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 switchoffmechanism 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 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.

Vehicles today are equipped with control systems that improve their handling and stability. Knowledge of road bank angle and vehicle parameters is crucial for good behavior in this type of control. This paper develops a new method for estimating different states, such as vehicle roll angle, road bank angle, and vehicle parameters. This method combines a dual Kalman filter with a probability density function truncation method to consider the parameter physical limitations. Experimental results show the effectiveness of the proposed method and demonstrate that the incorporation of parameter constraints improves its estimation accuracy. The proposed method provides an estimation of the parameters and the states' physical meaning and the stable values within the real boundary limits in contrast to other estimation methods.

In this paper a system for generation of topological maps is presented. This system is considered as one of the deliberative skills of the mobile robots architecture named AD. AD is a two level architecture: deliberative and automatic. Those skills which require high computational time as consequence of high level reasoning are found in the deliberative level, while the automatic level skills interact with robot sensors and actuators. The topological map generated with this deliberative skill is the map belonging to the EDN navigation system, and is named Navigation Chart. In the Navigation Chart, the information obtained from the chart is stored as nodes and as edges. Nodes correspond to the sensorial events and edges correspond to the sensorimotor skills.