Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859723
Md. Abdul Alim Sheikh, Alok Kole, T. Maity
Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour based segmentation are applied to find out if a traffic sign is present. If present, the sign will be highlighted, normalized in size and then classified. Neural network is used for classification purposes. For evaluation purpose, four type traffic signs such as Stop Sign, No Entry Sign, Give Way Sign, and Speed Limit Sign are used. Altogether 300 sets images, 75 sets for each type are used for training purposes. 200 images are used testing. The experimental results show the detection rate is above 90% and the accuracy of recognition is more than 88%.
{"title":"Traffic sign detection and classification using colour feature and neural network","authors":"Md. Abdul Alim Sheikh, Alok Kole, T. Maity","doi":"10.1109/ICICPI.2016.7859723","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859723","url":null,"abstract":"Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour based segmentation are applied to find out if a traffic sign is present. If present, the sign will be highlighted, normalized in size and then classified. Neural network is used for classification purposes. For evaluation purpose, four type traffic signs such as Stop Sign, No Entry Sign, Give Way Sign, and Speed Limit Sign are used. Altogether 300 sets images, 75 sets for each type are used for training purposes. 200 images are used testing. The experimental results show the detection rate is above 90% and the accuracy of recognition is more than 88%.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"25 1","pages":"307-311"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79870336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859694
K. Chakraborty, Gitanjali Saha
Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.
{"title":"Off-line voltage security assessment of power transmission systems using UVSI through artificial neural network","authors":"K. Chakraborty, Gitanjali Saha","doi":"10.1109/ICICPI.2016.7859694","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859694","url":null,"abstract":"Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"24 1","pages":"158-162"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83647611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859707
D. Santra, A. Mukherjee, K. Sarker, D. Chatterjee
This paper presents a novel solution of convex and non-convex economic load dispatch (ELD) problem of small scale thermal power system using a hybrid soft computing approach. The solution method involves a combination of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms where the latter is used to tune the solution obtained by the former towards finding global optima. The proposed approach is found useful in finding economic dispatch in a 3-generator 5-bus system by considering generator capacity constraints, transmission loss, ramp rate limits, prohibited operating zones and valve point loading. Six test cases have been studied in a simulated environment. The paper shows that by applying the PSO-ACO hybrid algorithm 300MW power demand can be successfully met at minimum generation cost incurring minimum transmission loss.
{"title":"Hybrid PSO-ACO algorithm to solve economic load dispatch problem with transmission loss for small scale power system","authors":"D. Santra, A. Mukherjee, K. Sarker, D. Chatterjee","doi":"10.1109/ICICPI.2016.7859707","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859707","url":null,"abstract":"This paper presents a novel solution of convex and non-convex economic load dispatch (ELD) problem of small scale thermal power system using a hybrid soft computing approach. The solution method involves a combination of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms where the latter is used to tune the solution obtained by the former towards finding global optima. The proposed approach is found useful in finding economic dispatch in a 3-generator 5-bus system by considering generator capacity constraints, transmission loss, ramp rate limits, prohibited operating zones and valve point loading. Six test cases have been studied in a simulated environment. The paper shows that by applying the PSO-ACO hybrid algorithm 300MW power demand can be successfully met at minimum generation cost incurring minimum transmission loss.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"48 1 1","pages":"226-230"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88657260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859664
P. Deori, A. B. Kandali
In this paper a new method of Variable Structure Control (VSC) using Multiquadric Radial Basis Function Neural Network (MQRBFNN) identifier is proposed to achieve chaos synchronization of underactuated gyroscope (master-slave) system with known and unknown system parameters. Gyroscopes are nonlinear underactuated systems which show chaotic motions. Chaos control is achieved by designing three control laws (constant, exponential and power rate reaching law) using Lyapunov stability criteria using VSC under known system parameters. For unknown system parameters, MQRBFNN identifiers are trained for online estimators. In this work it is shown that VSC using power rate reaching law, achieves better synchronization under known system parameters. It is also found that with MQRBFNN identifier based VSC using power rate reaching law, good synchronization is achieved under unknown system parameters.
