Pith. sign in

REVIEW 3 major objections 5 minor 34 references

A Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A modular control framework with online-learned uncertainty bounds keeps an autonomous vehicle within 0.043 m of a double-lane-change path under injected disturbances, about 60 percent more accurate than two standard robust baselines.

desk verdict A well-structured modular adaptive control paper with an interesting RLS/GPR/Bayesian-optimization coupling, undermined by same-scenario tuning of the robust boundaries before the headline comparison. read the letter →

arxiv 2411.17745 v1 pith:Y4C5RUXN submitted 2024-11-25 eess.SY cs.ROcs.SY

Jiarui Song , Yingbo Sun , Qing Dong , Xuewu Ji This is my paper · ORCID
classification eess.SYcs.ROcs.SY
keywords autonomousvehiclestrajectorytrackingrobustcontrolparameteradaptationrecursiveleastsquaresGaussianprocessregressionBayesianoptimizationyawstability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that an autonomous vehicle can track a reference path accurately and keep yaw stable under uncertain tire stiffness, unmodeled dynamics, and external disturbances without paying a complexity price in any single controller. It proposes a modular framework: an LQR (linear-quadratic regulator) kinematic tracker generates a desired motion, and three robust controllers (LMI, sliding-mode, backstepping) track it while absorbing different robustness factors. The range of uncertain parameters and the sizes of disturbance and model-mismatch bounds are estimated online by recursive least squares and Gaussian process regression, then tuned by Bayesian optimization to reduce conservatism. In a Carsim double-lane-change at 60 km/h with 1000 N lateral force and 1000 N-m yaw disturbance, the claimed maximum tracking error is 0.043 m versus 0.113 m and 0.104 m for MPC (model predictive control) and LMI baselines, with smaller sideslip and smoother steering.

What carries the argument

The central mechanism is the modular decomposition plus the parameter-adaptive boundary estimation loop. A linear-quadratic regulator uses the kinematic tracking-error model to output desired speed, side-slip angle, and yaw rate; three robust controllers (LMI, SMC, BSC) then track these references while handling parameter uncertainty, unmodeled subsystem mismatch, and external disturbances. The loop is closed by recursive least squares identifying tire stiffness, Gaussian process regression learning the residual dynamics to set the envelopes, and Bayesian optimization scaling the robust factor bounds via a global cost function. The LMI design's polytopic uncertainty model [ΔA ΔB] = M F(t)[Na Nb] with F(t)^T F(t) ≤ 1 is the load-bearing object for the stability proofs.

What would settle it

Run the same double-lane-change scenario with tire cornering stiffness reduced by more than the identified range (for example, a 40 percent drop or a change in road friction from μ ≈ 1 to μ ≈ 0.3) and measure lateral tracking error and sideslip envelope. If ARC's maximum error exceeds the claimed 0.043 m margin or the sideslip grows beyond the phase-plane attraction domain, the boundary-estimation loop has not captured the true uncertainty.

Watch

Extended reading notes

Core claim

The central claim is that a parameter-adaptive robust control framework (ARC), which decomposes the driving task into an LQR trajectory-tracking layer and three stacked robust motion-control layers (LMI for longitudinal-lateral dynamics, sliding-mode for lateral-yaw stability, backstepping for wheel dynamics), simultaneously improves tracking accuracy and yaw stability relative to standalone robust controllers. The improvement is attributed to the adaptive determination of robust boundaries: RLS tracks time-varying tire stiffness, GPR learns the magnitude of unmodeled subsystem mismatch and external disturbances, and Bayesian optimization scales those boundaries (1.50, 1.22, 0.51 in the experiments) to balance conservatism against robustness. The paper reports simulation evidence that ARC keeps the maximum lateral tracking error at 0.043 m and yields smaller, smoother sideslip and yaw-rate trajectories than the two baselines.

Load-bearing premise

All real uncertainties, model mismatches, and disturbances stay inside the estimated ranges: the tire parameters stay in the polytope Θ, the mismatch fits the bounded form [ΔA ΔB] = M F(t)[Na Nb] with F(t)^T F(t) ≤ 1, and the disturbance envelopes are correctly captured by the GPR-learned bounds.

