Constrained Fractional-Order Model Predictive Control for Robust Path Following of FWID-AGVs With Asymptotic Prescribed Performance

This paper proposes a prescribed performance function (PPF)-based constrained fractional-order model predictive controller (CFMPC) for the robust path following of the four-wheel-independent-drive autonomous ground vehicle (FWID-AGV) subject to the model mismatch and external disturbances. First, a...

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Vydáno v:IEEE transactions on vehicular technology Ročník 74; číslo 2; s. 2692 - 2705
Hlavní autoři: Zhao, Jing, Li, Renbin, Zheng, Xinyang, Li, Wenfeng, Hu, Chuan, Liang, Zhongchao, Wong, Pak Kin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Asymptotic methods
Asymptotic properties
Autonomous ground vehicle
Closed loops
Computational modeling
Constraints
Controllers
Cost function
Disturbances
fractional-order
Land vehicles
Liapunov functions
model predictive control
Neural networks
Optimal control
path following
Predictive control
Predictive models
prescribed performance control
Recursive functions
Robust control
Robustness
Steady-state
Tires
Trajectory planning
Unmanned ground vehicles
Vehicle dynamics
Wheels
ISSN:0018-9545, 1939-9359
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Shrnutí:This paper proposes a prescribed performance function (PPF)-based constrained fractional-order model predictive controller (CFMPC) for the robust path following of the four-wheel-independent-drive autonomous ground vehicle (FWID-AGV) subject to the model mismatch and external disturbances. First, a PPF-based model predictive controller (MPC) is developed to guarantee the transient-state performance by dealing with the model mismatch. Second, a contraction constraint of the MPC is constructed with an adaptive neural network backstepping method, aiming to ensure the optimal control inputs fall into the region of attraction. Third, a fractional-order cost function of the MPC is discretized to reject the external disturbances and improve the robustness of the controller. Besides, a sufficient condition is given to prove the recursive feasibility of the PPF-based CFMPC, and a Lyapunov function candidate is derived for the asymptotic stability of the closed-loop path following system. The effectiveness and practicability of the proposed method are validated by hardware-in-the-loop tests, respectively.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3476921