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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.

Nonparametric models do not require any assumptions on the underlying input/output relationship of the system being modeled so that they are highly useful for studying and modeling the nonlinear behaviour of Magnetorheological (MR) fluid dampers. However, the application of these models in semi-active suspension is very rare and most theoretical works available on this topic address the application of parametric models (e.g. Modified Bouc-Wen model). In this paper, a nonparametric MR damper model based on the Restoring Force Surface technique is applied in vehicle semi-active suspension system. It consists of a three dimensional interpolation using Chebyshev orthogonal polynomial functions to simulate the MR damper force as a function of the displacement, velocity and input voltage. Also, a damper controller based on a Signum function method is proposed, for the first time, for use in conjunction with the system controller of a semi-active vehicle suspension.

Proportional integral derivative (PID) control method is an effective, easy in implementation and famous control technique applied in several engineering systems. Also, Genetic Algorithm (GA) is a suitable approach for optimum searching problems in science, industrial and engineering applications. This paper presents the usage of GA for determining the optimal PID controller gains and their implementation in the active quarter-vehicle suspension system to achieve good ride comfort and vehicle stability levels. The GA is applied to solve a combined multi-objective (CMO) problem to tune PID controller gains of vehicle active suspension system for the first time. The active vehicle suspension system is modeled mathematically as a two degree-of-freedom mechanical system and simulated using Matlab/Simulink software.

Active vibration control is the most effective method used for suppressing vibrations from external sources. This paper presents the particle swarm optimization (PSO) algorithm to search about the optimum feedback controller gains for the active mount suspension, for the first time, to reduce the vibrations level of a structural system. It consists of vibrating mass and flexible beam subjected to an external disturbance. A mathematical model and the equations of motion of the structure system with an active mount suspension are simulated using Matlab/Simulink software. The active controller was designed to control the first three modes of the structure. The proposed PSO algorithm aims to minimize the acceleration of the suspended mass as the objective function with constraint of the actuator force. Vibrations level is examined theoretically in order to assess the effectiveness of the proposed controller.

This paper introduces an optimum design for a feedback controller of a fully active vehicle suspension system using the combined multi-objective particle swarm optimization (CMOPSO) in order to minimize the actuator power consumption while enhancing the ride comfort. The proposed CMOPSO algorithm aims to minimize both the vertical body acceleration and the actuator power consumption by searching about the optimum feedback controller gains. A mathematical model and the equations of motion of the quarter-car active suspension system are considered and simulated using Matlab/Simulink software. The proposed active suspension is compared with both active suspension system controlled using the linear quadratic regulator (LQR) and the passive suspension systems. Suspension performance is evaluated in time and frequency domains to verify the success of the proposed control technique.