Multi‐model adaptive predictive control for path following of autonomous vehicles

Liang, Yixiao; Li, Yinong; Khajepour, Amir; Zheng, Ling

doi:10.1049/iet-its.2020.0357

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…The establishment of a vehicle kinematics model and a dynamics model is the basis for the analysis and research of the intelligent vehicle control system and the design of the controller [22]. A vehicle kinematics model is established based on the position of an intelligent vehicle in space and the current driving speed and other geometric variable changes over time.…”

Section: Establishing a Model Of The Vehiclementioning

confidence: 99%

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Shi

et al. 2022

IEEE Access

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In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

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Section: Establishing a Model Of The Vehiclementioning

confidence: 99%

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Shi

et al. 2022

IEEE Access

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“…The relative degree between the sliding surface and control input equals one, as illustrated by (18). Then the control law can be derived in a general rule.…”

Section: (18)mentioning

confidence: 99%

“…However, due to the negligence of the internal dynamics of the model, the geometric and kinematic-based controllers have some limitations for practical applications [13]. Therefore, the dynamic vehicle model-based controllers are mostly used in trajectory following controller design recently [10,11,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Robust Backstepping Super-Twisting Sliding Mode Control for Autonomous Vehicle Path Following

Ao¹,

Huang²,

Wong³

et al. 2021

IEEE Access

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This paper focuses on the path following control problems for autonomous driving vehicles. Aiming at enhancing the robustness and attenuating the chattering phenomenon, a super-twisting sliding mode control algorithm (STA) is developed based on Lyapunov theory, where the proof of the stability of the control system is presented by applying the backstepping technique. Moreover, co-simulation between Matlab/Simulink and Carsim is carried out to verify the path following control performance. In this research, Stanley controller, conventional sliding mode control (SMC), and model predictive control (MPC) are used as the benchmark controllers for evaluating the proposed STA performance. Two driving scenarios are considered in the simulations, including normal driving and fierce driving. To comprehensively assess the control performance and control effort (i.e. magnitude of steering), an integrated and weighted performance evaluation index is novelly provided. Simulation results show that the of the proposed STA can be reduced by 40.5%, 25.8%, 10.9% in the normal driving scenario; and 62.5%, 24%, 6.8% in the fierce driving scenario as compared with Stanley controller, conventional SMC, and MPC, respectively. The results also indicate that the proposed STA outperforms the conventional SMC in terms of the chattering attenuation, resulting in a smoother front steering wheel angle input and a smoother yaw rate performance. As compared with MPC, the advantage of the proposed STA lies in its much lower computational complexity. Furthermore, the robustness of the controllers is verified by changing the vehicle mass and tire parameters. The proposed STA can reduce the fluctuation of the by 22.6%, 22.3%, and 5.9% compared with the benchmark approaches. These results imply that the consideration of system perturbations is very critical in the design of the super-twisting sliding mode controller which can improve the robustness of the autonomous vehicle path following system. INDEX TERMSPath following control, super-twisting sliding mode, backstepping, robustness, perturbations.

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“…Numerous findings have been obtained by studying the control of nonlinear systems when using fuzzy systems, such as [1][2][3]. Adaptive control methods can automatically adjust estimated parameters based on real-time system feedback information to optimize system performance, as mentioned in [4,5]. Therefore, combining adaptive control with fuzzy logic systems can adapt to system changes and uncertainties, can enhance the robustness of the control system to uncertainties, and can manage complex nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy compensation disturbance observer-based adaptive tracking control for pure-feedback systems with deferred output constraint

Liu,

Zhang

2024

Preprint

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In this paper, the concentration is on a tracking control issue for a class of deferred time-varying output-constrained pure-feedback systems subject to unknown external disturbance. In the control scheme design procedure, the nonlinearities are approximated by fuzzy logic systems. To obtain a virtual term, the mean value theorem is applied to separate the non-affine component of pure-feedback systems. A fuzzy compensation disturbance observer is proposed to further weaken the impact of disturbances on the system. An auxiliary system is designed to ensure that the compensation term is related to the error between the ideal and actual signals. The shifting function and the fractional barrier function are combined to achieve deferred constraints on the system output. The proposed strategy can be used to determine the boundary parameters of the barrier function immediately and to avoid the need for numerous simulation iterations. Two simulations are performed to confirm the validity of the presented strategy.

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Multi‐model adaptive predictive control for path following of autonomous vehicles

Cited by 12 publications

References 28 publications

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Robust Backstepping Super-Twisting Sliding Mode Control for Autonomous Vehicle Path Following

Fuzzy compensation disturbance observer-based adaptive tracking control for pure-feedback systems with deferred output constraint

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