{"title":"Synchronization and chaos control of heavy symmetric chaotic unknown gyroscope using MQRBVSC","authors":"P. Deori, A. B. Kandali","doi":"10.1109/ICICPI.2016.7859664","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859664","url":null,"abstract":"In this paper a new method of Variable Structure Control (VSC) using Multiquadric Radial Basis Function Neural Network (MQRBFNN) identifier is proposed to achieve chaos synchronization of underactuated gyroscope (master-slave) system with known and unknown system parameters. Gyroscopes are nonlinear underactuated systems which show chaotic motions. Chaos control is achieved by designing three control laws (constant, exponential and power rate reaching law) using Lyapunov stability criteria using VSC under known system parameters. For unknown system parameters, MQRBFNN identifiers are trained for online estimators. In this work it is shown that VSC using power rate reaching law, achieves better synchronization under known system parameters. It is also found that with MQRBFNN identifier based VSC using power rate reaching law, good synchronization is achieved under unknown system parameters.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"6 1","pages":"12-16"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79541728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859698
Saleh S Almasabi, J. Mitra
Phasor measurement units (PMUs) have revolutionized power systems monitoring and control. By providing higher resolution measurements which provides better situational awareness, those time-synchronized measurements have enabled the development of better controls and operations for power systems. This paper discusses the basics of PMUs and their applications, mainly state estimation. The paper also presents a Kalman filter approach for state estimation. The NE 39-bus system is used under different circumstances to show the accuracy and robustness of the Kalman filter approach.
{"title":"An overview of synchrophasors and their applications in smart grids","authors":"Saleh S Almasabi, J. Mitra","doi":"10.1109/ICICPI.2016.7859698","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859698","url":null,"abstract":"Phasor measurement units (PMUs) have revolutionized power systems monitoring and control. By providing higher resolution measurements which provides better situational awareness, those time-synchronized measurements have enabled the development of better controls and operations for power systems. This paper discusses the basics of PMUs and their applications, mainly state estimation. The paper also presents a Kalman filter approach for state estimation. The NE 39-bus system is used under different circumstances to show the accuracy and robustness of the Kalman filter approach.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"427 1","pages":"179-183"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75033071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859681
Nilava Debabhuti, Sk Babar Ali, B. Ghatak, Vinita Parasrampuria, Sk. Md. Rafiqul, Pranav Agarwal Hassan, Sudipto Dutta Gupta, Prolay Sharma, B. Tudu, R. Bandyopadhyay, N. Bhattacharyya
Ripeness monitoring of tomato is very important due to the biosynthesis of a carotenoid Lycopene that increases with the maturity process. The ripening stages of tomato can be estimated by the smell it generates during particular maturity stages. This paper aims the detection of Trans-2-hexenal, an important ripening volatile of tomato with the help of quartz crystal microbalance sensor. A coating material formed by the chemical reaction process of pentafluorobenzyl bromide (PFBBr), polyethylene glycol 6000 (PEG 6000), tri-ethyl amine and chloroform has been developed for this purpose. Moreover different characterization of the sensor has been performed e.g. sensitivity, selectivity, repeatability, reproducibility and the perforamance is verified.
{"title":"Development of a QCM sensor for detection of trans-2-hexenal in tomatoes","authors":"Nilava Debabhuti, Sk Babar Ali, B. Ghatak, Vinita Parasrampuria, Sk. Md. Rafiqul, Pranav Agarwal Hassan, Sudipto Dutta Gupta, Prolay Sharma, B. Tudu, R. Bandyopadhyay, N. Bhattacharyya","doi":"10.1109/ICICPI.2016.7859681","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859681","url":null,"abstract":"Ripeness monitoring of tomato is very important due to the biosynthesis of a carotenoid Lycopene that increases with the maturity process. The ripening stages of tomato can be estimated by the smell it generates during particular maturity stages. This paper aims the detection of Trans-2-hexenal, an important ripening volatile of tomato with the help of quartz crystal microbalance sensor. A coating material formed by the chemical reaction process of pentafluorobenzyl bromide (PFBBr), polyethylene glycol 6000 (PEG 6000), tri-ethyl amine and chloroform has been developed for this purpose. Moreover different characterization of the sensor has been performed e.g. sensitivity, selectivity, repeatability, reproducibility and the perforamance is verified.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"5 1","pages":"93-97"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75196962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859710
R. Bose, Kaniska Samanta, S. Chatterjee
In this contribution, classification of two main neuromuscular diseases namely Myopathy and Neuropathy and Healthy signals is performed using cross-correlation based feature extraction technique. For this purpose, cross-correlation of Healthy, Myopathy and Neuropathy disease EMG signal is done with a reference Healthy signal. Selective features like Hjorth, Adaptive Autoregressive and statistical features comprising mean, standard deviation and power are extracted from the cross-correlated signals. Support Vector Machine(SVM) and k-Nearest Neighbor(kNN) are the two classifiers used for this work. Highest classification accuracy of 100% is obtainedby SVM using Gaussian Radial Basis Function (RBF) as the kernel function with AAR and all combined features as the feature set. For kNN, k=4 yields best result of 100% accuracy using the combined feature set.
