{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:57:19Z","timestamp":1769709439222,"version":"3.49.0"},"reference-count":39,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>This research work focus on level control in quadruple tank systems based on proposed Deep Neural Fuzzy based Fractional Order Proportional Integral Derivative (DN-FFOPID) controller system. This is used for controlling the liquid level in these non- linear cylindrical systems. These model helps in identifying the dynamics of the tank system which gives the control signal feed forwarded from the reference liquid level. But, it fails to minimize the error and the system is also subjected to external disturbances. Hence, to minimize this drawback a novel controller must be introduced in it. The proposed Deep Neural model is a six layered network which are optimized with the back-propagation algorithm. It effectively trains the system thus reducing the steady state error, offset model errors and unmeasured disturbances. This neural intelligent system maintains the liquid level which fulfils the required design criteria like time constant, no overshoot, less rise time and less settling time, which can be applied to various fields. MATLAB\/simulink at FOMCON toolbox is used to perform the simulation. Real time liquid control experimental results and simulation results are demonstrated which proves the effectiveness and feasibility of the proposed methods for the quadruple tank system which finds applications in effluent treatment, petrochemical, pharmaceutical and aerospace fields.<\/jats:p>","DOI":"10.3233\/jifs-221674","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T11:48:50Z","timestamp":1680608930000},"page":"1847-1861","source":"Crossref","is-referenced-by-count":0,"title":["Deep neural fuzzy based fractional order PID controller for level control applications in quadruple tank system"],"prefix":"10.1177","volume":"45","author":[{"given":"T.","family":"Agitha","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Vivekanandha College of Engineering for Women (Autonomous), Truchengode, Tamilnadu, India"}]},{"given":"T.S.","family":"Sivarani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Arunachala College of Engineering for Women, Vellichanthai, Tamilnadu, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-221674_ref3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.future.2017.10.047","article-title":"Workload prediction in cloud using artificial neural network and adaptive differential evolution","volume":"81","author":"Kumar","year":"2018","journal-title":"Futur. Gener. Comput. Syst"},{"issue":"2","key":"10.3233\/JIFS-221674_ref4","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.conengprac.2009.09.003","article-title":"Design of discrete time adaptive PID control systems with parallel feedforward compensator","volume":"18","author":"Mizumoto","year":"2010","journal-title":"Control Eng.Pract"},{"issue":"1","key":"10.3233\/JIFS-221674_ref5","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/TCST.2013.2250504","article-title":"Adaptive backstepping-based composite nonlinear feedback water level control for the nuclear U-tube steam generator","volume":"22","author":"Wei","year":"2013","journal-title":"IEEE Trans. Control Syst. Technol"},{"issue":"3","key":"10.3233\/JIFS-221674_ref6","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/87.845876","article-title":"The quadruple-tank process: a multivariable laboratory process with an adjustable zero","volume":"8","author":"Johansson","year":"2000","journal-title":"IEEE Trans. Control Syst. Technol"},{"key":"10.3233\/JIFS-221674_ref7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jprocont.2019.01.006","article-title":"Design of sliding mode control for quadruple-tankMIMOprocess with time delay compensation","volume":"76","author":"Shah","year":"2019","journal-title":"Journal of Process Control"},{"key":"10.3233\/JIFS-221674_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jprocont.2021.05.009","article-title":"Parameter estimation based robust liquid level control of quadruple tank system\u2014Second order sliding mode approach","volume":"104","author":"Gurjar Bhagyashri","year":"2021","journal-title":"Journal of Process Control"},{"issue":"1","key":"10.3233\/JIFS-221674_ref9","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ifacol.2020.06.002","article-title":"Robust liquid level control of quadruple tank system-second order sliding mode approach","volume":"53","author":"Chaudhari Vinita","year":"2020","journal-title":"IFAC-PapersOnLine"},{"issue":"2022","key":"10.3233\/JIFS-221674_ref10","first-page":"146","article-title":"Disturbance observer-based feedback linearization control for a quadruple-tank liquid level system","volume":"122","author":"Meng