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Based on the test data of MR damper, a non-parametric model of MR damper using adaptive neuro-fuzzy inference system (ANFIS) is first established, and based on that, a dynamics model of the SAS system is derived. Next, a BPNN controller is designed to fulfill the effective control of the current in MR damper. Meanwhile, the improved PSO with adaptive weight and dynamic acceleration constant is introduced to optimize the weights and thresholds of the BPNN controller, which can avoid the designed BPNN falling into the local optimum and then improve the convergence rate of the designed controller. Besides, the stability of the developed controller is analyzed via Lyapunov stability theory. Different from the existing models and methods, the established model can well describe the dynamics behaviors of the actual MR damper, and the proposed control method has better adaptability, convergence speed and precision. Finally, a simulative investigation is performed to validate the effectiveness and feasibility of the proposed controller, compared to existing BP-PID control and the passive suspension, the vehicle acceleration of SAS with this proposed controller is respectively improved by 10% and 30%.<\/jats:p>","DOI":"10.1177\/01423312221118224","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T05:30:54Z","timestamp":1662615054000},"page":"512-523","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["On an enhanced back propagation neural network control of vehicle semi-active suspension with a magnetorheological damper"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3715-3118","authenticated-orcid":false,"given":"Mingxiang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7550-8376","authenticated-orcid":false,"given":"Hui","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, China"}]},{"given":"Jibo","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, China"}]},{"given":"Minhao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, China"}]}],"member":"179","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"e_1_3_2_2_1","article-title":"An improved method for stochastic nonlinear system\u2019s identification using fuzzy-type output-error autoregressive Hammerstein\u2013Wiener model based on gradient algorithm, multi-innovation, and data filtering techniques","volume":"8525090","author":"Abid DBH","year":"2021","unstructured":"Abid DBH, Abouda SE, Elloumi M, et al. 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