Preview Path Tracking Control With Delay Compensation for Autonomous Vehicles

Xu, Shaobing; Peng, Huei; Tang, Yuxin

doi:10.1109/tits.2020.2978417

Cited by 62 publications

(32 citation statements)

References 23 publications

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“…Below, is presented the theorem from [30] as the solution of the control synthesis problem for the minimization of (14). Given a compact set Ω ⊂ R s , non-negative {v i } s i=1 numbers, performance level γ > 0, and the open-loop LPV system in (12), the LPV synthesis γ-performance/ v-variation problem is solvable if and only if there exist continuously differentiable matrix functions X : R s → R n×n and Y : R s → R n×n , such that for all ρ ∈ Ω, X(ρ) 0, Y (ρ) 0 and the set of Linear Matrix Inequalities (LMIs) expressed by ( 15) is satisfied. where:…”

Section: Definition 3 Lpv Controllermentioning

confidence: 99%

“…To cite a few, in [11], Model Predictive Control (MPC) approaches were formulated using a non-linear model in the first case and in the second an on-line linearization vehicle model, solving a finite horizon optimal control problem respecting the state constraints for the stabilization of the car. [12] proposes a preview steering control design that tackles communication delay, steering lag and is implemented as a state feedback controller that uses as a feedforward term the future road curvature information. In AUTOPIA program different steering systems were implemented in mass-produced cars focused on fuzzy logic: A cascade control architecture [13] was implemented mimicking a human driver's behaviour and in [14] genetic algorithms were used to adjust automatically a fuzzy steering controller.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gain‐scheduled steering control for autonomous vehicles

Kapsalis

Sename

Milanés

et al. 2020

IET Control Theory & Appl

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This study presents a linear parameter varying (LPV) approach for the lateral control of autonomous vehicles, in order to take into account the whole operating domain of longitudinal speeds, as well as the variation of the look‐ahead distance. Combining a dynamical vehicle model with look‐ahead dynamics, together with an identified actuator model including an input delay, the closed‐loop performances can be achieved and the tracking capabilities can be improved for every speed. This is obtained in particular through ad hoc representation of the look‐ahead time as a parameter‐dependent function. An H∞ LPV control problem is formulated considering parameter‐dependent weighting functions, allowing the control adaptation for all speeds. The synthesis is performed using the gridding approach, in order to account for varying parameter rate. The proposed steering control system has been implemented on a real electric Renault Zoe car. The performances are therefore assessed experimentally on a real test track with a varying longitudinal speed profile, and compared with a classical LPV polytopic controller, which proves the advanced lane‐tracking capabilities of the proposed methodology.

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Section: Definition 3 Lpv Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gain‐scheduled steering control for autonomous vehicles

Kapsalis

Sename

Milanés

et al. 2020

IET Control Theory & Appl

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“…The path tracking problem has been widely studied recently. [2][3][4][5][6] Existing path tracking strategies can be categorized into two types, that is, model-free methods and model-based methods. Model-free methods have the advantages of simplicity and ease of engineering application, such as PID control and fuzzy control methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Huang

Yang

et al. 2022

Intl J Robust & Nonlinear

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In practical autonomous vehicle systems, model uncertainty and measurement noise are two challenging factors that deteriorate path tracking accuracy and system stability. This article proposes an adaptive robust controller to deal with these two factors and enhance path tracking accuracy. First, the path tracking model considering time‐varying uncertainty is built and represented as nominal and uncertain portions. Second, the control law is designed separately for both portions. A linear quadratic regulator controller is utilized to stabilize the nominal system. An additional robust control law is proposed to suppress the matched uncertainty and measurement noise, with an adaptive scheme aimed at estimating the bound outside uncertainty. The path tracking system with the developed control law is proved to possess uniform boundedness, uniform ultimate boundedness properties, and robustness against mismatched uncertainty. Eventually, the effectiveness of the developed controller is validated using both MATLAB/Simulink‐TruckSim co‐simulation and an autonomous vehicle platform. The results demonstrate that the designed adaptive robust control can achieve accurate path tracking in the presence of time‐varying uncertainty and measurement noise.

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“…Some literatures concerned MPC controller for time-delay vehicle system have been addressed Wang et al, 2019). However, most existing papers often assume that the delay is known and fixed (Yu et al, 2018), or do not consider the model uncertainties (Xu et al, 2020) or the external disturbances (Bououden et al, 2016;Nahidi et al, 2019). Therefore, the research on robust MPC controller for vehicle with time-varying delay has not been completely investigated and hence several problems still remain unsolved.…”

Section: Introductionmentioning

confidence: 99%

Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External Disturbances, and Time-Varying Delay

2021

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In this paper, we design a robust model predictive control (MPC) controller for vehicle subjected to bounded model uncertainties, norm-bounded external disturbances and bounded time-varying delay. A Lyapunov-Razumikhin function (LRF) is adopted to ensure that the vehicle system state enters in a robust positively invariant (RPI) set under the control law. A quadratic cost function is selected as the stage cost function, which yields the upper bound of the infinite horizon cost function. A Lyapunov-Krasovskii function (LKF) candidate related to time-varying delay is designed to obtain the upper bound of the infinite horizon cost function and minimize it at each step by using matrix inequalities technology. Then the robust MPC state feedback control law is obtained at each step. Simulation results show that the proposed vehicle dynamic controller can steer vehicle states into a very small region near the reference tracking signal even in the presence of external disturbances, model uncertainties and time-varying delay. The source code can be downloaded on https://github.com/wenjunliu999.

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Preview Path Tracking Control With Delay Compensation for Autonomous Vehicles

Cited by 62 publications

References 23 publications

Gain‐scheduled steering control for autonomous vehicles

Gain‐scheduled steering control for autonomous vehicles

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External Disturbances, and Time-Varying Delay

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