Xiangxiang","journal-title":"ISA Transactions"},{"issue":"9","key":"10.3233\/JIFS-221674_ref11","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.1007\/s12555-019-0504-8","article-title":"Level control of quadruple tank system based on adaptive inverse evolutionary neural controller","volume":"18","author":"Son Nguyen Ngoc","year":"2020","journal-title":"International Journal of Control, Automation and Systems"},{"key":"10.3233\/JIFS-221674_ref12","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.ins.2014.11.050","article-title":"An ant colony optimization-based fuzzy predictive control approach for nonlinear processes","volume":"299","author":"Bououden","year":"2015","journal-title":"Inf. Sci. (Ny)"},{"issue":"2","key":"10.3233\/JIFS-221674_ref13","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s00521-017-3068-7","article-title":"Adaptive uncertainty compensation-based nonlinear model predictive control with real-time applications","volume":"31","author":"\u00c7etin","year":"2019","journal-title":"Neural Comput. Appl"},{"issue":"5","key":"10.3233\/JIFS-221674_ref14","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1007\/s12555-017-0614-0","article-title":"Fuzzy iterative learning control-based design of fault tolerant guaranteed cost controller for nonlinear batch processes","volume":"16","author":"Yu","year":"2018","journal-title":"Int. J. Control. Autom. Syst"},{"key":"10.3233\/JIFS-221674_ref15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.measurement.2018.12.050","article-title":"A new constrained PSO for fuzzy predictive control of Quadruple-Tank process","volume":"136","author":"Thamallah","year":"2019","journal-title":"Measurement"},{"issue":"3","key":"10.3233\/JIFS-221674_ref16","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.jfranklin.2013.11.018","article-title":"Adaptive iterative learning control for nonlinear pure-feedback systems with initial state error based on fuzzy approximation","volume":"351","author":"Zhang","year":"2014","journal-title":"J. Franklin Inst"},{"key":"10.3233\/JIFS-221674_ref17","doi-asserted-by":"publisher","DOI":"10.1177\/0142331219834998"},{"issue":"4","key":"10.3233\/JIFS-221674_ref18","first-page":"305","article-title":"Feedback error based discontinuous and continuous variable learning rate CMAC","volume":"3","author":"Rawat","year":"2014","journal-title":"Int. J. Electron. Electr. Eng"},{"issue":"4","key":"10.3233\/JIFS-221674_ref19","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/j.enconman.2008.12.028","article-title":"Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system","volume":"50","author":"Sabahi","year":"2009","journal-title":"Energy Convers. Manag"},{"key":"10.3233\/JIFS-221674_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2948414"},{"key":"10.3233\/JIFS-221674_ref21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03772063.2021.1911694","article-title":"Automatic skin tumor detection using online tiger claw region based segmentation\u2013a novel comparative technique","volume":"69","author":"Ashwini","year":"2021","journal-title":"IETE Journal of Research"},{"key":"10.3233\/JIFS-221674_ref22","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.energy.2017.10.121","article-title":"Intelligent parameter optimization of Savonius rotor using artificial neural network and genetic algorithm","volume":"143","author":"Mohammadi","year":"2018","journal-title":"Energy"},{"key":"10.3233\/JIFS-221674_ref23","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.asoc.2018.09.013","article-title":"Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration","volume":"73","author":"ElSaid","year":"2018","journal-title":"Appl. Soft Comput"},{"issue":"1","key":"10.3233\/JIFS-221674_ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00500-016-2442-1","article-title":"Optimizing connection weights in neural networks using the whale optimization algorithm","volume":"22","author":"Aljarah","year":"2018","journal-title":"Soft Comput"},{"key":"10.3233\/JIFS-221674_ref25","doi-asserted-by":"crossref","first-page":"20281","DOI":"10.1109\/ACCESS.2019.2897580","article-title":"An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem","volume":"7","author":"Deng","year":"2019","journal-title":"IEEE Access"},{"issue":"4","key":"10.3233\/JIFS-221674_ref26","doi-asserted-by":"crossref","first-page":"728","DOI":"10.3390\/en11040728","article-title":"Hybrid GA-PSO optimization of artificial neural network for forecasting electricity demand","volume":"11","author":"Anand","year":"2018","journal-title":"Energies"},{"issue":"1","key":"10.3233\/JIFS-221674_ref27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1002\/asjc.1529","article-title":"A neural differential evolution identification approach to nonlinearsystems and modelling of shape memory alloy