Vibration Control of MR-Damped Vehicle Suspension System Using PID Controller Tuned by Particle Swarm Optimization

Abstract

Proportional integral derivative (PID) control technique is the most common control algorithm applied in various engineering applications. Also, particle swarm optimization (PSO) is extensively applied in various optimization problems. This paper introduces an investigation into the use of a PSO algorithm to tune the PID controller for a semi-active vehicle suspension system incorporating magnetorheological (MR) damper to improve the ride comfort and vehicle stability. The proposed suspension system consists of a system controller that determine the desired damping force using a PID controller tuned using PSO, and a continuous state damper controller that estimate the command voltage that is required to track the desired damping force. The PSO technique is applied to solve the nonlinear optimization problem to find the PID controller gains by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm. A mathematical model of a two degree-of-freedom MR-damped vehicle suspension system is derived and simulated using Matlab/Simulink software. The proposed PSO PID controlled suspension is compared to both the conventional PID controller and the passive suspension systems. System performance criteria are evaluated in both time and frequency domains, in order to quantify the success of the proposed suspension system. The simulated results reflect that the proposed PSO PID controller of the MR-damped vehicle suspension offers a significant improvement in ride comfort and vehicle stability.

GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control

“…One of the main objectives was to minimize body acceleration to improve ride comfort. Metered et al (2015) had implemented particle swarm optimization (PSO) algorithm to tune the PID controller implemented on a semi-active quarter car suspension system. A 2DoF model with MR damper is simulated in a Matlab/Simulink environment with bump and random road inputs.…”

“…It is observed that optimization of PID controller applied to suspension system is carried out in single objective nature (Metered et al 2015;Niu 2014) whereas (Kesarkar and Selvaganesan 2015) presented a multi-modal optimization approach and (Gad et al 2017) presented dual objective function optimization. Kalaivani et al (2014) studied a fuzzy logic-based active quarter car suspension system.…”

Experimental analysis of vibration control algorithms applied for an off-road vehicle with magnetorheological dampers

“…In the case of vibration control of vehicle suspension systems a set value, which is fed to the controller, is commonly equal to zero since the goal of the algorithm is to minimize vibration. Thus, the PID algorithm can be simplified to the sum of three feedback loops dedicated to the displacement, velocity and acceleration as was presented in Talib and Darus 34 and Metered et al 23 Furthermore, it is worth noticing that a classical idea of the gain scheduling, which is dependent on the set value does not fit in this case. However, other approaches to the gain scheduling dedicated to the semi-active dampers were presented in the literature, where the scheduling is dependent on the dominant frequency of the sinusoidal road-induced excitation 35 or on a general class of the road excitation.…”

“…Various optimization algorithms can be used for such a task, e.g. iterative learning algorithm applied in Talib and Darus 34 or particle swarm optimization algorithm used in Metered et al 23 However, the proposed methods require performing consecutive experiments and evaluating values of the quality index for different combinations of control parameters, which is very time-consuming. Thus, in this study the solution space was directly analyzed while consecutive candidates and direction of the search were selected manually.…”

“…Furthermore, the displacement or position signal can be also used in feedback loop, which was shown by Cabrera-Amado and Silva-Navarro 22 as applied in a positioning application dedicated to MR dampers. Studies presented by Metered et al 23 are the example of application of a proportional-integral-derivative (PID) control algorithm, where all three feedback signals (displacement, velocity, and acceleration) are used for MR-damped vehicle suspension system. The PID algorithm was optimized using a particle swarm optimization procedure.…”