Robust Ride Height Control for Active Air Suspension Systems With Multiple Unmodeled Dynamics and Parametric Uncertainties

Abstract

This paper addresses the problem of ride height tracking for an electronically-controlled active air suspension (AAS) system in the presence of parametric uncertainties and unmodeled dynamics. A mathematical model of a quarter vehicle with AAS system is first built on the basis of thermodynamics to describe the dynamic characteristics. Then, by employing the backstepping technique, a novel height tracking controller is proposed in order to guarantee that 1) the ride height of a vehicle can converge on a neighborhood of the desired height, achieving global uniform ultimate boundedness (GUUB); 2) the controller is robust to the parametric uncertainties by designing parameter estimators and introducing some conservativeness in the control law to dominate the unmodeled dynamics. Moreover, a group of smooth projectors is used to ensure all estimates remain within predefined corresponding bounds. To validate the efficiency and performance of the proposed strategy, the simulation and experimental results are presented and analyzed, showing that the proposed control strategy is superior to the PID controller and the recently proposed hybrid model predictive controller. INDEX TERMS Active air suspension, pneumatic system, automobile height control, robust control, parameter estimation.

Nonlinear Ride Height Control of Active Air Suspension System with Output Constraints and Time-Varying Disturbances

“…Moreover, the adjustment of ride height usually changes the stiffness and hysteresis, and generates perturbations in the AAS system [ 10 , 11 ]. In addition, because of the mechanical structure and travel limitations of the AAS, the ride height movement should always be constrained in a reliable range for safety performance [ 12 , 13 , 14 ]. Therefore, an appropriate ride height controller should be designed for the AAS systems in the presence of perturbations and output constraints.…”

“…Aiming to deal with the aforesaid problems, many results have been reported, such as robust control [ 15 , 16 ], sliding mode control (SMC) [ 11 , 17 , 18 ], fuzzy logic [ 19 ], neural network-based [ 20 ], and backstepping control techniques [ 14 , 21 ]. In [ 16 ], a robust controller for AAS systems was proposed, where the ride comfort and time domain hard constraints were considered.…”

“…Meanwhile, to our best knowledge, the numbers of the needed rules or neurons are difficult to be determined for keeping the estimation error bounded in a specific range. In [ 14 , 21 ], nonlinear backstepping-based height tracking controllers were designed, where some conservativeness was adopted in the control law to reduce the effects of time-varying disturbances.…”

“…In addition to the challenge raised from developing control strategy for handling disturbances, the output height constraint is also considered as a critical issue due to the mechanical structure limitation of the AAS system. Although the nonlinear ride height controllers based on the classic Quadratic Lyapunov function are presented to track predefined trajectories in the presence of perturbations and the height constraint are neglected for the ride height control applications with the AAS system [ 14 , 21 ]. By using the backstepping control approaches, the Barrier Lyapunov Functions (BLFs) have been developed and defined as control Lyapunov candidates for achieving the constrained objectives control [ 13 , 22 , 23 , 24 ].…”

“…In this research, unlike the linearized models used in [ 16 ], the mathematical model with the time-varying disturbances is employed to describe the perturbations in the AAS system. Unlike the disturbance rejection methods presented in [ 14 , 19 , 20 , 21 ], a time-varying disturbance observer is designed in this paper, guaranteeing that the estimation error is bounded by certain value. The designed disturbance observer can guarantee the estimate converges closely to zero.…”