Vehicle active control is an important technology for vehicle safety and performance. Most vehicle control systems are based on models, and the effects of model-based controllers are more dependent on the model parameters. In order to improve the adaptability and the veracity of a control system, a new vehicle predictive control method is proposed. Based on the recursive optimized version of the predictor-based subspace identification method, the vehicle model can be identified from input–output data. According to the predicted outputs and the optimal criterion, two predictive controllers are derived. One is called the recursive subspace predictive controller, and the other is called the recursive subspace predictive controller with integrator, which is an improved form to handle integrated white noise. Compared with the outputs from traditional predictive control, the predicted outputs in this paper are obtained directly from the subspace identification process at each step, which avoids the difficult process of solving Diophantine equations. In this paper, a simulation vehicle model, which is made up of a seven-degree-of-freedom vehicle model and a magic formula tyre model, is used to verify the effectiveness of the controllers. By numerical examples, it can be shown that the proposed methods not only are effective for vehicle control in the linear domain but also can track the desired values well and can improve the effect of vehicle control even when the lateral tyre forces reach saturation.

Modelling of vehicle handling dynamics has received a renewed attention in recent years. Different from traditional vehicle modelling, a novel data-driven identification method for vehicle handling dynamics is proposed, which can avoid the problems of the under-modelling and parameter uncertainties in the first-principle modelling process. By first-order Taylor expansion, the nonlinear vehicle system can be linearised as a slowly linear time-varying system with fourth-order. In order to identify the derived identifiable model structure, a recursive subspace method is presented. Derived by optimal version of predictor-based subspace identification (PBSID opt ) and projection approximation subspace tracking (PAST), the identification method is numerical stability and gives an unbiased estimation for the closed-loop system. Based on standard road tests, the proposed modelling method is proven effective and the obtained model has good predictive ability. Additionally, it is noted that the model obtained from the initial phase of straight driving is just a mathematical model to describe the relationship between input and output. And when the vehicle is steering, the model can converge to a stable phase quickly and represent vehicle dynamic performance.

Modeling of vehicle behavior based on the identification method has received a renewed attention in recent years. In order to improve the linear time-invariant vehicle identification model, a more general identifiable vehicle model structure is proposed, in which time-varying characteristics of vehicle speed and cornering stiffness are taken into consideration. To identify the proposed linear time-varying vehicle model, a well-established data-driven method, named recursive optimized version of predictor-based subspace identification, is introduced. Before vehicle model identification, the influences of four parameters in the subspace algorithm are studied based on pulse input road test. And then a set of practical optimal parameters are chosen and used for the vehicle model identification. Through a series of standard road tests under different maneuvers, the linear time-varying vehicle model can be identified in real-time. It is demonstrated by comparison that the predicted outputs of the proposed vehicle model are much closer to the real vehicle outputs and the model has a wider range of application.

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