Body attitude control strategy based on road level for heavy rescue vehicles

Abstract

This paper proposes an attitude control strategy based on road level for heavy rescue vehicles. The strategy aims to address the problem of poor ride comfort and stability of heavy rescue vehicles in complex road conditions. Firstly, with the pressure of the suspension hydraulic cylinder chamber without a piston rod as the parameter, Takagi–Sugeno fuzzy controller classification and adaptive network-based fuzzy inference system controller classification are used to recognise the road level. Secondly, particle swarm optimisation is adopted to obtain the optimal parameters of the active suspension system of vehicle body attitude control under different road levels. Lastly, the parameters of the active suspension system are selected in accordance with the road level recognised in the driving process to improve the adaptive adjustment capability of the active suspension system at different road levels. Test results show that the root mean square values of vertical acceleration, pitch angle and roll angle of the vehicle body are reduced by 59.9%, 76.2% and 68.4%, respectively. This reduction improves the ride comfort and stability of heavy rescue vehicles in complex road conditions.

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

“…Xu et al [104] optimized AVB 3 (k) to 9.6% through fuzzy logic control; Anandan et al used a PID control strategy, and the optimization of AVB 3 (k) was 33% [105]. The maximum AVB 3 (k) optimization was 76.2%, which appeared in Chen et al's study [96].…”

“…Wei et al [94] used fuzzy PID control, and the optimization of AVB 3 (k) was 3.06%. Chen et al [96] used the T-S fuzzy control strategy, achieving a 76.2% optimization of AVB 3 (k), while Ahmad et al [103] achieved a 16% optimization of AVB 3 (k). Xu et al [104] optimized AVB 3 (k) to 9.6% through fuzzy logic control; Anandan et al used a PID control strategy, and the optimization of AVB 3 (k) was 33% [105].…”

“…Wei et al [94] applied fuzzy PID control on vehicles and achieved an AVB 4 (k) optimization of 3.54%. Chen et al [96] achieved a 68.4% optimization of AVB 4 (k), and Nan et al [107] achieved a 35% optimization of AVB 4 (k) using fuzzy logic control. The maximum AVB 4 (k) optimization was 89.41%, which appeared in the study by Gomonwattanapanich et al [83].…”

Analysis of Suspension Control Strategy of Wheel-track Composite Amphibious Vehicle Based on AMESim-Simulink

“…Some researchers have also done a corresponding analysis on the suspension control of multi-axle heavy vehicles [8][9]. By changing the suspension mechanical structure, body attitude control, etc., vehicle handling and stability are improved [10][11]. Most of the existing research is on wheeled vehicles, and the goal of suspension control is to improve the ride comfort and safety of the vehicle.…”

Integrated Control of Spray System and Active Suspension Systems Based on Model-Assisted Active Disturbance Rejection Control Algorithm

“…Tchamna [17] studied a variable-stiffness semiactive suspension system to control vehicle attitude under cornering conditions. Chen [18] proposed an attitude control strategy for heavy emergency rescue vehicles based on the road level. For different road grades, the control parameters of the active suspension system are changed to reduce the root-mean-square values of the vertical acceleration, pitch angle, and roll angle of the vehicle body.…”