A combined nonlinear longitudinal and lateral vehicle control is investigated. Flatness-based nonlinear control and new algebraic estimation techniques for noise removal and numerical differentiation are the main theoretical tools. An accurate automatic pathtracking via vehicle steering angle and driving/braking wheel torque is thus ensured. It combines the control of the lateral and longitudinal motions in order to track straight or curved trajectories and to perform a combined lane-keeping and steering control during critical driving situations such as obstacle avoidance, stop-and-go control, lane-change maneuvers or any other maneuvers. Promising results have been obtained with noisy experimental data, which were acquired by a laboratory vehicle with high dynamic loads and high lateral accelerations.

A combined longitudinal and lateral vehicle control is presented. It employs flatness-based control and new algebraic estimation techniques for the numerical differentiation of noisy signals. This nonlinear control is designed for automatic path-tracking via vehicle steering angle and driving/braking wheel torque. It combines the control of the lateral and longitudinal movements in order to ensure an accurate tracking of straight or curved trajectories. It can also be used to perform a combined lane-keeping and steering control during critical driving situations such as obstacle avoidance, stop-and-go control, lane-change maneuvers or any other maneuvers. Promising results have been obtained using the noisy experimental data acquired by a laboratory vehicle under high dynamic loads and characterized by high lateral accelerations.

Abstract-In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. Let us point out that a "good" modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to "intelligent" controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this work is dedicated to present simulation results with actual data. Actual data, used in Matlab as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatnessbased control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems.

To cite this version:Lghani Menhour, Brigitte D'Andréa-Novel, Michel Fliess, Hugues Mounier. Abstract-The newly introduced model-free control is applied to a multivariable decoupled longitudinal and lateral vehicle control. It combines two outputs (lateral and longitudinal motions) via two inputs (braking/traction wheel torques and steering angle). It yields driving maneuvers requiring a control coordination of steering angle, braking and traction torques, in order to ensure an accurate tracking in straight or curved trajectories. It is also robust with respect to modeling errors and parametric uncertainties, even during critical driving situations, where such a control is required. Convincing computer simulations are displayed with noisy real data from a laboratory vehicle, which were used as reference trajectories and acquired under high lateral accelerations.

A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems.