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Research Papers

Vehicle Path-Tracking Linear-Time-Varying Model Predictive Control Controller Parameter Selection Considering Central Process Unit Computational Load

[+] Author and Article Information
Zejiang Wang

Walker Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: wangzejiang@utexas.edu

Yunhao Bai

Department of Electrical and
Computer Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: bai.228@osu.edu

Junmin Wang

Fellow ASME
Walker Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: jwang@austin.utexas.edu

Xiaorui Wang

Department of Electrical and
Computer Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: wang.3596@osu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 5, 2018; final manuscript received November 26, 2018; published online January 14, 2019. Assoc. Editor: Huiping Li.

J. Dyn. Sys., Meas., Control 141(5), 051004 (Jan 14, 2019) (12 pages) Paper No: DS-18-1271; doi: 10.1115/1.4042196 History: Received June 05, 2018; Revised November 26, 2018

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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Figures

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Fig. 1

Bounds on tire slip angle

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Fig. 2

Weight on slack variable

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Fig. 3

Path-tracking result

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Fig. 4

Front steering angle

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Fig. 5

Four tire sideslip angles

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Fig. 6

Hyperbolic tangent function

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Fig. 7

Computational load index

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Fig. 10

Computational feasibility rate

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Fig. 11

Confliction between stability and tracking indices

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Fig. 12

Computational load index

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Fig. 13

Constant and dynamic parameter setting

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Fig. 14

Integrated mean of the normalized stability margin

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Fig. 15

Comparisons of path-tracking results

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Fig. 16

Normalized minimum margin between vehicle body and path boundaries

Tables

Errata

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