN) ; Moradi, H ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2022
Abstract
Reducing the renewable energy costs is necessary for the competition with the fossil energies and control strategies have great impact on the efficiency of wind machines. In the wind turbine industry, a practical approach is to maximize the energy capture of a wind machine by optimizing the power coefficient in the under-rated situations. In this paper, with the main objective of maximizing the energy capture in the second region, four different control strategies are compared in the presence of uncertainties. The proposed control methods are compared based on their power capture and robustness against probable uncertainties in the structural and environmental parameters. A two-mass...
Design and Implementation of Intelligent Memory Control for Flexible Magnetic Robot
, M.Sc. Thesis Sharif University of Technology ; Arghavani Hadi, Jamal (Supervisor) ; Zohoor, Hassan (Supervisor) ; Nejat Pishkenari, Hossein (Co-Supervisor)
Abstract
The flexible magnetic robot is used for minimally invasive surgeries where there is a complex environment. Precise control of the position of the end of the robot and quick adaptation of the robot to uncertainties are among the most important challenges in this field, where the controller is responsible for compensating the error and controlling the position of the end of the robot. Now, is it possible to create a kind of memory in the system that when faced with the same or similar errors in the same situations or close to previous errors, the system uses its past and compensates the error? The importance of this work is that the response speed of the system is increased and the system can...
Introducing neural networks as a computational intelligent technique
, Article Applied Mechanics and Materials ; Vol. 464 , 2014 , pp. 369-374 ; ISSN: 16609336 ; Entessari, F ; Osgouie, K. G ; Rashnoodi, A. R ; Sharif University of Technology
Abstract
Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures
QSAR analysis of platelet-derived growth inhibitors using GA-ANN and shuffling crossvalidation
, Article QSAR and Combinatorial Science ; Volume 27, Issue 6 , 2008 , Pages 750-757 ; 1611020X (ISSN) ; Asadollahi Baboli, M ; Sharif University of Technology
2008
Abstract
Quantitative Structure - Activity Relationship (QSAR) models for the inhibition action of some 1-phenylbenzimidazoles on platelet-derived growth are constructed using Genetic Algorithm and Artificial Neural Network (GA-ANN) method. The statistical parameters of R2 and root-mean-square error are 0.82 and 0.21, respectively using this method. These parameters show a considerable improvement compared to the stepwise multiple linear regression combined with ANN (stepwise MLR-ANN). Ten-fold shuffling crossvalidations are carried out to select the most important descriptors. Five descriptors of index of Balaban (J), average molecular weight (AMW), 3D-Wiener index (W3D), mean atomic van der Waals...
Implementation of an optimal control strategy for a hydraulic hybrid vehicle using CMAC and RBF networks
, Article Scientia Iranica ; Volume 19, Issue 2 , 2012 , Pages 327-334 ; 10263098 (ISSN) ; Foumani, M. S ; Boroushaki, M ; Sharif University of Technology
2012
Abstract
A control strategy on a hybrid vehicle can be implemented through different methods. In this paper, the Cerebellar Model Articulation Controller (CMAC) and Radial Basis Function (RBF) neural networks were applied to develop an optimal control strategy for a split parallel hydraulic hybrid vehicle. These networks contain a nonlinear mapping, and, also, the fast learning procedure has made them desirable for online control. The RBF network was constructed with the use of the K-mean clustering method, and the CMAC network was investigated for different association factors. Results show that the binary CMAC has better performance over the RBF network. Also, the hybridization of the vehicle...
Neural networks control of autonomous underwater vehicle
, Article ICMEE 2010 - 2010 2nd International Conference on Mechanical and Electronics Engineering, Proceedings, 1 August 2010 through 3 August 2010 ; Volume 2 , August , 2010 , Pages V2117-V2121 ; 9781424474806 (ISBN) ; Khayyat, A. A ; Ghaemi Osgouie, K ; Sharif University of Technology
2010
Abstract
This paper describes a neural network controller for autonomous underwater vehicles (AUVs). The designed online multilayer perceptron neural network (OMLPNN) calculates forces and moments in earth fixed frame to eliminate the tracking errors of AUVs whose dynamics are highly nonlinear and time varying. Another OMLPNN has been designed to generate an inverse model of AUV, which determine the appropriate propeller's speed and control surfaces' angles receiving the forces and moments in the body fixed frame. The designed approximation based neural network controller with the use of the backpropagation learning algorithm has advantages and robustness to control the highly nonlinear dynamics of...
Modeling and Control of gas turbine combustor with dynamic and Adaptive Neural networks
, Article International Journal of Engineering, Transactions B: Applications ; Volume 21, Issue 1 , 2008 , Pages 71-84 ; 1728-144X (ISSN) ; Lahroodi, M ; Sharif University of Technology
Materials and Energy Research Center
2008
Abstract
This paper presents an Artificial Neural Network (ANN)-based modeling technique for prediction of outlet temperature, pressure and mass flow rate of gas turbine combustor. Results obtained by present modeling were compared with those obtained by experiment. The results showed the effectiveness and capability of the proposed modeling technique with reasonable accuracies of about 95 percent. This paper describes a nonlinear SVFAC (State Vector Feedback Adaptive Control) controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the effect of nonlinear factors contained in controller. The controller is adaptively trained to force the...
Modeling and intelligent control of a robotic gas metal arc welding system
, Article Scientia Iranica ; Volume 15, Issue 1 , 2008 , Pages 75-93 ; 10263098 (ISSN) ; Eftekharian, A. A ; Sharif University of Technology
Sharif University of Technology
2008
Abstract
Welding is an important manufacturing process that can be automated and optimized. This paper focuses on the development of a robotic arc welding system, wherein a three-degree-of-freedom Selective Compliance Assembly Robot Arm (SCARA) is interfaced to a Gas Metal Arc Welding (GMAW) process. The entire system is composed of a robot linked to a GMAW system. Set points are derived using the desired mass and heat input, along with the weld speed. The stick-out and the current of the welding process are controlled using an Adaptive Neural Network Controller (ANNC). The trajectory of the robot or the weld profile is also controlled by implementing a Mixed Fuzzy-GA Controller (MFGAC) on a...
Variable speed wind turbine power control: A comparison between multiple MPPT based methods
, Article International Journal of Dynamics and Control ; 2021 ; 2195268X (ISSN) ; Moradi, H ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2021
Abstract
Reducing the renewable energy costs is necessary for the competition with the fossil energies and control strategies have great impact on the efficiency of wind machines. In the wind turbine industry, a practical approach is to maximize the energy capture of a wind machine by optimizing the power coefficient in the under-rated situations. In this paper, with the main objective of maximizing the energy capture in the second region, four different control strategies are compared in the presence of uncertainties. The proposed control methods are compared based on their power capture and robustness against probable uncertainties in the structural and environmental parameters. A two-mass...
Robust DTC control of doubly-Fed induction machines based on input-output feedback linearization using recurrent neural networks
, Article Journal of Power Electronics ; Volume 11, Issue 5 , 2011 , Pages 719-725 ; 15982092 (ISSN) ; Hashemnia, M. N ; Faiz, J ; Sharif University of Technology
2011
Abstract
This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine...
Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks
, Article Chemosphere ; Volume 72, Issue 5 , 2008 , Pages 733-740 ; 00456535 (ISSN) ; Kyani, A ; Sharif University of Technology
2008
Abstract
The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison...