Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Yu, Jiaxing; Pei, Xiaofei; Guo, Xuexun; Lin, Jianguo; Zhu, Maolin

doi:10.1177/0954407020914666

Cited by 13 publications

(9 citation statements)

References 24 publications

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“…Given that the main objective is that of following a predefined trajectory, the state variables are the difference between the actual position and/or velocity to the references (i.e., the errors), whereas the control input is the vehicle acceleration. When the lateral dynamic is considered, the steering angle and the yaw rate are added as control input and state variable, respectively [60,[65][66][67][77][78][79][80][81][82][83][84][85][86][87][88][89]. It is worth mentioning that no further analyses on the works just cited is brought up, owing to the fact that all the studies use the same state variables and control inputs.…”

Section: Cost Function In Mpc Problemsmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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Section: Cost Function In Mpc Problemsmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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“…During sharp turning, there will be a slip angle at the vehicle mass center, if the additional yaw moment equals to zero. Theoretically, the slip angle can be calculated by equation (24). Usually, the vehicle steering characteristic is under steer, which means in some saturations (sharp turn, etc.)…”

Section: Pftc Strategymentioning

confidence: 99%

“…In this chapter, a model predictive control (MPC) 24 strategy based upper layer yaw stability controller and a PID based longitudinal motion controller were established. Additionally, a tire force distributor with multiple constraints was also established.…”

Section: Aftc Strategymentioning

confidence: 99%

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A hybrid fault-tolerant control strategy for four-wheel independent drive vehicles

Chen

2021

Advances in Mechanical Engineering

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In this paper, a hybrid fault-tolerant control strategy is putted forward to improve the stability of the four-wheel independent drive (4WID) electric vehicle with motor failures. To improve the handling performance of the vehicle with in-wheel motor failures, the faults of in-wheel motors are analyzed and modeled. Then, a model reference adaptive fault observer was designed to observe the faults in real-time. Based on the observation results, there are designed a model predictive control (MPC) based high-performance active fault-tolerant control (AFTC) strategy and a sliding mode control based high-robust passive fault-tolerant control (PFTC) strategy. However, the fault observation results may not always be accurately. For this circumstance, a hybrid fault-tolerant control strategy was designed to make the control method find a balance between optimality and robustness. Finally, a series of simulations are conducted on a hardware-in-loop (HIL) real-time simulator, the simulation results show that the control strategy designed in this paper is effectiveness.

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“…The pure pursuit 9 and Stanley 10 methods are classic path tracking control algorithms based on automotive kinematics, and have simple and real-time characteristics, but do not consider the vehicle’s dynamic performance, which is generally achieved at low speed and under other simple working conditions. To adapt to the automatic vehicle’s complicated and variable driving conditions and consider the nonlinear dynamic characteristics of automatic driving vehicles, the path-based path tracking control method has been extensively investigated 11 , 12 . The dynamic path tracking control method often uses a conventional linear two-degrees-of-freedom (2-DOF) model as a reference model 13 , 14 , and the employed control algorithm includes the PID algorithm 15 , model prediction algorithm 16 , sliding mode control 5 , and reinforcement learning algorithm 17 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic-boundary-based lateral motion synergistic control of distributed drive autonomous vehicle

Wang

Ding

Yang

et al. 2021

Sci Rep

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To improve the path tracking accuracy and yaw stability of distributed drive autonomous vehicles (DDAVs) under extreme working conditions, a cooperative lateral motion control method based on the dynamic boundary is proposed to prevent different road adhesion conditions from affecting the motion stability of DDAVs. Based on the analysis of the DDAV lateral dynamics system coordination mechanism, a dynamic boundary considering the pavement adhesion coefficient is proposed, and the Lateral Motion Synergistic Control System (LMSCS) is designed. The LMSCS is divided into the coordination, control, and executive layers. The coordination layer divides the control domain into the stable, quasi-stable, and unstable domains by the dynamic boundary, and coordinates the control strength of the path following control and yaw stability control. In the control layer, the path following control and yaw stability control laws are designed based on the global fast terminal sliding mode. The executive layer estimates the expected steering wheel angle and expected additional wheel torque. Joint simulations under double line shifting conditions confirmed that LMSCS reflects the impact of the road attachment conditions and improves the path tracking accuracy and vehicle yaw stability. The LMSCS has better overall performance than existing lateral motion control methods.

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Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Cited by 13 publications

References 24 publications

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

A hybrid fault-tolerant control strategy for four-wheel independent drive vehicles

Dynamic-boundary-based lateral motion synergistic control of distributed drive autonomous vehicle

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