Intelligent control system for active suspension for off-road vehicles

Abstract

Since the passive suspension system is not enough for creating good compromise between the road handling and ride comfort for off-road vehicles, this thesis presents a study on the application of an active suspension system. The system is considered under the Neuro-Fuzzy controller (NFC) for two and three-axle half-car off-road vehicle models. In this work, two methods are studied and employed as benchmarks for the NFC. The two methods are fuzzy logic control (FLC) method and neural networks control (NNC) method based on NARMA-L2 neurocontroller. The system designed for this study takes in two types of road disturbances as inputs, namely pothole and random road inputs. The sprung mass vertical and pitch accelerations are the criteria employed for the evaluation of the ride comfort, while the tire deflection is used for the road handling. The work's results demonstrates that, the NFC has a superior performance over the FLC and the NNC in terms of the ride comfort without compromising the rattle-space requirement, since the NFC improved the vertical acceleration of the sprung masses of the two-axle and three axle vehicle models for both the pitch and the random road disturbances more than the NNC and the FLC. On the other hand, the NFC requires higher control effort than the FLC and NNC as it gives the best performance that requires more control effort. The results are compared as well with the responses of the passive suspension system.