A modified predictive PID controller for dynamic positioning of vessels with autoregressive model

Ye, Yutu; Wang, Yiting; Wang, Lei; Wang, Xuefeng

doi:10.1016/j.oceaneng.2023.115176

Cited by 3 publications

(1 citation statement)

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“…In the field of trajectory tracking control, commonly used control methods include classical PID control [20], linear-quadratic regulator (LQR) methods [21], back stepping, adaptive control, robust control, the model predictive control (MPC) method, etc. PID and LQR control are effective and mature algorithms, but they are generally difficult to be applied to complex working conditions; both cannot exhibit high robustness [22,23] because the classical PID controller operates on the principle of "using error feedback to eliminate errors". However, since the system output exhibits some inertia, it cannot undergo sudden changes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

Feng,

Shen,

Wang

et al. 2024

Machines

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In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system model. Based on the non-singular terminal sliding mode and adaptive control theory, this paper establishes a trajectory tracking control strategy for an autonomous vehicle with unknown parameters and unknown disturbances. Firstly, the complex trajectory tracking problem is decoupled from the position and heading angle tracking problem, and the preview error equation is established. Secondly, a non-singular terminal sliding mode (NTSM) controller is established to stabilize the trajectory tracking error to the origin in a finite time, and adaptive laws are proposed to estimate the unknown vehicle parameters to adapt to environmental changes. Through the CarSim–Matlab platform, typical working conditions are implemented to verify the proposed controller. Our experimental outcomes affirm that the NTSM controller effectively guarantees the autonomous vehicle’s accurate following of the reference path, ensuring smooth control inputs throughout the entire process.

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Section: Introductionmentioning

confidence: 99%