Editorial extensions

If this is right

  • Under the paper's assumptions, the same framework should transfer to other reference paths and speeds with no controller redesign, because the robust boundaries adapt online rather than being fixed worst-case constants.
  • The guaranteed asymptotic stability of the LMI/SMC/BSC layers means the composite system's stability does not rely on the LQR tuning, so the tracking layer can be retuned independently.
  • The Bayesian scaling coefficients indicate when the fixed-boundary design is conservative: for parameter uncertainty and internal disturbance the optimal scaling is greater than 1 (boundaries need enlarging), while for external disturbance it is less than 1 (boundaries can shrink).
  • A practical consequence is that vehicle controllers can maintain tracking accuracy under emergency obstacle avoidance without increasing online optimization complexity relative to a single MPC.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Both baseline controllers likely use fixed, conservatively large robust bounds; if their bounds were optimally tuned for this scenario, the reported gap in tracking error might shrink, so the claimed advantage is specifically about adaptive rather than fixed boundary selection.
  • The modular split means the same adaptive boundary loop could be attached to other motion-control methods, such as NMPC or LPV/H-infinity, potentially improving them without changing their inner structure.
  • Because the GPR boundary learner is trained on Carsim data, its performance on roads with different friction surfaces or tire wear would test whether the learned envelopes generalize or require periodic retraining.
  • A testable extension is to run a sine-with-dwell or moose-test maneuver at higher speed to see whether the yaw-stability benefit persists near the handling limits.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper proposes a modular trajectory tracking and motion control framework for autonomous vehicles. A kinematic LQR generates reference speed, sideslip angle, and yaw rate; three robust controllers (LMI, SMC, and back-stepping) track those references while addressing parameter uncertainty, unmodeled subsystem mismatch, and external disturbances. RLS is used for online tire-stiffness identification, GPR for estimating disturbance/model-mismatch bounds, and Bayesian optimization to tune robust-boundary scaling coefficients. Stability is claimed via Lyapunov arguments for each robust controller. The method is evaluated in a MATLAB/Simulink–Carsim joint simulation on a double-lane-change scenario at 60 km/h with injected 1000 N lateral-force and 1000 N·m yaw disturbances, reporting a maximum tracking error of 0.043 m for the proposed ARC framework versus 0.113 m for MPC and 0.104 m for an LMI controller.

Significance. If the framework performs as claimed, it offers a useful modular architecture: separating trajectory tracking from motion control while integrating three robust controllers with online parameter and boundary adaptation. The Lyapunov stability arguments are standard and self-contained, and the pseudocode in Algorithm 1 and Algorithm 2 is sufficiently concrete to reproduce the proposed adaptive strategy. The main empirical claim, however, is currently weakened by the in-sample tuning of the robust-boundary scaling coefficients on what appears to be the same scenario used for the final comparison, as well as by an apparent error in the tire side-slip angle model that feeds the LMI controller. The significance is therefore conditional on addressing these points.

