Manh Quan Nguyen

Semi-active suspension control problem: Some new results using an LPV/H∞ state feedback input constrained control

The semi-active suspension control problem faces the challenge of the dissipativity constraints of the semi-active dampers. This induces some compromises (actuator saturation, comfort, road holding...) which need to be taken into account in the control design step. In this paper, a state feedback input constrained control problem for LPV systems is considered with H∞ performance objective. Stabilization conditions based on the Finslers Lemma are derived in order to ensure the stability in the presence of the input saturation, and to attenuate the disturbance effects. To this aim, two different Lyapunov functions are used. For the stability analysis, a generalized sector condition for LPV systems is applied to treat the nonlinearity caused by the actuator saturation. The considered performance objective regards the reduction of the ℒ2 gain from the disturbance to the controlled output. The LPV controller is computed from the solution of LMIs considering a polytopic representation for the LPV closed-loop system. These theoretical results are applied to a semi-active suspension system where the dissipativity conditions of the semi-active dampers are recast as saturation conditions on the control inputs. The comfort criteria is used as a performance objective in this study. Some simulation results are presented in order to illustrate the effectiveness of the proposed approach.

A Model Predictive Control approach for semi-active suspension control problem of a full car

In this paper, a semi-active suspension Model Predictive Control (MPC) is designed for a full vehicle system equipped with 4 semi-active dampers. The main challenge in the semi-active suspension control problem is to tackle with the dissipativity constraints of the semi-active dampers. The constraints are here recasted as input and state constraints. The controller is designed in the MPC framework where the effects of the unknown road disturbances are taken into account. An observer approach allows to estimate the road disturbance information to be used by the controller during the prediction step. Then, the MPC suspension control law with road estimation (but without road preview) is computed by minimizing a quadratic cost function, giving a trade-off between the comfort and the handling, while guaranteeing phyiscal constraints of the semi-active dampers. Simulation results performed on a nonlinear full car model are presented in order to show the effectiveness of the proposed approach.

Comparison of observer approaches for actuator fault estimation in semi-active suspension systems

In this paper, the actuator fault estimation problem of semi-active suspension systems is considered. For instance, an oil leakage in a damper could cause a reduction of the damping force. The fault estimation requires a modeling of the damper fault (both multiplicative and additive fault models can be used). Three observer-based approaches are compared for fault estimation: an observer using fast adaptive fault estimation (FAFE) approach (used for estimation of additive faults), a parametric adaptive observer (AO) and a switched LPV observer (LPVO) (both intended to estimate mulplicative faults). Since the damper fault estimation is strongly affected by the unknown road disturbances, an H∞ performance objective is used to reduce the effect of disturbances on the estimation error for performance assessment. Some simulations are performed on a quarter car model to validate these methodologies and a comparison is then given to show the interest of each method.

A motion-scheduled LPV control of full car vertical dynamics

In this paper, we present a motion-scheduled LPV/H∞ suspension controller that takes into account the three main motions of the vehicle vertical dynamics: bounce, roll and pitch motions. The new approach aims, by using a motion detection method, at designing a controller which is able to adapt the suspension forces at the four corners of the vehicle, in order to mitigate the road-induced effects. The motion detection strategy is based on the supervison of load transfer distributions (pitch and roll motions). The main idea of the LPV control is to use three scheduling parameters, representative of the motion distribution of the car dynamics, in order to adapt and distribute efficiently the suspension actuators. A full 7 degree of freedom (DOF) vertical model is used to describe the body motion (chassis and wheels) and to synthesize the LPV controller. The controller solution, derived in the LPV/H∞ framework, is based on the LMI solution for polytopic systems. Some simulation results are presented that show the effectiveness of this approach.