Design of Fault-Tolerant Control for Trajectory Tracking

International audienceThe paper proposes a fault-tolerant integrated control system with the brake and the steering for developing a driver assistance system. The purpose is to design a fault-tolerant control which is able to guarantee the trajectory tracking and lateral stability of the vehicle against actuator fault scenarios. Since both actuators affect the lateral dynamics of the vehicle, in the control design a balance and priority between them must be achieved. The method is extended with a fault-tolerant feature based on a robust LPV method, into which the detected fault information are incorporated. The control design is performed by using the Matlab/Simulink software and the verification of the designed controller is performed by using the CarSim software

Robust Fault-Tolerant Control of In-Wheel Driven Bus with Cornering Energy Minimization

The aim of this paper is to design fault-tolerant and energy optimal trajectory tracking control for a four-wheel independently actuated (FWIA) electric bus with a steer-by-wire steering system. During normal driving conditions, the architecture of the proposed controller enables the bus to select an energy optimal split between steering intervention and torque vectoring, realized by the independently actuated in-wheel motors by minimizing the cornering resistance of the bus. In the case of skidding or a fault event of an in-wheel motor or the steering system, a high-level control reconfiguration using linear parameter varying (LPV) techniques is applied to reallocate control signals in order to stabilize the bus. The main novelty of the paper is the control reconfiguration method based on the specific characteristics of the in-wheel bus which enables introducing such scheduling variables, with which the safety and efficiency of the FWIA bus can be enhanced

Supervisory fault tolerant control of the GTM UAV using LPV methods

A multi-level reconfiguration framework is proposed for fault tolerant control of over-actuated aerial vehicles, where the levels indicate how much authority is given to the reconfiguration task. On the lowest, first level the fault is accommodated by modifying only the actuator/sensor configuration, so the fault remains hidden from the baseline controller. A dynamic reallocation scheme is applied on this level. The allocation mechanism exploits the actuator/sensor redundancy available on the aircraft. When the fault cannot be managed at the actuator/sensor level, the reconfiguration process has access to the baseline controller. Based on the LPV control framework, this is done by introducing fault-specific scheduling parameters. The baseline controller is designed to provide an acceptable performance level along all fault scenarios coded in these scheduling variables. The decision on which reconfiguration level has to be initiated in response to a fault is determined by a supervisor unit. The method is demonstrated on a full six-degrees-of-freedom nonlinear simulation model of the GTM UAV

LPV methods for fault-tolerant vehicle dynamic control

International audienceThis paper aims at presenting the interest of the Linear Parameter Varying methods for vehicle dynamics control, in particular when some actuators may be in failure. The cases of the semi-active suspension control problem and the yaw control using braking, steering and suspension actuators will be presented. In the first part, we will consider the semi-active suspension control problem, where some sensors or actuator (damper leakage) faults are considered. From a quarter-car vehicle model including a non linear semi-active damper model, an LPV model will be described, accounting for some actuator fault represented as some varying parameters. A single LPV fault-tolerant control approach is then developed to manage the system performances and constraints. In the second part the synthesis of a robust gain-scheduled H1 MIMO vehicle dynamic stability controller (VDSC), involving front steering, rear braking, and four active suspension actuators, is proposed to improve the yaw stability and lateral performances. An original LPV method for actuator coordination is proposed, when the actuator limitations and eventually failures, are taken into account. Some simulations on a complex full vehicle model (which has been validated on a real car), subject to critical driving situations (in particular a loss of some actuator), show the efficiency and robustness of the proposed solution

Intelligent Road-Adaptive Semi-Active Suspension and Integrated Cruise Control †

The availability of road and vehicle data enables the control of road vehicles to adapt for different road irregularities. Vision-based or stored road data inform the vehicle regarding the road ahead and surface conditions. Due to these abilities, the vehicle can be controlled efficiently to deal with different road irregularities in order to improve driving comfort and stability performances. The present paper proposes an integration method for an intelligent, road-adaptive, semi-active suspension control and cruise control system. The road-adaptive, semi-active suspension controller is designed through the linear parameter-varying (LPV) method, and road adaptation is performed with a road adaptivity algorithm that considers road irregularities and vehicle velocity. The road adaptivity algorithm calculates a dedicated scheduling variable that modifies the operating mode of the LPV controller. This modification of operation mode provides a trade-off between driving comfort and vehicle stability performances. Regarding the cruise control, the velocity design of the vehicle is based on the ISO 2631-1 standard, the created database, and the look-ahead road information. For each road irregularity, the velocity of the vehicle is designed according to previous measurements and the table of ISO 2631-1 standard. The comfort level must be selected in order to calculate dedicated velocity for road irregularity. The designed velocity is tracked by the velocity-tracking controller evaluated with the LPV control framework. The designed controllers are integrated, and the operation of the integrated method is validated in a TruckSim simulation environment