Semi-active Suspension Model Research Articles

Simulación y control de una suspensión semiactiva: caso de una bicicleta para descenso

In this paper, we have used commercial software ADAMS© and Matlab© to simulate a rider-bike-track in a downhill race. The bike suspension dynamic behavior was analyzed by developing a novel control algorithm denominated MTB for a semi-active suspension model. Nevertheless, the control algorithm is not the only original contribution of this work. Even though the semi-active model has been used in previous projects, to the best of our knowledge they have not included bike, rider and track as variables in the system. We have simulated the control actions the rider has to develop in a race with a specific track, feeding the model with some data from literature and others were obtained by direct measures. We have also included a simulation using the most referenced control algorithm, Skyhook, for semi-active suspension. This was done with the purpose of comparing this algorithm with the performance of our MTB algorithm.

TIME DELAY ON SEMI-ACTIVE SUSPENSION AND CONTROL SYSTEM

The mathematic model of semi-active suspension with time-delay is built. The theory analysis and the calculational methods of the critical time-delay of the semi-active suspension with time-delay are discussed, and according to those, the tunable damper and control system of semi-active suspension are designed. Based on that, the bench test of 1/4 model of semi-active suspension system is done. The effect of time delay in the control path on dynamic performance of semi-active suspension equipped with fuzzy control is studied. The calculation is the basal same as the test. It is the foundation for the study of destabilizating mechanics and stability control of semi-active suspension.

Study on the Semi-Active Suspension Control System and Its Stability Based on T-S Fuzzy Neural Control Strategy

A semi-active vehicle suspension model is built, and semi-active suspension control system based on T-S fuzzy neural control strategy is designed. Then, the stability of the control system is analyzed and the condition of stability of the system is deduced. Simulations and experiments are carried out and their results accord with each other, which shows that the controller is stable, valid and has strong robust performance.

Integration of Bilinear Systems and Neural Networks for Designing Nonlinear Semi-Active Suspensions

This paper presents a new design method of semi-active suspensions based on the integration of neural networks and bilinear systems. It is known that semi-active suspensions with ideal linear components have a bilinear structure. However actual semi-active suspensions with nonlinear components have an structure which is not purely bilinear. In order to improve the performance of semi-active suspensions, neural networks and bilinear systems are integrated and used for the identification and optimal control of nonlinear semi-active suspensions. The validity and applicability of the proposed method are analyzed and verified theoretically and experimentally using a semi-active suspension model equipped with piezoelectric actuators.