Sliding mode-based lateral vehicle dynamics control using tyre force measurements

Madhusudhanan, Anil Kunnappillil; Corno, Matteo; Holweg, Edward

doi:10.1080/00423114.2015.1066018

Cited by 18 publications

(4 citation statements)

References 18 publications

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“…Compared with the sliding mode controller, it has better tracking accuracy and convergence speed, good robustness, and capability to resist the disturbance of uncertain factors. In Kunnappillil et al [13], a vehicle lateral dynamics control method based on tire force measurement was proposed, and the gain scheduling sliding mode control technology was adopted to solve the uncertainty of the vehicle model. Zhao et al [14] designed a robust tracking controller for the autonomous driving fleet system by taking the performance of the artificial swarm system as a constraint condition, which is robust to the vehicle system with modeling and parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Optimal robust vehicle motion control under equality and inequality constraints

Zhang

Song

Yang

et al. 2022

Asian Journal of Control

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In this paper, a new robust controller is proposed to improve the motion control performance of an autonomous four‐wheel steering vehicle. The vehicle is subject to time‐varying uncertainties caused by parameter perturbation and unmodeled disturbance. First of all, the vehicle motion control problem is modeled as a constraint‐following problem with the goal to drive the vehicle system to follow the given constraints. For safety reasons, inequality constraints are imposed on the lateral displacement of the vehicle. Next, the diffeomorphism mapping method is used to deal with the inequality constraints in the vehicle motion control, and the constrained lateral displacement space is mapped to an unbounded space. On this basis, a new multi‐parameter robust constraint‐following control is designed. Based on Lyapunov stability theory, the approximate constraint tracking performance of the path tracking system is proved. In order to make a trade‐off between the system performance and control cost, a multi‐parameter optimization problem is established, and the optimal robust controller is obtained. Last, the main theoretical results are verified by the Carsim‐Simulink co‐simulations.

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Section: Introductionmentioning

confidence: 99%

Optimal robust vehicle motion control under equality and inequality constraints

Zhang

Song

Yang

et al. 2022

Asian Journal of Control

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“…high speed and low adhesion road). [10][11][12] One of the most common methods is active front steering (AFS). Cui et al 10 proposes a multi-constraints model predictive control (MPC) method for path tracking on the adhesion-changing road.…”

Section: Introductionmentioning

confidence: 99%

“…However, the longitudinal slip ratios of wheels are not considered in this work, thus the control performance may decline if the road friction coefficient is lower enough. Kunnappillil Madhusudhanan et al 11 proposes a tire utilization coefficient control (TUCC) strategy, in which the nonlinearities and uncertainties in higher steering angles were handled by a gain scheduling sliding mode controller. To improve the yaw stability in extreme path tracking tasks, an MPC-based lateral control approach is proposed.…”

Section: Introductionmentioning

confidence: 99%

Coordinated yaw stability control for extreme path tracking of 4WIDEVs based on predictive control

Lin

Liang

Gong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve safety and stability of four-wheel independent drive electric vehicles (4WIDEVs) in extreme path tracking maneuvers, a hierarchical yaw stability control scheme based on predictive control is proposed in this work. The upper-level stability control strategy coordinates active front steering (AFS) and direct yaw moment control (DYC), yielding desired front steering angle and longitudinal force of each tire. The control task is formulated into a finite horizon optimal control problem, which is solved by hybrid model predictive control (hMPC) algorithm. The lower-level slip ratio control strategy calculates and tracks the optimal longitudinal slip ratio of each tire for implementation of the longitudinal tire force distributions in a wide range of road friction coefficient. Based on a qualified piecewise affine (PWA) approximation of lateral tire force models, the stable regions of vehicle states under multiple varying inputs are obtained by analyzing trajectories of fixed points. Furthermore, the control modes of the upper-level control strategy with different control inputs and state constraints are determined by judging bifurcation point of system. Finally, the proposed control approach is verified and compared through the dSPACE-based hardware-in-the-loop (HIL) experiments. The results indicate that the proposed yaw stability control strategy can improve yaw stability performance and reduce the tracking error significantly in extreme situations.

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“…Using sliding mode control based on Lyapunov stability, proper results of stability and path tracking are presented in literature. [10][11][12][13][14] However, its integration with the adaptive control law, 15 fuzzy systems, 16 adaptive fuzzy systems, 17 the proportional integral (PI) controller 18 and fuzzy neural networks (FNNs) 19 improved the results compared to the classical sliding mode controller. In Janbakhsh et al, 15 switching gain is updated based on the sliding surface.…”

Section: Introductionmentioning

confidence: 99%

Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

Norouzi

Masoumi

Barari

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part K:

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In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

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Sliding mode-based lateral vehicle dynamics control using tyre force measurements

Cited by 18 publications

References 18 publications

Optimal robust vehicle motion control under equality and inequality constraints

Optimal robust vehicle motion control under equality and inequality constraints

Coordinated yaw stability control for extreme path tracking of 4WIDEVs based on predictive control

Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

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