actuator","volume":"20","author":"Nguyen","year":"2018","journal-title":"Asian J. Control"},{"issue":"2-3","key":"10.3233\/JIFS-221674_ref28","doi-asserted-by":"crossref","first-page":"22242","DOI":"10.3233\/FI-2019-1764","article-title":"Adaptive differential evolution with elite opposition-based learning and its application to training artificial neural networks","volume":"164","author":"Choi","year":"2019","journal-title":"Fundam. Informaticae"},{"issue":"6","key":"10.3233\/JIFS-221674_ref29","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/j.asr.2018.01.011","article-title":"Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm","volume":"61","author":"Wang","year":"2018","journal-title":"Adv. Sp. Res"},{"key":"10.3233\/JIFS-221674_ref30","doi-asserted-by":"publisher","DOI":"10.1177\/1729881416677695"},{"key":"10.3233\/JIFS-221674_ref31","first-page":"1","article-title":"Automatic skin tumour segmentation using prioritized patch based region\u2013a novel comparative technique","volume":"66","author":"Ashwini","year":"2020","journal-title":"IETE Journal of Research"},{"key":"10.3233\/JIFS-221674_ref32","doi-asserted-by":"crossref","first-page":"102948","DOI":"10.1016\/j.micpro.2019.102948","article-title":"Novel fuzzy fractional order PID controller for non linear interacting coupled spherical tank system for level process","volume":"72","author":"Jegatheesh","year":"2020","journal-title":"Microprocessors and Microsystems"},{"issue":"13","key":"10.3233\/JIFS-221674_ref33","doi-asserted-by":"crossref","first-page":"10161","DOI":"10.1007\/s00500-019-04532-z","article-title":"Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor","volume":"24","author":"Gobinath","year":"2020","journal-title":"Soft Computing"},{"key":"10.3233\/JIFS-221674_ref34","doi-asserted-by":"crossref","unstructured":"Barik Amar Kumar , Debasis Tripathy , Dulal Chandra Das and Subash Ch Sahoo , Optimal load-frequency regulation of demand response supported isolated hybrid microgrid using fuzzy PD+I controller. In International Conference on Innovation in Modern Science and Technology, Springer, Cham, pp. 798\u2013806. 2019.","DOI":"10.1007\/978-3-030-42363-6_93"},{"key":"10.3233\/JIFS-221674_ref35","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.neucom.2019.04.087","article-title":"Deep convolutional neural network based fractional-order terminal sliding-mode control for robotic manipulators","volume":"416","author":"Zhou Minghao","year":"2020","journal-title":"Neurocomputing"},{"issue":"5","key":"10.3233\/JIFS-221674_ref36","first-page":"7457","article-title":"Frequency stabilization in deregulated energy system using coordinated operation of fuzzy controller and redox flow battery","volume":"45","author":"Mandeep","year":"2000","journal-title":"International Journal of Energy Research"},{"issue":"3","key":"10.3233\/JIFS-221674_ref37","first-page":"504","article-title":"Cascade-I\u03bbD\u03bcN controller design for AGC of thermal and hydro-thermal power systems integrated with renewable energy sources","volume":"15","author":"Yogendra Arya","year":"2000","journal-title":"Wiley"},{"issue":"3","key":"10.3233\/JIFS-221674_ref38","first-page":"504","article-title":"Cascade-I\u03bbD\u03bcN controller design for AGC of thermal and hydro-thermal power systems integrated with renewable energy sources","volume":"15","author":"Yogendra Arya","year":"2000","journal-title":"Wiley"},{"issue":"18","key":"10.3233\/JIFS-221674_ref39","doi-asserted-by":"crossref","first-page":"3886","DOI":"10.1049\/iet-gtd.2019.0935","article-title":"Integrating layered recurrent ANN with robust control strategy for diverse operating conditions of AGC of the power system","volume":"14","author":"Sharma","year":"2000","journal-title":"IET Generation, Transmission & Distribution"},{"issue":"10","key":"10.3233\/JIFS-221674_ref40","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/j.neunet.2004.05.003","article-title":"Feedback error learning and nonlinear adaptive control","volume":"17","author":"Nakanishi","year":"2004","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-221674_ref41","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.asoc.2014.02.022","article-title":"Application of type-2 fuzzy logic system for load frequency control using feedback error learning approaches","volume":"21","author":"Sabahi","year":"2014","journal-title":"Appl. Soft Comput"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-221674","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T05:54:35Z","timestamp":1769666075000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-221674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,2]]},"references-count":39,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-221674","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,2]]}}}