In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that the vehicle follows the target values of the yaw rate and the sideslip angle. The simulation results show the effectiveness of the proposed control method when the vehicle is subjected to different cornering steering manoeuvres such as change line and J-turn under different driving conditions (dry road and snow-covered).

This paper proposes a multi-input multi-output (MIMO) method for path tracking control of autonomous vehicles under network-induced delays while taking into account the roll dynamics to improve both the driving safety and the passenger comfort. The steering control is directly applied to the front wheels, while the anti-roll moment is exerted by an active suspension. The asynchronous phenomenon caused by the sampling process and the time-varying vehicle speed are explicitly taken into account in the control design using a polytopic linear parameter-varying (LPV) control approach. Moreover, to avoid using costly vehicle sensors and complex control structures, a static output feedback (SOF) control scheme is considered. An effective event-triggering mechanism is also proposed to alleviate the communication burden of the vehicle networked control system. Based on augmented Lyapunov-Krasovskii functional, the control design conditions are derived to guarantee the vehicle closed-loop stability under the effects of transmission delays, event-triggered control signals and time-varying parameters. The design procedure is reformulated as an iterative optimization problem involving linear matrix inequality (LMI) constraints, which can be effectively solved with available numerical solvers. The proposed event-triggered SOF controller is evaluated with the vehicle dynamics simulation software CarSim under several dynamic scenarios. A comparative study with related vehicle control results is performance to emphasize the effectiveness of the control method in terms of path tracking performance, driving safety and comfort, and data communication efficiency of the vehicle networked control system.

Mass is an important parameter in vehicle dynamics because it affects not only safety but also comfort. The mass influences the three movements corresponding to vehicle dynamics: longitudinal, lateral, and vertical. Therefore, having an accurate value of mass is essential for having a suitable model which will lead to proper controller and observer operation. Additionally, unlike other vehicle parameters, the mass can vary during a trip due to loading and unloading items and passengers onto the vehicle, greatly influencing its dynamics. This is critical in heavy vehicles where the mass can vary by around 400%. Therefore, the mass must be updated online. The novelty of this paper is the construction of a state-parameter observer which allows the mass under static and dynamic driving conditions to be estimated using measurements from sensors that can be mounted easily on vehicles. In this study, a vertical complete model is considered based on the dual Kalman filter for mass and road irregularities estimation using the data obtained from suspension deflection sensors and a vertical accelerometer. Both simulation and experimental results are carried out to prove the effectiveness of the proposed algorithm.