{"title":"Cross-correlation based feature extraction from EMG signals for classification of neuro-muscular diseases","authors":"R. Bose, Kaniska Samanta, S. Chatterjee","doi":"10.1109/ICICPI.2016.7859710","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859710","url":null,"abstract":"In this contribution, classification of two main neuromuscular diseases namely Myopathy and Neuropathy and Healthy signals is performed using cross-correlation based feature extraction technique. For this purpose, cross-correlation of Healthy, Myopathy and Neuropathy disease EMG signal is done with a reference Healthy signal. Selective features like Hjorth, Adaptive Autoregressive and statistical features comprising mean, standard deviation and power are extracted from the cross-correlated signals. Support Vector Machine(SVM) and k-Nearest Neighbor(kNN) are the two classifiers used for this work. Highest classification accuracy of 100% is obtainedby SVM using Gaussian Radial Basis Function (RBF) as the kernel function with AAR and all combined features as the feature set. For kNN, k=4 yields best result of 100% accuracy using the combined feature set.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"32 1","pages":"241-245"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75059266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/ICICPI.2016.7859679
A. Paul, Madhurima Panja, M. Bagchi, Nairit Das, R. Mazumder, S. Ghosh
In this 21st century, there has been a remarkable change in the field of Room Automation due to the introduction of improved voice recognition & wireless technologies. These systems are supposed to be implemented in the existing infrastructure of any home without any kind of changes in the existing connections. This system is most suitable for elderly and physically challenged person those who have difficulty in moving around from place to place. The voice recognizing feature of this system also provides a security aspect to this system. The physically challenged [1] persons would be able to control various home [2] appliances by their mere voice commands according to their need and comfort. The Room Automation system is intended to control lights and other electrical appliances in a room using voice commands. So in this project our aim is to design and implement a voice recognition wireless based room automation system.
{"title":"Voice recognition based wireless room automation system","authors":"A. Paul, Madhurima Panja, M. Bagchi, Nairit Das, R. Mazumder, S. Ghosh","doi":"10.1109/ICICPI.2016.7859679","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859679","url":null,"abstract":"In this 21st century, there has been a remarkable change in the field of Room Automation due to the introduction of improved voice recognition & wireless technologies. These systems are supposed to be implemented in the existing infrastructure of any home without any kind of changes in the existing connections. This system is most suitable for elderly and physically challenged person those who have difficulty in moving around from place to place. The voice recognizing feature of this system also provides a security aspect to this system. The physically challenged [1] persons would be able to control various home [2] appliances by their mere voice commands according to their need and comfort. The Room Automation system is intended to control lights and other electrical appliances in a room using voice commands. So in this project our aim is to design and implement a voice recognition wireless based room automation system.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"150 1","pages":"84-88"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77379465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electro Mechanical Miniature Circuit Breaker (MCB) has been introduced under LT Protection Scheme since 1910 for protection against short circuit faults. In the present days a good number of areas have been explored on intelligent circuit breakers like AI incorporated Vacuum Circuit Breaker, SF6, and LV DC Breaker and so on. The aforesaid breakers are single port based and for a single load. In this paper discussion has been drawn about an Intelligent MCB with multiple ports availability. The Fuzzy Inference System based MCB has been developed under MATLAB environment. Hardware developed here is a Microcontroller belongs to ATMEL family. By developing one script file under MATLAB Environment and by introducing Microcontroller a single shot scope has been implemented for validating the outputs obtained from the output pins of the concerned microcontroller. For an alternate way to record outputs with respect to time in continuous form another scope has also been taken into account by introducing Microcontroller as well.
