Fault-Tolerant Control for Intelligent Electrified Vehicles Against Front Wheel Steering Angle Sensor Faults During Trajectory Tracking

Wang, Zihao; Ding, Xiaolin; Li, Shaohua; Wang, Zhenpo

doi:10.1109/access.2021.3075325

Cited by 50 publications

(19 citation statements)

References 21 publications

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“…Besides a perception and decision-making system, advanced X-by-wire chassis, including drive-by-wire, steer-by-wire, brake-by-wire and active/semiactive suspension subsystems are of vital importance to improving the performance and safety of connected and autonomous vehicles. Zhang et al [9] proposed a fault-tolerant control method for steer-by-wire systems to mitigate the undesirable influence of front wheel steering angle sensor faults via the use of the Kalman filtering technique. A complete and systematic survey on chassis coordinated control methods for full X-by-wire vehicles can be found in [10].…”

Section: Introductionmentioning

confidence: 99%

“…The control inputs are obtained by solving the optimization problem (9). The first element in the control inputs is taken as the optimal control at the current time.…”

mentioning

confidence: 99%

“…To meet the real-time requirement, instead of directly calculating a control sequence by solving (9), we solve an approximate optimization problem by imposing the constraints u k+1 = u k+2 = . .…”

mentioning

confidence: 99%

See 2 more Smart Citations

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

Huang

et al. 2021

Sensors

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Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

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Section: Introductionmentioning

confidence: 99%

“…The control inputs are obtained by solving the optimization problem (9). The first element in the control inputs is taken as the optimal control at the current time.…”

mentioning

confidence: 99%

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A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

Huang

et al. 2021

Sensors

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“…Different connectivity mechanisms have been investigated by Vahidi et al [24] that lead to energy saving potentials of CAVs, and the ways preview information can be used intelligently. However, communication issues, such as time delays, quantization errors, and packet loss pose a significant challenge to vehicle distributed control [25] even driving safety [26]. Up to now, few noticeable works have been documented within the context of EMSs for CAVs considering communication issues.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian-Aware Supervisory Control System Interactive Optimization of Connected Hybrid Electric Vehicles via Fuzzy Adaptive Cost Map and Bees Algorithm

Wang

et al. 2022

IEEE Trans. Transp. Electrific.

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Electrified vehicles are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. Due to the nature of engine-assisted vehicle exhaust systems, pedestrians in close proximity to these vehicles may experience events where specific emission concentrations are high enough to cause health effects. To minimize pedestrians' exposure to vehicle emissions and pollutants nearby, we present a pedestrian-aware supervisory control system for connected hybrid electric vehicles by proposing an interactive optimization methodology. This optimization methodology combines a novel fuzzy adaptive cost map and the Bees Algorithm to optimize power-split control parameters. It enables the self-regulation of inter-objective weights of fuel and exhaust emissions based on the real-time pedestrian density information during the optimization process. The evaluation of the vehicle performance by using the proposed methodology is conducted on the realistic trip map involving pedestrian density information collected from the University College Dublin campus. Moreover, two bootstrap sampling techniques and effect of communication quality are both investigated in order to examine the robustness of the improved vehicle system. The results demonstrate that 14.42% mass of exhaust emissions can be reduced for the involved pedestrians, by using the developed fuzzy adaptive cost map.

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“…[5] proposed a novel hybrid controller, called acceleration slip regulation (ASR). A robust control strategy for an in wheel motor-drive EV has been suggested in [6] to enhance vehicle lateral stability in the presence of time delays, while [7] proposed a fault-tolerant controller which, by driving the steering angle sensor of an EV, ensures the trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

et al. 2021

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This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

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Fault-Tolerant Control for Intelligent Electrified Vehicles Against Front Wheel Steering Angle Sensor Faults During Trajectory Tracking

Cited by 50 publications

References 21 publications

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

Pedestrian-Aware Supervisory Control System Interactive Optimization of Connected Hybrid Electric Vehicles via Fuzzy Adaptive Cost Map and Bees Algorithm

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

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