{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:39:38Z","timestamp":1783445978073,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,7,6]],"date-time":"2017-07-06T00:00:00Z","timestamp":1499299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In order to improve the response performance of a proportion integration differentiation (PID) controller for magnetorheological fluids (MRF) brake and to reduce the braking fluctuation rate, an improved fruit fly optimization algorithm for PID controller parameters tuning of MRF brake is proposed. A data acquisition system for MRF brake is designed and the transfer function of MRF brake is identified. Moreover, an improved fruit fly optimization algorithm (IFOA) through integration of PID control strategy and cloud model algorithm is proposed to design a PID controller for MRF brake. Finally, the simulation and experiment are carried out. The results show that IFOA, with a faster response output and no overshoot, is superior to the conventional PID and fruit fly optimization algorithm (FOA) PID controller.<\/jats:p>","DOI":"10.3390\/sym9070109","type":"journal-article","created":{"date-parts":[[2017,7,6]],"date-time":"2017-07-06T10:55:45Z","timestamp":1499338545000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-6532","authenticated-orcid":false,"given":"Xinhua","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China"},{"name":"Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China"},{"name":"The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2857","DOI":"10.1142\/S0217979296001306","article-title":"Commercial magnetorheological fluid devices","volume":"10","author":"Carlson","year":"1996","journal-title":"Int. J. Mod. Phys. B"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1177\/1045389X06054997","article-title":"Cable vibration control using magnetorheological dampers","volume":"17","author":"Duan","year":"2006","journal-title":"Intell. Mater. Syst. Struct."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s12591-012-0128-4","article-title":"On the chaotic cuppression of both ideal and non-ideal duffing based vibrating systems, using a magnetorheological damper","volume":"21","author":"Tusset","year":"2013","journal-title":"Differ. Equ. Dyn. Syst."},{"key":"ref_4","first-page":"226","article-title":"Effect of silicone oil viscosity on the properties of magnetorheological fluids","volume":"9","author":"Liu","year":"2015","journal-title":"Optoelectron. Adv. Mater. Commun."},{"key":"ref_5","unstructured":"Visioli, A. (2006). Practical PID Control, Springer Science & Business Media."},{"key":"ref_6","unstructured":"Liu, J.K. (2011). Advanced PID Control based on MATLAB, Publishing House of Electronics Industry."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TCST.2005.847331","article-title":"PID control system analysis, design, and technology","volume":"13","author":"Ang","year":"2005","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_8","unstructured":"Pan, W.T. (,  2011). A new evolutionary computation approach: Fruit fly optimization algorithm. Proceedings of the Conference on Digital Technology and Innovation Management, Taipei, Taiwan."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.knosys.2012.08.015","article-title":"A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm","volume":"37","author":"Li","year":"2013","journal-title":"Knowl. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s00521-011-0769-1","article-title":"Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network","volume":"22","author":"Lin","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.chemolab.2014.12.007","article-title":"Melt index prediction by least squares support vector machines with an adaptive mutation fruit fly optimization algorithm","volume":"141","author":"Wang","year":"2014","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4310","DOI":"10.1016\/j.eswa.2015.01.048","article-title":"An improved fruit fly optimization algorithm and its application to joint replenishment problems","volume":"42","author":"Wang","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.knosys.2014.02.021","article-title":"An improved fruit fly optimization algorithm for continuous function optimization problems","volume":"62","author":"Pan","year":"2014","journal-title":"Knowl. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1016\/j.automatica.2009.03.018","article-title":"Synthesis of PID controllers for a class of time delay systems","volume":"45","author":"Oliveira","year":"2009","journal-title":"Automatica"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0959-1524(03)00039-8","article-title":"Applying neural networks to on-line updated PID controllers for nonlinear process control","volume":"14","author":"Chen","year":"2004","journal-title":"J. Process Control"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pelusi, D. (2011, January 14\u201317). On designing optimal control systems through genetic and neuro-fuzzy techniques. Proceedings of the IEEE International Symposium on Signal Processing and Information Technology(ISSPIT), Bilbao, Spain.","DOI":"10.1109\/ISSPIT.2011.6151547"},{"key":"ref_17","first-page":"65","article-title":"PID and intelligent controllers for optimal timing performances of industrial actuators","volume":"13","author":"Pelusi","year":"2012","journal-title":"Int. J. Simul. Syst. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pelusi, D. (2012, January 28\u201330). Improving settling and rise times of controllers via intelligent algorithms. Proceedings of the IEEE International Conference on Computer Modelling and Simulation (Uksim), Cambridge, UK.","DOI":"10.1109\/UKSim.2012.34"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.4028\/www.scientific.net\/AMM.233.66","article-title":"Study on variable universe fuzzy PID control for landing gear based on MRF (magneto-rheological fluid) damper","volume":"233","author":"Zhu","year":"2012","journal-title":"Appl. Mech. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s12541-016-0020-1","article-title":"Tension control of wire rope in winch spooler using magneto rheological brake","volume":"17","author":"Lee","year":"2016","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2654","DOI":"10.15282\/ijame.11.2015.42.0223","article-title":"Performance evaluation of shaft speed control using a magnetorheological brake","volume":"11","author":"Abdullah","year":"2015","journal-title":"Int. J. Automot. Mech. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Han, J., Wang, P., and Yang, X. (2012, January 5\u20138). Tuning of PID controller based on fruit fly optimization algorithm. Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), Chengdu, China.","DOI":"10.1109\/ICMA.2012.6282878"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11071-013-0814-y","article-title":"Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle","volume":"73","author":"Sheng","year":"2013","journal-title":"Nonlinear Dyn."},{"key":"ref_24","first-page":"69","article-title":"A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller","volume":"7","author":"Li","year":"2012","journal-title":"J. Converg. Inf. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1155\/2013\/108768","article-title":"LGMS-FOA: An improved fruit fly optimization algorithm for solving optimization problems","volume":"2013","author":"Shan","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Perna, C., and Sibillo, M. (2012). Optimal trading rules at hourly frequency in the foreign exchangemarkets. Mathematical and Statistical Methods for Actuarial Sciences and Finance, Springer.","DOI":"10.1007\/978-88-470-2342-0"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pelusi, D., Mascella, R., and Tallini, L. (2017). Revised gravitationalsearch algorithms based on evolutionary-fuzzy systems. Algorithm, 10.","DOI":"10.3390\/a10020044"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/7\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:41:47Z","timestamp":1760208107000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/7\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,6]]},"references-count":27,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2017,7]]}},"alternative-id":["sym9070109"],"URL":"https:\/\/doi.org\/10.3390\/sym9070109","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,6]]}}}