major comments (3)
  1. [Section IV-C, Algorithm 2, Fig. 3, and Section V] The robust-boundary scaling coefficients αb=[1.50, 1.22, 0.51] are optimized by Bayesian optimization to minimize JG (Eq. 53), which includes tracking error, and the text says the optimized values are 'adopted to simulation in section V' (Fig. 3). Since Section V uses the same double-lane-change scenario with the same injected 1000 N / 1000 N·m disturbances, the reported comparison (0.043 m vs 0.113 m and 0.104 m maximum tracking error) is an in-sample tuning comparison, not an out-of-sample validation. The Lyapunov proofs guarantee stability for any feasible bounds, but they do not establish superiority over MPC/LMI; that claim rests entirely on this comparative simulation. Please validate on a scenario distinct from the tuning scenario, or provide cross-validation, sensitivity analysis, or multiple repeated trials with different disturbance realizations.
  2. [Section II-B, Eq. (4)] The tire side-slip angle definitions appear to have the ratio inverted and the sign of the steering term wrong: the paper gives α_fj = (v_x' + a ω_z')/v_y' − δ and α_rj = (v_x' − b ω_z')/v_y', whereas the standard definitions are α_f = δ − (v_y + a ω_z)/v_x and α_r = (b ω_z − v_y)/v_x. Since the LMI controller later outputs a desired side-slip angle α_des and the steering command is computed from it (Section III-B), this inconsistency affects the controller model itself, not just notation. Please correct or clarify the formula and re-check whether the subsequent LMI derivation and the Carsim implementation use consistent kinematic definitions.
  3. [Section IV-B and Section V] The GPR model is trained on data 'sourced from the Carsim simulation software' (Section IV-B), and the robust boundaries are then scaled using closed-loop simulations of the same Carsim model in Algorithm 2. This blurs the distinction between an 'unmodeled subsystem' and a mismatch that the GPR has effectively memorized from the validation plant. Consequently, the claimed robustness to unmodeled dynamics is not independently tested. Please use a validation plant or maneuver that is not part of the GPR training and boundary-optimization procedure, or state this limitation explicitly and temper the corresponding robustness claims.
minor comments (5)
  1. [Section I-B] The sentence 'Nam et al [30] introduces...' is associated with reference [30] (Cordeiro et al.), but the cited methodology on lateral tire force sensors and cornering stiffness estimation appears in reference [29] (Nam et al.). Please correct the citation.
  2. [Throughout] There are numerous typographical and stylistic errors, including 'duo to', 'designes', inconsistent spacing in 'A Vs', and inconsistent use of v_x', v_y' vs. v_x, v_y. A careful proofreading pass is needed.
  3. [Section III-D, Eq. (44)] The Lyapunov derivative is shown to satisfy ˙V_ω(e_ω) ≤ 0, and the text concludes asymptotic stability. Since Γ(e_ω) ≥ ϱ(e_ω)+Γ_0, strict negativity for e_ω ≠ 0 requires Γ_0 > 0 (or an additional argument if Γ_0 = 0). Please state this condition explicitly.
  4. [Section IV-C, Eq. (55)] The UCB acquisition function is written as arg min(μ_b + κ_b σ_b), whereas standard UCB maximizes μ + κσ. If the convention is to minimize the objective after a sign change, the sign of the exploration term should be clarified to avoid ambiguity.
  5. [Figures 7–9] The phase-plane figures lack axis labels and units, and the captions refer to subplots (a)–(c) whose layout is not fully clear from the text. Please add labels and explain what the dots and trajectories represent in each subplot.

Circularity Check

1 steps flagged · score 5.0 of 10

Performance comparison is partly a fit: Bayesian optimization tunes robust-boundary scaling on a simulation cost that includes tracking error, and the optimized coefficients are then used directly in the Section V double-lane-change comparison.

  1. fitted input called prediction [Section IV-C (Algorithm 2, Eq. 53) and Section V (Fig. 5)]
    "J i G = PN k=1 (∥ze∥2 We + ∥av∥2 Wa + ∥φv∥2 Wφ); ... Carry out simulation experiment and record data ... Through algorithm 2, the robust scaling coefficients have been determined ... as 1.50, 1.22 and 0.51 respectively, which will be adopted to simulation in section V ... A double lane change scenario ... extreme values of 1000 N and 1000 N.m ... maximum tracking error of ARC (0.043m) significantly surpasses that of the other controllers (0.113m and 0.104m, respectively)."

    Algorithm 2 selects αb=[αbθ, αbi, αbe] by minimizing JG, whose first term is the tracking-error norm ∥ze∥²_We. The optimized values are then explicitly 'adopted to simulation in section V,' where the same double-lane-change environment with 1000 N and 1000 N·m disturbances is used to report ARC's 0.043 m maximum tracking error against MPC and LMI. The manuscript does not state that the simulation experiments inside CalculateGlobalCost used a scenario distinct from the Section V comparison. Consequently, the headline accuracy figure is obtained after optimizing the very metric on which ARC is compared with the baselines, so the claimed improvement is partly a training-score fit rather than an independent out-of-sample prediction.

full rationale

The control-theoretic derivation is largely self-contained. The LMI stabilizing condition (Eqs. 26-29), the SMC Lyapunov argument (Eqs. 35-37), and the BSC Lyapunov argument (Eqs. 42-44) are proven analytically in the paper and do not rest on the simulation results or on self-citations. Citations to the authors' prior work ([25], [31]) appear as background and comparative context, not as load-bearing justifications for the stability theorems. The circularity is confined to the empirical performance claim: αb is tuned by Bayesian optimization against a cost functional that includes the same tracking-error measure used to declare ARC superior, and the tuned values are directly adopted in the Section V comparison. Because the text does not document a separate tuning scenario, the 0.043 m versus 0.113/0.104 m error comparison is vulnerable to being a training-score comparison rather than a validated prediction. This partial circularity lowers the score but does not invalidate the analytical stability contributions.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The framework rests on a large number of hand-tuned or fitted quantities: the Bayesian-optimized robust scaling coefficients, GPR hyperparameters, RLS forgetting factors, and controller weights. The stability proofs assume bounded, structurally known uncertainty. No new physical entities are introduced.

