Model predictive control (MPC) has become more
relevant to vehicle dynamics control due to its inherent capacity
of treating system constraints. However, online optimization
from MPC introduces an extensive computational burden for
today’s onboard microprocessors. To alleviate MPC
computational load, several methods have been proposed.
Among them, online successive system linearization and the
resulting linear time-varying model predictive controller
(LTVMPC) is one of the most popular options. Nevertheless,
such online successive linearization commonly approximates the
original (nonlinear) system by a linear one, which inevitably
introduces extra modeling errors and therefore reduces MPC
performance. Actually, if the controlled system possesses the
“differential flatness” property, then it can be exactly linearized
and an equivalent linear model will appear. This linear model
maintains all the nonlinear features of the original system and
can be utilized to design a flatness-based model predictive
controller (FMPC). CarSim-Simulink joint simulations
demonstrate that the proposed FMPC substantially outperforms
a classical LTVMPC in terms of the path-tracking performance
for autonomous vehicles.
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Vehicle Path-Tracking Linear-Time-Varying Model Predictive Control Controller Parameter Selection Considering Central Process Unit Computational Load
Model predictive control (MPC) has drawn a considerable amount of attention in automotive applications during the last decade, partially due to its systematic capacity of treating system constraints. Even though having received broad acknowledgements, there still exist two intrinsic shortcomings on this optimization-based control strategy, namely the extensive online calculation burden and the complex tuning process, which hinder MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed. Nevertheless, the majority of these approaches treat these two issues independently. However, parameter tuning in fact has double-sided effects on both the controller performance and the real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization, and computational burden easement, a look-up table-based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. matlab-carsim conjoint simulations show the effectiveness of the proposed strategy.
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- Award ID(s):
- 1901632
- NSF-PAR ID:
- 10113096
- Date Published:
- Journal Name:
- Journal of Dynamic Systems, Measurement, and Control
- Volume:
- 141
- Issue:
- 5
- ISSN:
- 0022-0434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4042196
