On an enhanced back propagation neural network control of vehicle semi-active suspension with a magnetorheological damper

Abstract

To improve the ride quality of a vehicle, an enhanced vibration control method is presented for semi-active suspension (SAS) with magnetorheological (MR) damper by combining back propagation neural network (BPNN) and particle swarm optimization (PSO). 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%.

Robust controller design of a semi-active quasi-zero stiffness air suspension based on polynomial chaos expansion

“…The output force of the magnetorheological damper is mainly composed of the Coulomb damping force generated by the front yielding of the magnetic field, the viscous damping force of the rear yielding, the elastic force and the bias force. The commonly used hyperbolic tangent model expressing the mechanics of the magnetorheological damper is Wang (2020)where, c is the post-yield damping factor; k is the stiffness factor; s is the piston displacement; v is the piston velocity; a is the piston acceleration; f y , b 1 , and b 2 denote the hysteresis loop proportionality factor, maximum slope factor, and hysteresis loop half-width factor, respectively; and f 0 is the bias force.…”

Semi-active control of a new quasi-zero stiffness air suspension for commercial vehicles based on H2H∞ state feedback

“…The Bingham model is adopted in this paper to describe the dynamic characteristics of MR dampers. Then, the damping force can be calculated as (Wang 2020)…”

“…Due to the operating characteristics of the damper itself, the MR damper can only generate a force F d , direction of which is contrary to the velocity of the suspension deflection (Wang 2020).…”

“…The Bingham model is adopted in this paper to describe the dynamic characteristics of MR dampers. Then, the damping force can be calculated as (Wang 2020)where l is the effective length of the piston passage; A r is the effective area of the piston passage; h is the gap height between the piston and the damper cylinder; c is constant basic damping coefficient of MR damper; F d is the controlled coulomb force for the MR damper; sgn is the symbolic function; v is the relative movement speed between the piston rod and piston sleeve; τ y is the shear yield stress of the MR damper, which shows a complex nonlinear relationship with the control current of the MR damper; e is a natural constant; β is the correction factor of the fitted curve; i is the control current of the MR damper; and A 1 , A 2 , A 3 , and A 4 are the fitted parameters.…”