The design of the integrated active front steering and active differential control for handling improvement of road vehicles is undertaken. The controller design algorithm is based on the solution of a set of linear matrix inequalities that guarantee robustness against a number of vehicle parameters such as speed, cornering and braking stiffnesses. Vehicle plane dynamics are first expressed in the generic linear parameter-varying form, where the above-stated parameters are treated as interval uncertainties. Then, static-state feedback controllers ensuring robust performance against changing road conditions are designed. In a first series of simulations, the performance of the integrated controller is evaluated for a fishhook manoeuvre for different values of road adhesion coefficient. Then, the controller is tested for an emergency braking manoeuvre executed on a split-μ road. In all cases, it is shown that static-state feedback controllers designed by the proposed method can achieve remarkable road handling performance compared with uncontrolled vehicles.

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.

This paper presents a gain-scheduled active steering control and active differential design method to preserve vehicle stability in extreme handling situations. A new formulation of the bicycle model in which tyre slip angles, longitudinal slips and vehicle forward speed appear as varying vehicle parameters is introduced. Such a model happens to be useful in the design of vehicle dynamics controllers scheduled by vehicle parameters: after having expressed the parametric bicycle model in the parametric descriptor form, gain-scheduled active steering and differential controllers are designed to improve vehicle handling at ‘large’ driver-commanded steering angles. Simulations reveal the efficiency of the selected modelling and controller design methodology in enhancing vehicle handling capacity during cornering on roads with varying adhesion coefficient and under variable speed operation.

The purpose of this study is to design high-performance active braking control and observer algorithms for passenger vehicles equipped with electromechanical brake systems. These algorithms are designed to be adaptive with changing driving and road conditions in a switched multiple-model manner to ensure high performance and robustness. The effectiveness of a set of multiple-model switching lead-lag controllers is evaluated during transitions between different road friction coefficients. Meanwhile, a multiple-model switching observer algorithm is developed to estimate the shape of the tyre braking force curve with respect to the longitudinal slip. Each switched observer predicts signals according to its preset tyre model. The observers are designed based on different Burckhardt tyre models that are parameterized for different road conditions. In our simulations, the value of the friction coefficient is assumed to be unknown and our switching algorithms are observed to estimate successfully the varying friction coefficients by comparing a quadratic cost function of measured signals from the vehicle with signals generated by observers. We demonstrate that our algorithms provide high reliability and fast response, thus ensuring a stopping distance close to the theoretical minimum.