free parameters (5)
  • Robust scaling coefficients αbθ, αbi, αbe = 1.50, 1.22, 0.51
    Tuned via Bayesian optimization to minimize JG, which includes tracking error, on the double-lane-change scenario used for final validation (Eq 53-55, Fig 3).
  • GPR hyperparameters (l, σf^2, σε^2) = not reported
    Optimized by marginal log-likelihood on Carsim training data (Eq 51); no values given.
  • RLS forgetting factor parameters (λmin, h, σε) = not reported
    Chosen by hand to balance convergence and noise sensitivity (Eq 48).
  • LQR and robust controller weights (Qk, Rk, Q, R, ξ, kω) = not reported
    No numeric values are provided; performance depends on these weights.
  • Bayesian optimization UCB coefficient κb = not reported
    Controls exploration-exploitation trade-off in Eq (55); value not reported.
assumptions (6)
  • standard math Lyapunov stability theorems and Schur complement for LMI conversion
    Used in Section III-B to derive stability conditions from quadratic Lyapunov functions.
  • domain assumption Boundedness of parameter uncertainty and disturbances within compact sets Θ, Ωs, Ωt
    Assumed throughout Section III and IV; if violated, the stability proofs and boundary adaptation fail.
  • domain assumption Polytopic uncertainty representation [ΔA ΔB] = M F(t)[Na Nb] with F^T F <= 1
    Eq (24); assumes the mismatch between nominal and actual linearized model has this norm-bounded structure.
  • domain assumption k(xs) > 0 for the sliding mode system
    Used in Eq (36)-(37) to guarantee the Lyapunov derivative is negative; asserted from the lateral-yaw model.
  • domain assumption GPR provides a statistically valid estimate of model mismatch and disturbances
    The robust boundaries are derived from differences between GPR predictions and RLS model outputs (Algorithm 2); this assumes GPR errors bound the true mismatch.
  • standard math Reference speed or yaw rate nonzero for controllability
    Section II-C: the linearized error model is controllable only if vref or ωref is nonzero; silent for zero-speed maneuvers.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle." pith.science (2026). https://pith.science/paper/Y4C5RUXN

@misc{pith2026241117745,
  author       = {Pith},
  title        = {Pith review of: A Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y4C5RUXN}},
  note         = {Machine review of arXiv:2411.17745}
}
read the original abstract

This paper studies the trajectory tracking and motion control problems for autonomous vehicles (AVs). A parameter adaptive control framework for AVs is proposed to enhance tracking accuracy and yaw stability. While establishing linear quadratic regulator (LQR) and three robust controllers, the control framework addresses trajectory tracking and motion control in a modular fashion, without introducing complexity into each controller. The robust performance has been guaranteed in three robust controllers by considering the parameter uncertainties, mismatch of unmodeled subsystem as well as external disturbance, comprehensively. Also, the dynamic characteristics of uncertain parameters are identified by Recursive Least Squares (RLS) algorithm, while the boundaries of three robust factors are determined through combining Gaussian Process Regression (GPR) and Bayesian optimization machine learning methods, reducing the conservatism of the controller. Sufficient conditions for closed-loop stability under the diverse robust factors are provided by the Lyapunov method analytically. The simulation results on MATLAB/Simulink and Carsim joint platform demonstrate that the proposed methodology considerably improves tracking accuracy, driving stability, and robust performance, guaranteeing the feasibility and capability of driving in extreme scenarios.

Figures

Figures reproduced from arXiv: 2411.17745 by the authors.

Figure 1
Figure 1. Schematic representation of the autonomous vehicle model and its [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the parameter adaptive trajectory tracking and motion [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The adjusted scaling coefficients of robust boundary by Bayesian [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: The driving trajectory (a), tracking error (b) and steering angle (c) in [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: The sideslip angle (a) and angular velocity (b) in simulation results [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: The 2D phase trajectory of sideslip angle and angular velocity in the [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 9
Figure 9. Figure 9: Vx = 16.54m/s, δf = −0.046rad. (a)-(c) represent the phase plane trajectory and phase trajectory points of the three controllers ARC, LMI and MPC, respectively. angle and motion state. It is illustrated that when the projected point, representing the actual side-slip a…
Figure 8
Figure 8. Figure 8: Vx = 16.51m/s, δf = 0.079rad. (a)-(c) represent the phase plane trajectory and phase trajectory points of the three controllers ARC, LMI and MPC, respectively. To further analyze vehicle yaw stability, a phase plane analysis method has been utilized as shown in [PITH_…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages

  1. [1]

    Drive like a machine: Remembering the origin and goal of autonomous driving and intelligent vehicles,

    F.-Y . Wang, “Drive like a machine: Remembering the origin and goal of autonomous driving and intelligent vehicles,” IEEE Trans. Intell. Veh. , vol. 8, no. 7, pp. 3763–3766, 2023

    work page 2023

  2. [2]

    Autonomous vehicles: Autodriver algorithm and vehicle dynamics,

    H. Marzbani, H. Khayyam, D. V . Quoc, and R. N. Jazar, “Autonomous vehicles: Autodriver algorithm and vehicle dynamics,” IEEE Trans. Veh. Technol., vol. 68, no. 4, pp. 3201–3211, 2019

    work page 2019

  3. [3]

    Semantic-level maneuver sampling and trajectory planning for on-road autonomous driving in dynamic scenarios,

    H. Li, G. Yu, B. Zhou, P. Chen, Y . Liao, and D. Li, “Semantic-level maneuver sampling and trajectory planning for on-road autonomous driving in dynamic scenarios,” IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1122–1134, 2021

    work page 2021

  4. [4]

    Graph-based scenario- adaptive lane-changing trajectory planning for autonomous driving,

    Q. Dong, Z. Yan, K. Nakano, X. Ji, and Y . Liu, “Graph-based scenario- adaptive lane-changing trajectory planning for autonomous driving,” IEEE Robotics and Automation Letters , vol. 8, no. 9, pp. 5688–5695, 2023

    work page 2023

  5. [5]

    Finite-time optimized robust control with adaptive state estimation algorithm for autonomous heavy vehicle,

    Y . Liu, X. Ji, K. Yang, X. He, X. Na, and Y . Liu, “Finite-time optimized robust control with adaptive state estimation algorithm for autonomous heavy vehicle,” Mech. Syst. Signal Proc. , vol. 139, p. 106616, 2020

    work page 2020

  6. [6]

    Robust preview path tracking control of autonomous vehicles under time- varying system delays and saturation,

    G. Cai, L. Xu, Y . Liu, J. Feng, J. Liang, Y . Lu, and G. Yin, “Robust preview path tracking control of autonomous vehicles under time- varying system delays and saturation,” IEEE Trans. Veh. Technol., 2023

    work page 2023

  7. [7]

    Gain-scheduling lpv synthesis H ∞ robust lateral motion control for path following of autonomous vehicle via coordination of steering and braking,

    X. Cao, T. Xu, Y . Tian, and X. Ji, “Gain-scheduling lpv synthesis H ∞ robust lateral motion control for path following of autonomous vehicle via coordination of steering and braking,” Veh. Syst. Dyn., vol. 61, no. 4, pp. 968–991, 2023

    work page 2023

  8. [8]

    Tuning of digital pid controllers using particle swarm optimization algorithm for a can-based dc motor subject to stochastic delays,

    Z. Qi, Q. Shi, and H. Zhang, “Tuning of digital pid controllers using particle swarm optimization algorithm for a can-based dc motor subject to stochastic delays,” IEEE Trans. Ind. Electron. , vol. 67, no. 7, pp. 5637–5646, 2019

    work page 2019

Show all 34 references
  1. [9]

    Nested pid steering control for lane keeping in autonomous vehicles,

    R. Marino, S. Scalzi, and M. Netto, “Nested pid steering control for lane keeping in autonomous vehicles,” Control Eng. Practice , vol. 19, no. 12, pp. 1459–1467, 2011

    2011

  2. [10]

    Road-curvature-range-dependent path following controller design for autonomous ground vehicles subject to stochastic delays,

    Q. Shi and H. Zhang, “Road-curvature-range-dependent path following controller design for autonomous ground vehicles subject to stochastic delays,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 10, pp. 17 440– 17 450, 2022

    2022

  3. [11]

    Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles,

    C. Hu, R. Wang, F. Yan, and N. Chen, “Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles,” IEEE Trans. Veh. Technol. , vol. 65, no. 6, pp. 4033–4043, 2015

    2015

  4. [12]

    Dynamic drifting control for general path tracking of autonomous vehicles,

    G. Chen, X. Zhao, Z. Gao, and M. Hua, “Dynamic drifting control for general path tracking of autonomous vehicles,” IEEE Trans. Intell. Veh., 2023

    2023

  5. [13]

    Design, analysis, and experiments of preview path tracking control for autonomous vehicles,

    S. Xu and H. Peng, “Design, analysis, and experiments of preview path tracking control for autonomous vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 1, pp. 48–58, 2019

    2019

  6. [14]

    Mpc-based cooperative control strategy of path planning and trajectory tracking for intelligent vehicles,

    Z. Zuo, X. Yang, Z. Li, Y . Wang, Q. Han, L. Wang, and X. Luo, “Mpc-based cooperative control strategy of path planning and trajectory tracking for intelligent vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 6, no. 3, pp. 513–522, 2021

    2021

  7. [15]

    Mpc-based path tracking with pid speed control for high-speed autonomous vehicles considering time-optimal travel,

    S.-p. Chen, G.-m. Xiong, H.-y. Chen, and D. Negrut, “Mpc-based path tracking with pid speed control for high-speed autonomous vehicles considering time-optimal travel,” J. Cent. South Univ. , vol. 27, no. 12, pp. 3702–3720, 2020

    2020

  8. [16]

    Path-tracking and lateral stabil- isation for autonomous vehicles by using the steering angle envelope,

    Q. Cui, R. Ding, C. Wei, and B. Zhou, “Path-tracking and lateral stabil- isation for autonomous vehicles by using the steering angle envelope,” Veh. Syst. Dyn., vol. 59, no. 11, pp. 1672–1696, 2021

    2021

  9. [17]

    Real-time control for at-limit handling driving on a predefined path,

    T. Novi, A. Liniger, R. Capitani, and C. Annicchiarico, “Real-time control for at-limit handling driving on a predefined path,” Veh. Syst. Dyn., 2019

    2019

  10. [18]

    Nmpc-based path tracking control strategy for autonomous vehicles with stable limit handling,

    T. Fu, H. Zhou, and Z. Liu, “Nmpc-based path tracking control strategy for autonomous vehicles with stable limit handling,” IEEE Trans. Veh. Technol., vol. 71, no. 12, pp. 12 499–12 510, 2022

    2022

  11. [19]

    An adaptive hierarchical trajectory following control approach of autonomous four-wheel independent drive electric vehicles,

    J. Guo, Y . Luo, and K. Li, “An adaptive hierarchical trajectory following control approach of autonomous four-wheel independent drive electric vehicles,” IEEE Trans. Intell. Transp. Syst. , vol. 19, no. 8, pp. 2482– 2492, 2017

    2017

  12. [20]

    Holistic adaptive multi- model predictive control for the path following of 4wid autonomous vehicles,

    Y . Liang, Y . Li, A. Khajepour, and L. Zheng, “Holistic adaptive multi- model predictive control for the path following of 4wid autonomous vehicles,” IEEE Trans. Veh. Technol., vol. 70, no. 1, pp. 69–81, 2020

    2020

  13. [21]

    A gain-scheduled robust controller for autonomous vehicles path tracking based on lpv system with mpc and H∞,

    Y . Tian, Q. Yao, P. Hang, and S. Wang, “A gain-scheduled robust controller for autonomous vehicles path tracking based on lpv system with mpc and H∞,” IEEE Trans. Veh. Technol., vol. 71, no. 9, pp. 9350– 9362, 2022

    2022

  14. [22]

    Lpv static output feedback for constrained direct tilt control of narrow tilting vehicles,

    A.-T. Nguyen, P. Chevrel, and F. Claveau, “Lpv static output feedback for constrained direct tilt control of narrow tilting vehicles,” IEEE Trans. Control Syst. Technol., vol. 28, no. 2, pp. 661–670, 2018

    2018

  15. [23]

    Path tracking and direct yaw moment coordinated control based on robust mpc with the finite time horizon for autonomous independent-drive vehicles,

    H. Peng, W. Wang, Q. An, C. Xiang, and L. Li, “Path tracking and direct yaw moment coordinated control based on robust mpc with the finite time horizon for autonomous independent-drive vehicles,” IEEE Transactions on Vehicular Technology , vol. 69, no. 6, pp. 6053–6066, 2020

    2020

  16. [24]

    Model-predictive-control-based path tracking controller of autonomous vehicle considering parametric uncertainties and velocity-varying,

    S. Cheng, L. Li, X. Chen, J. Wu et al., “Model-predictive-control-based path tracking controller of autonomous vehicle considering parametric uncertainties and velocity-varying,” IEEE Trans. Ind. Electron., vol. 68, no. 9, pp. 8698–8707, 2020

    2020

  17. [25]

    Isolating trajectory tracking from motion control: A model predictive control and robust control framework for unmanned ground vehicles,

    J. Song, G. Tao, Z. Zang, H. Dong, B. Wang, and J. Gong, “Isolating trajectory tracking from motion control: A model predictive control and robust control framework for unmanned ground vehicles,” IEEE Robot. Autom. Lett., vol. 8, no. 3, pp. 1699–1706, 2023

    2023

  18. [26]

    Gain-scheduled steering and braking coordinated control in path tracking of intelligent heavy vehicles,

    Q. Dong, X. Ji, Y . Liu, and Y . Liu, “Gain-scheduled steering and braking coordinated control in path tracking of intelligent heavy vehicles,” J. Dyn. Syst. Meas. Contr. , vol. 144, no. 10, p. 101006, 2022

    2022

  19. [27]

    A novel approach for vehicle inertial parameter identification using a dual kalman filter,

    S. Hong, C. Lee, F. Borrelli, and J. K. Hedrick, “A novel approach for vehicle inertial parameter identification using a dual kalman filter,” IEEE Transactions on Intelligent Transportation Systems , vol. 16, pp. 151–161, 2015

    2015

  20. [28]

    A switching rollover controller coupled with closed-loop adaptive vehicle parameter identification,

    M. Akar and A. D. Dere, “A switching rollover controller coupled with closed-loop adaptive vehicle parameter identification,” IEEE Transac- tions on Intelligent Transportation Systems , vol. 15, pp. 1579–1585, 2014

    2014

  21. [29]

    Lateral stability control of in-wheel- motor-driven electric vehicles based on sideslip angle estimation using lateral tire force sensors,

    K. Nam, H. Fujimoto, and Y . Hori, “Lateral stability control of in-wheel- motor-driven electric vehicles based on sideslip angle estimation using lateral tire force sensors,” IEEE Transactions on Vehicular Technology, vol. 61, no. 5, pp. 1972–1985, 2012

    1972

  22. [30]

    Estimation of vertical, lateral, and longitudinal tire forces in four-wheel vehicles using a delayed interconnected cascade-observer structure,

    R. A. Cordeiro, A. C. Victorino, J. R. Azinheira, P. A. V . Ferreira, E. C. de Paiva, and S. S. Bueno, “Estimation of vertical, lateral, and longitudinal tire forces in four-wheel vehicles using a delayed interconnected cascade-observer structure,”IEEE/ASME Transactions on Mec...

    2019

  23. [31]

    Gain-scheduled LPV/H ∞ strategy for steering and braking coordination of intelligent commercial vehicle lateral automation,

    Y . Sun, J. Song, H. He, T. Xu, and X. Ji, “Gain-scheduled LPV/H ∞ strategy for steering and braking coordination of intelligent commercial vehicle lateral automation,” IEEE Trans. Intell. Veh., 2024

    2024

  24. [32]

    A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions,

    E. Schulz, M. Speekenbrink, and A. Krause, “A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions,” J. Math. Psychol., vol. 85, pp. 1–16, 2018. Jiarui Song received the B.S. degree a from the Beijing Institute of Technology, Beijing, China, in

    2018

  25. [2019]

    degree in mechanical engineering from School of Vehicle and Mobility, Tsinghua University, Beijing, China

    She is currently working towards the Ph.D. degree in mechanical engineering from School of Vehicle and Mobility, Tsinghua University, Beijing, China. Her research interests include a dynamic control, autonomous driving, and intelligent transportation systems. Xuewu Ji (Member,...

    1987

  26. [2020]

    degree in mechanical engineering with the Beijing Institute of Technology, Beijing, China, in 2023

    He received the M.S. degree in mechanical engineering with the Beijing Institute of Technology, Beijing, China, in 2023. He is currently working towards the Ph.D. degree in mechanical engineering from School of Vehicle and Mobility, Tsinghua University, Beijing, China. His res...

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.