This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. The newly proposed algorithm was tested using a set of standard benchmark functions with different searching space and global optima placement. An engineering optimization application was chosen to evaluate the performance of the algorithm in complex engineering applications. The optimization task involved hysteresis parameter identification of the root mean square error between the model and an actual magnetorheological damper. The magnetorheological damper response was chosen as the objective function. The final value of the fitness function and the iteration number it took to converge were used as the qualifying indicator to the proposed cuckoo search algorithm efficiency. A comparison was done against particle swarm optimization, genetic algorithm, and sine–cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.
The paper focuses on the practical implementation of a novel control method to an automotive suspension system using active force control (AFC) with iterative learning algorithm (ILA) and proportional-integral-derivative (PID) control strategy. The overall control system to be known as AFC-IL scheme essentially comprises three feedback control loops to cater for a number of specific tasks, namely, the innermost loop for the force tracking of the pneumatic actuator using PI controller, intermediate loops applying AFC with ILA strategy for the compensation of the disturbances and the outermost loop using PID controller for the computation of the desired force. A number of experiments were carried out on a physical test rig with hardware-in-the-loop simulation (HILS) feature that fully incorporates the theoretical elements. The performance of the proposed control method was evaluated and benchmarked to examine the effectiveness of the system in suppressing the vibration effect of the suspension system. It was found that the experimental results demonstrate the superiority of the active suspension system with proposed AFC-IL scheme compared to the PID and passive counterparts.
This paper presents a modified intelligent active force control (AFC) control strategy in a semi active seat suspension system. The main actuator studied in this research is the Magneto-rheological (MR) damper. Since a semi-active device like MR damper can only dissipate energy so a modified version of AFC controller is needed. The modified AFC controller main function is to determine the appropriate control force. A Heaviside Step Function (HSF) is used to ensure the MR damper produce the desired damping force according to the control force generated by AFC controller. The phenomenological Bouc-Wen is used to study the effectiveness of the new AFC controller taking into account the dynamic response of the damper. Sinusoidal signals simulated as vibration sources are applied to the seat suspension system to investigate the improvement of ride comfort as well as to ascertain the new AFC controller robustness. Comparison of body acceleration signals from the passive suspension with AFC controller semi active seat suspension system shows up to to 45% improvement to the occupant ride comfort under different vibration intensities.
With the rapid development of electronic sensor and actuator technology, semi-active seat suspension system has become even more practical driven by lower power consumption. Magneto-rheological (MR) dampers are among the best and the most reliable semi active control devices that can produce controllable damping force in seat suspension system to further improve the ride comfort. This paper focus on a new controller scheme named Active Force Control (AFC) to control the damping force of the MR damper to achieve better ride comfort. The phenomenological Bouc-Wen model for MR damper has been simulated in Matlab Simulink to study the effectiveness of the new AFC controller. A sinusoidal signal simulated as vibration source is applied to the seat suspension system to investigate the improvement of ride comfort as well as to ascertain the new AFC controller robustness. Comparison of body acceleration signals from the passive suspension with AFC controller semi active seat suspension system shows improvement to the occupant ride comfort under different vibration intensities.
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