NeuroFuzzy Adaptive Control for Full-Car Nonlinear Active Suspension with Onboard Antilock Braking System

Abstract

In this paper, the dynamic behavior of the nonlinear full-car model having active suspensions with nine degrees of freedom including driver, passenger seats and antilock braking system (ABS) is analyzed. The comfort analysis of a driver and passengers of the full car with active suspension model including all forms of nonlinearities is very rare in the literature. The literature also lacks the comfort analysis of the driver and passengers, while the car accelerates and decelerates during cornering. The nonlinearities in the proposed model include the dry friction nonlinearities of the suspension dampers and the geometric nonlinearities of the four corners of the car chassis. Modified adaptive NeuroFuzzy Takagi-Sugeno-Kang (NFTSK) control strategies are presented for the vehicle active suspension control to improve the ride quality, road holding capability and vehicle stability. Separate control strategy is used for ABS to avoid wheel locking and slipping for providing better roadwheel contact. The paper also investigates the coordination of active suspensions and ABS to further enhance the performance of the ABS control. To validate the performance of the proposed intelligent control strategies, the response of the vehicle nonlinear model due to road irregularities is evaluated using various performance indexes. The results are then compared with passive control to verify the performance of modified adaptive NFTSK control.

A Novel Adaptive and Nonlinear Electrohydraulic Active Suspension Control System with Zero Dynamic Tire Liftoff

“…The test results showed the effectiveness of the proposed integrated method. In [5], a neuro-fuzzy adaptive control for a full car nonlinear active suspension with onboard antilock braking system was investigated. A comparative study was done between the intelligent control system and passive suspension.…”

Modelling and Analysis of Automobile Vibration System Based on Fuzzy Theory under Different Road Excitation Information

“…It has been known that the intelligent control theory has the ability of logical reasoning and decision-making, and it is best suited to solve the complexity and uncertainty system [8][9][10]. Therefore, a variety of intelligent control methods has been widely used like the fuzzy control, neural network, genetic algorithm, and so on [11][12][13][14]. Huang et al designed a fuzzy controller for converting chaos into periodic motion of stable performance based on the neuralnet tyre model [10].…”

“…Huang et al designed a fuzzy controller for converting chaos into periodic motion of stable performance based on the neuralnet tyre model [10]. Riaz et al presented adaptive Neuro Fuzzy Takagi-Sugeno-Kang control strategies for the vehicle active suspension to improve the ride quality and vehicle stability [13]. Zirkohi and Lin proposed an interval type-2 fuzzy-neural network approach incorporating the Lyapunov design approach and SMC method to improve controller robustness [14].…”