{"title":"FIS incorporated microcontroller based MCB","authors":"Soumyadeep Samonto, Sagarika Pal, Subrata Banerjee","doi":"10.1109/ICICPI.2016.7859689","DOIUrl":"https://doi.org/10.1109/ICICPI.2016.7859689","url":null,"abstract":"Electro Mechanical Miniature Circuit Breaker (MCB) has been introduced under LT Protection Scheme since 1910 for protection against short circuit faults. In the present days a good number of areas have been explored on intelligent circuit breakers like AI incorporated Vacuum Circuit Breaker, SF6, and LV DC Breaker and so on. The aforesaid breakers are single port based and for a single load. In this paper discussion has been drawn about an Intelligent MCB with multiple ports availability. The Fuzzy Inference System based MCB has been developed under MATLAB environment. Hardware developed here is a Microcontroller belongs to ATMEL family. By developing one script file under MATLAB Environment and by introducing Microcontroller a single shot scope has been implemented for validating the outputs obtained from the output pins of the concerned microcontroller. For an alternate way to record outputs with respect to time in continuous form another scope has also been taken into account by introducing Microcontroller as well.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"87 1","pages":"132-136"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84030703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.13140/RG.2.2.32646.47680
Siladitya Khan, A. Paul, Tanmoy Sil, Arnab Basu, Rishikesh Tiwari, Saroni Mukherjee, Ujjwal Mondal, A. Sengupta
Control techniques over the decades have evolved from the various aspects of Model-Based Control (MBC) to Data Driven Control (DDC). In stark contrast to the model based paradigm which is targeted at addressing the fundamental physics driving the system and intends to freely determine the process transfer function. The data-driven approach instead connotes to ascertaining the process parameters of a system, void of a specified architecture by measuring the input and output data. The present investigation targets a validatory execution of a simple feedback DDC architecture on the position control of a DC motor module. The input-output data obtained from an onboard potentiometer is logged to the host PC using a low cost acquisition set-up and the corresponding model structure is identified using Matlab System Identification Toolbox. Based on the identified plant model, an appropriately tuned PID scheme is proposed that can represent the original hardware response with acceptable fidelity. The ability of the proposed control scheme is augmented by the introduction of standard repetitive control strategy in order to reduce Steady-State tracking errors of the system while negotiating periodic inputs. The experimental results demonstrate the effectiveness of the proposed scheme in offering a highly accurate asymptotic tracking ability.
{"title":"Position control of a DC motor system for tracking periodic reference inputs in a data driven paradigm","authors":"Siladitya Khan, A. Paul, Tanmoy Sil, Arnab Basu, Rishikesh Tiwari, Saroni Mukherjee, Ujjwal Mondal, A. Sengupta","doi":"10.13140/RG.2.2.32646.47680","DOIUrl":"https://doi.org/10.13140/RG.2.2.32646.47680","url":null,"abstract":"Control techniques over the decades have evolved from the various aspects of Model-Based Control (MBC) to Data Driven Control (DDC). In stark contrast to the model based paradigm which is targeted at addressing the fundamental physics driving the system and intends to freely determine the process transfer function. The data-driven approach instead connotes to ascertaining the process parameters of a system, void of a specified architecture by measuring the input and output data. The present investigation targets a validatory execution of a simple feedback DDC architecture on the position control of a DC motor module. The input-output data obtained from an onboard potentiometer is logged to the host PC using a low cost acquisition set-up and the corresponding model structure is identified using Matlab System Identification Toolbox. Based on the identified plant model, an appropriately tuned PID scheme is proposed that can represent the original hardware response with acceptable fidelity. The ability of the proposed control scheme is augmented by the introduction of standard repetitive control strategy in order to reduce Steady-State tracking errors of the system while negotiating periodic inputs. The experimental results demonstrate the effectiveness of the proposed scheme in offering a highly accurate asymptotic tracking ability.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"18 1","pages":"17-21"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89022790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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