This paper presents an investigation into the problem of controlling a heterogeneous vehicle platoon, focusing on two aspects: the impact of noise in sensor measurements and the effect of road slope, wind, and rolling resistance on the longitudinal dynamics of each vehicle. To maintain an adequate separation between each follower, a static output feedback (SOF) controller is designed within the predecessor-leader-follower vehicle platoon topology. In order to mitigate the impact of sensor measurement noise and external disturbances on the system, robust control theory is incorporated into the controller design. The engine’s control input is adjusted to compensate for external road disturbances affecting the longitudinal dynamics of each vehicle. This estimation is achieved through the use of a Kalman filter. Closed-loop stability of the heterogeneous vehicle platoon is ensured through a Lyapunov functional analysis. Simulations have shown that the proposed methodology achieves smoother platoon following than a strategy that does not consider or compensate the effect of disturbances on the longitudinal dynamics of the vehicle.

Tire slip control is one of the most critical topics in vehicle dynamics control, being the basis of systems such the Anti-lock Braking System (ABS), Traction Control System (TCS) or Electronic Stability Program (ESP). The highly nonlinear behavior of tire–road contact makes it challenging to design robust controllers able to find a dynamic stable solution in different working conditions. Furthermore, road conditions greatly affect the braking performance of vehicles, being lower on slippery roads than on roads with a high tire friction coefficient. For this reason, by knowing the value of this coefficient, it is possible to change the slip ratio tracking reference of the tires in order to obtain the optimal braking performance. In this paper, an $H_{\infty }$ controller is proposed to deal with the tire slip control problem and maximize the braking forces depending on the road condition. Simulations are carried out in the vehicular dynamics simulator software CarSim. The proposed controller is able to make the tire slip follow a given reference based on the friction coefficient for the different tested road conditions, resulting in a small reference error and good transient response.

This paper addresses the problem of integrated motion planning and trajectory tracking for multiple intelligent vehicles in unsignalized intersections. A depth-first spanning tree method is employed to determine the optimal sequence for vehicle traversal across the intersection. It must be acknowledged that the strategy presented here does not guarantee the prevention of collisions. Therefore, a centralized planning algorithm is designed for collision-free planning of speed profiles for each vehicle. It is essential that vehicles are able to follow the designated routes with precision to avoid the creation of additional conflict points at intersections. To address this issue, a novel trajectory tracking method is introduced. Given the inherent limitations in measuring each vehicle state, and the necessity of constraining both the input and the vehicle states to ensure system stability, this work derives the linear matrix inequality conditions for the design of a robust static output- feedback trajectory tracking controller, taking into account the aforementioned constraints. In order to validate the proposed method, cooperative driving in intersections has been evaluated under co-simulations with Simulink and Unreal Engine. In comparison to alternative state-of-the-art methodologies, the proposed method has the potential to reduce the time of arrival of the last vehicle by up to 25.63%. Furthermore, the lateral deviation and velocity tracking errors have been reduced by up to 50%.

In recent years, there has been a great deal of interest in the development of fault detection and isolation (FDI) techniques because they have been found to be important in road transport systems to ensure safe operation, reliability, and maintainability. Active suspension systems (ASSs) play an important role in passengers vehicles, especially in autonomous vehicles, because they can adapt based on the information provided by on-board sensors, thereby improving passengers' comfort and safety. However, the possible occurrence of faults in critical components, such as actuators and sensors requires robust fault diagnosis schemes to ensure good system performance and reliability. Numerous investigations exist on identification and estimation of sensor and actuator faults in ASSs, but faults in both types of components are never considered. This article proposes a new fault diagnosis scheme that allows integrated detection and estimation of actuator and sensors faults in ASSs. The proposed methodology uses two unknown input observers (UIOs) to estimate actuator faults and sensor faults separately. To avoid coupling between the estimations, it is also proposed a switch off mechanism for the actuator so that the coupled deflection sensor and actuator faults can be distinguished and isolated. Finally, signal flags are generated to distinguish the faulty suspension component and refine the UIO estimations.

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system.

In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload.