Simulation and experiments on preview tracking controller for intelligent vehicles based on hierarchical and refined estimation of multi-heterogeneous disturbances

Abstract

The path tracking control system of intelligent vehicles is affected by multiple heterogeneous disturbances, including modeling inaccuracies, network delays, parameter perturbations, and external environmental factors. Addressing these influences is critical to achieving precise tracking. This paper proposed a new robust preview control framework with hierarchical and refined disturbance rejection, which integrates linear quadratic regulator, hierarchical and refined estimation of disturbances, and adaptive preview model into a control system with efficient disturbance rejection. Firstly, an adaptive preview model with advanced predictability is designed to obtain the states of the future target “road,” effectively handling the unavoidable network delays. On this basis, a preview error dynamic model is established considering multi-heterogeneous disturbances. According to the heterogeneity of the disturbances, the multiple disturbances can be refined into two parts: the known external time-varying disturbances caused by road curvature and the unknown disturbances caused by other factors. These are estimated using a preview forward feedback controller (PFFC) and a linear extended state observer (LESO), respectively. After that, the path tracking problem is designed as an optimal controller based on a linear quadratic regulator (LQR) to obtain the steering wheel angle with safety constraints to track the desired path. The asymptotic stability of the closed-loop system is analyzed through the Lyapunov theorem. Finally, the proposed controller is verified and compared by the Carsim-Simulink simulation platform preliminarily, and real-vehicle experiments are implemented on an intelligent vehicle platform. Simulation and real-vehicle experimental results show excellent control performance.

Keywords

Intelligent vehicles multi-heterogeneous disturbances preview path tracking linear quadratic regulator hierarchical and refined disturbance rejection

Get full access to this article

View all access options for this article.

References

Liang

Khajepour

, et al. A novel combined decision and control scheme for autonomous vehicle in structured road based on adaptive model predictive control. IEEE Trans Intell Transp Syst 2022; 23(9): 16083–16097.

Yang

, et al. Robust lane change decision making for autonomous vehicles: an observation adversarial reinforcement learning approach. IEEE Trans Intell Veh 2023; 8(1): 184–193.

Mozaffari

Al-Jarrah

Dianati

, et al. Deep learning-based vehicle behavior prediction for autonomous driving applications: a review. IEEE Trans Intell Transp Syst 2022; 23(1): 33–47.

Liu

Zhong

, et al. Computing systems for autonomous driving: state of the art and challenges. IEEE Internet Things J 2021; 8(8): 6469–6486.

Shi

Zhang

Road-curvature-range-dependent path following controller design for autonomous ground vehicles subject to stochastic delays. IEEE Trans Intell Transp Syst 2022; 23(10): 17440–17450.

Peng

Design, analysis, and experiments of preview path tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst 2020; 21(1): 48–58.

Peng

Tang

Preview path tracking control with delay compensation for autonomous vehicles. IEEE Trans Intell Transp Syst 2021; 22(5): 2979–2989.

Wang

Zhang

Yang

, et al. Adaptive model predictive control-based path following control for four-wheel independent drive automated vehicles. IEEE Trans Intell Transp Syst 2022; 23(9): 14399–14412.

Zhang

Sun

Shi

Trajectory tracking control of autonomous ground vehicles using adaptive learning MPC. IEEE Trans Neural Netw Learn Syst 2021; 32(12): 5554–5564.

10.

Cheng

Chen

, et al. Model-predictive-control-based path tracking controller of autonomous vehicle considering parametric uncertainties and velocity-varying. IEEE Trans Ind Electron 2021; 68(9): 8698–8707.

11.

Guo

Guan

, et al. Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances. Meas Sci Technol 2024; 35(10): 106202.

12.

Wang

Zhang

, et al. Path following control of autonomous ground vehicle based on nonsingular terminal sliding mode and active disturbance rejection control. IEEE Trans Veh Technol 2019; 68(7): 6379–6390.

13.

Guo

Guan

Jiang

, et al. Nonsingular terminal sliding mode controller-based path tracking control for autonomous vehicles considering multiple-uncertainties disturbances. Proc IMechE, Part D: J Automobile Engineering. Epub ahead of print 26 September 2024. DOI: 10.1177/09544070241272919.

14.

Corno

Panzani

Roselli

, et al. An LPV approach to autonomous vehicle path tracking in the presence of steering actuation nonlinearities. IEEE Trans Control Syst Technol 2021; 29(4): 1766–1774.

15.

Chen

Wang

Fuzzy observer-based transitional path-tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst 2021; 22(5): 3078–3088.

16.

Zhang

Qiu

, et al. A novel fuzzy observer-based steering control approach for path tracking in autonomous vehicles. IEEE Trans Fuzzy Syst 2019; 27(2): 278–290.

17.

Nguyen

Rath

Guerra

, et al. Robust set-invariance based fuzzy output tracking control for vehicle autonomous driving under uncertain lateral forces and steering constraints. IEEE Trans Intell Transp Syst 2021; 22(9): 5849–5860.

18.

Huang

Yuan

Shi

, et al. Adaptivity-enhanced path tracking system for autonomous vehicles at high speeds. IEEE Trans Intell Veh 2020; 5(4): 626–634.

19.

Yuan

Huang

Shi

Improved adaptive path following control system for autonomous vehicle in different velocities. IEEE Trans Intell Transp Syst 2020; 21(8): 3247–3256.

20.

Mohammadzadeh

Taghavifar

Zhang

, et al. A non-linear fractional-order type-3 fuzzy control for enhanced path-tracking performance of autonomous cars. IET Control Theory Appl 2024; 18: 40–54.

21.

Liu

Huang

, et al. Multi-kernel online reinforcement learning for path tracking control of intelligent vehicles. IEEE Trans Syst Man Cybern Syst 2021; 51(11): 6962–6975.

22.

Liu

Zhao

Yin

, et al. Reinforcement-tracking: an effective trajectory tracking and navigation method for autonomous urban driving. IEEE Trans Intell Transp Syst 2022; 23(7): 6991–7007.

23.

Tian

Wang

, et al. Parallel learning-based steering control for autonomous driving. IEEE Trans Intell Veh 2023; 8(1): 379–389.

24.

Xiao

Zhang

, et al. Deep neural networks with Koopman operators for modeling and control of autonomous vehicles. IEEE Trans Intell Veh 2023; 8(1): 135–146.

25.

Basile

Leccese

Petrillo

, et al. Sustainable DDPG-based path tracking for connected autonomous electric vehicles in extra-urban scenarios. IEEE Trans Ind Appl 2024; 71(10): 12973–12982.

26.

Sun

Xue

, et al. Quantitative tuning of active disturbance rejection controller for FOPTD model with application to power plant control. IEEE Trans Ind Electron 2022; 69(1): 805–815.

27.

Wei

Zhang

Zuo

Phase leading active disturbance rejection control for a nanopositioning stage. ISA Trans 2021; 116: 218–231.

28.

Huang

Output feedback stabilization of uncertain nonholonomic systems with external disturbances via active disturbance rejection control. ISA Trans 2020; 104: 245–254.

29.

Song

Xue

, et al. Active disturbance rejection decoupling control for nonlinear MIMO uncertain systems with application to path following of self-driving bus. Control Eng Pract 2023; 133: 105432.

30.

Han

From PID to active disturbance rejection control. IEEE Trans Ind Electron 2009; 56(3): 900–906.

31.

Gao

Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the American control conference, Denver, CO, USA, 4–6 June 2003, pp.4989–4996. New York: IEEE.

32.

Gao

Huang

Connecting theory and practice with ADRC. Control Theory Technol 2023; 21: 1–3.

33.

Han

Nie

, et al. Cooperative decision-making of connected and autonomous vehicles in an emergency. IEEE Trans Veh Technol 2023; 72(2): 1464–1477.

34.

Guan

Ren

Sun

, et al. Integrated decision and control: toward interpretable and computationally efficient driving intelligence. IEEE Trans Cybern 2023; 53(2): 859–873.

35.

Yang

Huang

Yin

, et al. Path tracking control for underactuated vehicles with matched-mismatched uncertainties: an uncertainty decomposition: based constraint-following approach. IEEE Trans Intell Transp Syst 2022; 23(8): 12894–12907.

36.

Shan

Benderius

, et al. Formally robust and safe trajectory planning and tracking for autonomous vehicles. IEEE Trans Intell Transp Syst 2022; 23(12): 22971–22987.

37.

Xia

Fan

, et al. Lateral path tracking control of autonomous land vehicle based on ADRC and differential flatness. IEEE Trans Ind Electron 2016; 63(5): 3091–3099.

38.

Guan

Liao

Wang

, et al. Path tracking control of intelligent vehicles via a speed-adaptive MPC for a curved lane with varying curvature. Proc IMechE, Part D: J Automobile Engineering 2024; 238(4): 802–824.