000			04069ntm a2200337 i 4500
003			MY-KuUP
005			20260525175836.0
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008			260525t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010529 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKMA .R67 2025 r Thesis
100	1		_aRosmazi Bin Rosli @ Pang See Hoon, _eauthor.
245	1	0	_aOptimization of modified bouc-wen model for magnetorheological damper using nature-inspired meta-heuristic algorithm / _cRosmazi Bin Rosli @ Pang See Hoon
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axiii, 135 pages : _billustration ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Mechanical and Automotive Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026
504			_aIncludes bibliographical references
520	3		_aIn semi-active vibration attenuation systems, the Magnetorheological (MR) damper has emerged as one of the most robust and cost-efficient actuators. The damper provides fail-safe operation, ensuring the safety and reliability of the system while delivering a favorable force-to-power input ratio. However, developing an effective controller for the MR damper remains challenging due to the complexity of its mathematical model and the hysteresis behavior of this highly nonlinear device. To address this, researchers have investigated several parametric models that successfully describe the damper's hysteresis behavior using simpler mathematical formulations. Although these parametric models are effective in capturing the MR damper's hysteresis curve, they present challenges in parameter optimization. This study focuses on optimizing a Bouc-Wen-based phenomenological model to accurately replicate the force-velocity hysteresis curve of a real MR damper. Recent studies shown that the meta-heuristic algorithm has gain popularity among researcher in optimization problems. This research focuses on the Cuckoo Search algorithm for its fast convergence rate and low number of parameters needed to utilize. For optimizing the parameters of the Bouc-Wen model, three new modified versions of the Cuckoo Search Algorithm are used, offering improved exploration and exploitation balances during the optimization process. The Dynamic Cuckoo Search and Inverse Dynamic Cuckoo Search algorithms adjust the exploration-exploitation balance based on the iteration number, while the Adaptive Cuckoo Search algorithm varies the balance based on the fitness evaluation update rate. Experiments were conducted to capture the actual dynamic responses of the MR damper, specifically analyzing its force-displacement and force-velocity characteristics across various current inputs. The optimized parameters of the Bouc-Wen model were then evaluated for tracking performance, demonstrating superior capabilities compared to other optimization methods, such as Particle Swarm Optimization, Genetic Algorithm, and Sine-Cosine Algorithm. The results show that the Bouc-Wen model, optimized using the proposed modified Cuckoo Search algorithm, tracks the MR damper's hysteresis curve more accurately than other algorithms. Finally, the phenomenological model optimized by the modified Cuckoo Search algorithm was tested in a two-degree-of-freedom (2-DOF) semi-active seat suspension system. The results indicate that the model successfully designed a controller scheme for the MR damper, achieving a 35% reduction in body acceleration. This research introduces a new modified Cuckoo Search algorithm and successfully integrates it with the phenomenological Bouc-Wen model for MR damper applications.
610	2	0	_aFaculty of Mechanical and Automotive Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104894 _d104900