文献类型：期刊文献

英文题名：Bioinspired Nonlinear Dynamics-Based Adaptive Neural Network Control for Vehicle Suspension Systems With Uncertain/Unknown Dynamics and Input Delay

作者：Zhang, Menghua Jing, Xingjian Wang, Gang

第一作者：Zhang, Menghua

通信作者：Zhang, MH[1];Jing, XJ[1]

机构：[1]Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong 999077, Peoples R China;[2]Univ Jinan, Sch Elect Engn, Jinan 250022, Peoples R China;[3]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Peoples R China

第一机构：Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong 999077, Peoples R China

通信机构：corresponding author), Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong 999077, Peoples R China.

年份：2021

卷号：68

期号：12

起止页码：12646-12656

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20205209677505);Scopus(收录号：2-s2.0-85097939891);WOS:【SCI-EXPANDED(收录号:WOS:000692884200097)】；

基金：This work was supported in part by the Innovation and Technology Fund (ITF) Project of HK ITC (Ref. ITP/020/19AP), in part by the Strategic Research Fund of the Research Institute of Urban Sustainable Development, HK Polytechnic University (PolyU), in part by the Project of Strategic Importance of HK PolyU, in part by the General Research Fund of HK RGC under Grant 15206717, in part by the Key Research and Development (Special Public-Funded Projects) of Shandong Province under Grant 2019GGX104058, and in part by the National Natural Science Foundation for Young Scientists of China under Grant 61903155.

语种：英文

外文关键词：Suspensions (mechanical systems); Neural networks; Delays; Biological system modeling; Vibrations; Vehicle dynamics; Nonlinear dynamical systems; Active suspension systems; bioinspired dynamics; finite-time convergence; input delay; neural network; uncertain; unknown dynamics

摘要：A unique adaptive neural network control scheme is proposed for active suspension systems by employing bioinspired nonlinear dynamics, so as to address several critical engineering issues including energy efficiency, input delay, and unknown/uncertain dynamics simultaneously. A novel constructive predictor is firstly designed to solve the effect of input delay. Neural networks are then adopted to approximate the uncertain/unknown dynamics, and importantly, a unique finite-time adaptive control is established which can not only online update the input and output weights of the neural networks but also intentionally introduce beneficial nonlinear dynamics to vibration control. The significant difference from most existing controllers lies in that the designed controller can effectively utilize beneficial nonlinear stiffness and damping characteristics of a novel bioinspired reference model and, thus, purposely achieve superior vibration suppression and obvious energy-saving performance simultaneously. Theoretical analysis and experimental results vindicate that the proposed controller can effectively suppress vibration with much more improved control performance and considerably reduced control energy consumption more than 44%. This should be for the first time to reveal both in theory and experiments that a superior suspension performance is achieved simultaneously with an obvious control energy saving, by employing beneficial bioinspired nonlinear dynamics, compared to most traditional control methods.