Passive Suspension Control Research Articles

A Fractional-Order Model Predictive Control Strategy with Takagi–Sugeno Fuzzy Optimization for Vehicle Active Suspension System

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

Modeling, analysis and PID controller implementation on suspension system for quarter vehicle model

The aim of this research is to acquire a mathematical model for the quarter vehicle model's passive and active suspension system and to build an active and passive suspension control for a quarter-vehicle model. The passive design of the suspension scheme is a compromise between vehicle handling capacity and passenger ride comfort. Passive suspension provides one of these two circumstances that conflict. Current car suspension systems use passive components only by using a coefficient of spring and damping with a fixed coefficient and two degrees of freedom. In the design and manufacture of cars, passenger comfort is a very significant parameter. The objective of the active suspension system is to enhance both the comfort of the ride and the handling of the highway by directly regulating the actuators of suspension power. In the active suspension system, the Proportional Integral Differential Controller (PID) method is applied. Various kinds of highway profiles and controllers such as P, PD, PI, and PID controllers evaluate the efficiency of the active suspension system comparing it with passive suspension scheme. This efficiency will be determined by using the MATLAB and SIMULINK to perform computer simulations.

Vibration Control of Semi-Active Suspension System using Modified Skyhook with Advanced Firefly Algorithm

The semi-active suspension (SAS) system is a partial suspension device used in the vehicle system to improve the ride comfort and road handling. Due to the high non-linearity of the road profile disturbances plus uncertainties derived from vehicle dynamics, a conventional Skyhook controller is not deemed enough for the vehicle system to improve the performance. A major problem of the implementation of the controller is to optimize a proper parameter as this is an important element in demanding a good controller response. An advanced Firefly Algorithm (AFA) integrated with the modified skyhook (MSky) is proposed to enhance the robustness of the system and thus able to improve the vehicle ride comfort. In this paper, the controller scheme to be known as MSky-AFA was validated via MATLAB simulation environment. A different optimizer based on the original firefly algorithm (FA) is also studied in order to compute the parameter of the MSky controller. This control scheme to be known as MSky-FA was evaluated and compared to the proposed MSky-AFA as well as the passive suspension control. The results clearly exhibit more superior and better response of the MSky-AFA in reducing the body acceleration and displacement amplitude in comparison to the MSky-FA and passive counterparts for a sinusoidal road profile condition.