Journal
VEHICLE SYSTEM DYNAMICS
Volume 56, Issue 6, Pages 923-946Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00423114.2017.1401100
Keywords
Vehicle dynamics control; system uncertainty; adaptive neural network; sliding mode control; extreme driving condition
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Funding
- National Natural Science Foundation of China [U1664263, 51375009]
- Independent Research Program of Tsinghua University [20161080033]
- Natural Science Foundation of Shandong Province [ZR2016EEQ06]
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Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.
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