Intelligent PD controller design for active suspension system based on robust model-free control strategy

Abstract

A novel active suspension control design method is proposed for attenuating vibrations caused by road disturbance inputs in vehicle suspension systems. For the control algorithm, we propose an intelligent PD controller structure that effectively rejects online estimated disturbances. The main theoretical techniques used in this paper consist of an ultra-local model which replaces the mathematical model of quarter car system and a new algebraic estimator of unknown information. The measurement of only input and output variables of the plant is required for achieving the reference tracking task and the cancellation of unmodeled exogenous and endogenous perturbations such as roughness road variation, unpredictable variation of vehicle speed and load variation. The performance and robustness of the proposed active suspension algorithm are compared with ADRC control and LQR control. Numerical results are provided for showing the improvement of passenger comfort criteria with model-free control.

MIMO intelligent-PID controller design for half car system based on model free control technique

“…In previous works, researchers have been mostly interested in simple quarter car models with two degrees of freedom (Haddar et al, 2019b). In this work, a half car model with four degrees of freedom is presented.…”

“…Several studies were carried to integrate this control method to a quarter car model which is a two degree of freedom vehicle ride model. For example, a novel intelligent--PD controller for the quarter car model was proposed by Haddar et al (2019b). Furthermore, Haddar et al (2017) suggested a method for classical Active Disturbances Rejection Control (ADRC) (Hasbullah et al, 2015) to ameliorate the dynamic response and prevent the problem of uncertainty parameters.…”

“…To avoid the problem of chattering, Wang et al (2019) proposed a new integral sliding mode strategy applied to a quarter car model. In fact, the tuning of intelligent controller parameters is quite simple (Haddar et al, 2019b) while the calibration process of ADRC (Hasbullah and Faris, 2017) is more complicated since it has many tuning parameters to be chosen by the designer. Furthermore, due to the choice of the classical Extended State Observer bandwidth (Pusadkar et al, 2019;Wang et al, 2019), a trade--off between the tracking performance and the perturbation rejection can be found (Pan et al, 2015).…”

Intelligent optimal controller design applied to quarter car model based on non-asymptotic observer for improved vehicle dynamics

“…Hence, this proposed strategy requires minimal a priori information of the plant. On the other hand, a new intelligent proportional–derivative (i-PD) controller for active suspension system with 2 degrees of freedom was proposed by Haddar et al 10 In this study, i-PD was designed similarly to active disturbance rejection control (ADRC). For solving the problem of precise tuning process requirement, 11,12 Haddar et al 13 suggested a modification in classical ADRC structure to ameliorate the dynamic response and prevent the problem of sprung mass uncertainties.…”

Model-free-adaptive-based data-driven method for three-axis Gimbal control

“…This article is focused on the data-driven MFAC-based approach. 8,9 Design and analysis of the controller depend only on the physical system’s I/O data, regardless of the order of the system. The controller is a hybrid of SMC and MFAC to control the position of a three-axis camera Gimbal platform.…”