The suspension system has the role of regulating and extinguishing oscillations in the vehicle. To improve stability and comfort, the active suspension system is proposed to replace the passive suspension system. There are many algorithms used for active suspension system control, such as PID, LQR, Fuzzy, etc. Among them, the nonlinear control method which uses the SMC algorithm gives a stable performance. This research proposes the use of the SMC algorithm to control the operation of the active suspension system equipped with a quarter dynamics model. The process of linearization of the hydraulic actuator is presented in the paper. As a result of the simulation, the values of displacement and acceleration of the sprung mass were significantly reduced when the vehicle used the active suspension system controlled by the SMC algorithm. The SMC controller established in this paper provides stability in many situations. Therefore, the vehicle's smoothness and comfort have been significantly improved. In the future, intelligent algorithms can be combined with SMC algorithms to improve the efficiency of the controller.

A Novel Sliding Mode Control Algorithm for an Active Suspension System Considering with the Hydraulic Actuator

Optimal design and dynamic performance analysis of a fractional-order electrical network-based vehicle mechatronic ISD suspension

Mostly, magnetorheological (MR) dampers were optimized based on individual performance, without considering the influence of structure parameters change on vehicle performance. Therefore, a multi-o...

Design and multi-objective optimization of magnetorheological damper considering vehicle riding comfort and operation stability:

For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems.

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Control system research in wave compensation based on particle swarm optimization

This article introduces the application of the Cuckoo Search (CS) Algorithm to tune Proportional-Integral-Derivative (PID) and Skyhook controller for the semi-active (SA) suspension system further to improve the vehicle’s ride comfort and stability. Meanwhile, the PID-CSA and Skyhook-CSA intelligent approaches have been compared to the passive suspension system. The performances of the PID controller and Skyhook controller are optimised by Cuckoo Search (CS) Algorithm, respectively. The system’s mean square error (MSE) is defined as an objective function for optimising the proposed controllers. The performance of the proposed PID-CSA and Skyhook-CSA controllers are evaluated with the passive suspension system in the form of body acceleration, body displacement, and tire acceleration. The sinusoidal road profile is set as the disturbance of this system. The percentage improvement for body acceleration and body displacement achieved about 25% for the PID-CSA controller and 1-4% for Skyhook-CSA. These simulated results reflect that the proposed controllers outperformed in comparison with other considered methods to obtain the most effective vehicle stability and ride comfort.

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Intelligent Cuckoo Search Algorithm of PID and Skyhook Controller for Semi-Active Suspension System using Magneto-Rheological Damper

Magnetorheological (MR) damper control for semi-active system is one of the areas of interest investigated to improve the ride comfort and stability of vehicle performance Many types of controllers used to control the semi-active MR damper have recently been investigated by previous researchers It is found that the improper design of control scheme has led to an unpredictable optimum target force Therefore, this study aims to investigate an intelligent optimizer called advanced firefly algorithm (AFA) to compute the proportional-integral-derivative (PID) controller for semi-active suspension system The performance of the PID controller with the AFA tuning was investigated and compared to the original FA technique and other conventional and intelligent optimizers as well as non-PID controller namely as heuristic method, particle swarm optimization (PSO) and Skyhook controller A MATLAB Simulation environment was used to generate the simulation model of semi-active suspension system complete will all control elements The study of the controllers has shown a significant improvement as the proposed PID-AFA is capable of reducing the amplitude of the sprung acceleration and body acceleration responses up to 565% and 671%, respectively compared to PID-HEURISTIC, PID-FA, PID-PSO, Skyhook and passive systems

Mohd Ibthisham Ardani

Papers

Vibration control of semi-active suspension system using PID controller with advanced firefly algorithm and particle swarm optimization