Commercial Vehicle-Based Robust Control of Seated Whole-Body Vibration Using Adaptive Indirect Type-2 Fuzzy Neural Network

Abstract

Drivers of commercial vehicles are invariably subject to chronic diseases such as back pain as a result of exposure to severe cabin excitations. In this paper, a six degrees of freedom (6-DOF) coupled human-body and seat suspension system is modeled in order to reduce the vibrations transmitted to the head, seat, and the relative seat and cabin floor displacement. The contributions of the present paper are: 1) two significant but inherently conflicting control objectives are employed, namely the seat acceleration and the relative displacement between seat and cabin floor to account for the effect of seat endstops, 2) A novel learning rate gradient descent based neural network approximator algorithm coupled to an adaptive indirect type-2 fuzzy neural network (T2FNN) controller to converge the controller to the ideal parameters of the uncertain model. 3) The controller model takes into account the seat suspension nonlinearities due to the nonlinear asymmetric piecewise damper and the cubic hardening of the suspension spring. The proposed controller employs the principle of type-2 fuzzy systems with interval membership function and unknown specifications. The effectiveness of the closed-loop system is validated regarding the uncertainties compared to observer-based sliding mode controller (SMC) and a high-fidelity virtual lab MSC.ADAMS-Simulink platform to validate the results in practical scenarios.

Keywords

• Human biodynamic model
• adaptive control
• seat suspension
• random vibration