A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions

Wang, Hongchao; Zhang, Wei Wei; Wu, Xuncheng; Cao, Haotian; Gao, Qiaoming; Luo, Suyun

doi:10.1631/fitee.1900185

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…In similar studies, Wang et al. [43] used a double‐layered control algorithm to solve the problem that contains a two‐layer non‐linear model predictive control (MPC).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, considering the non-linear objective and nonlinear constraints, this BMINLP is difficult to be solved using the classical heuristic algorithm. In similar studies, Wang et al [43] used a double-layered control algorithm to solve the problem that contains a two-layer non-linear model predictive control (MPC).…”

Section: Algorithm For the First-stage Model: Dn-gamentioning

confidence: 99%

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A two‐stage robust optimal traffic signal control with reversible lane for isolated intersections

Sun,

Wang,

et al. 2023

IET Intelligent Trans Sys

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The integrated design of traffic signal control (TSC) and reversible lane (RL) is an effective way to solve the problem of tidal congestion with uncertainty at isolated intersections, because of its advantage in making full use of temporal‐spatial transportation facilities. Considering the contradiction between the dynamic TSC scheme and the fixed RL scheme in one period, a two‐stage optimization method based on improved mean‐standard deviation (MSD) model for isolated intersections with historical and real‐time uncertain traffic flow is proposed. In the first stage, applying the same‐period historical data of multiple days, a robust optimal traffic signal control model with reversible lane based on MSD model (MSD‐RTR model) is put forward to obtain the fixed RL scheme and the compatible initial TSC scheme. A double‐layer nested genetic algorithm (DN‐GA) is designed to solve this model. In the second stage, applying real‐time period data and multi‐day same‐period historical data, a robust optimal dynamic traffic signal control model based on MSD model (MSD‐RDT model) is put forward to obtain the dynamic TSC scheme. Three modes which reflect the different weights of historical period and real‐time period in this MSD‐RDT model are presented to improve the model stability, and a multi‐mode genetic algorithm (MM‐GA) is designed. Finally, a case study is presented to demonstrate the efficiency and applicability of the proposed models and algorithms.

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“…In similar studies, Wang et al. [43] used a double‐layered control algorithm to solve the problem that contains a two‐layer non‐linear model predictive control (MPC).…”

Section: Methodsmentioning

confidence: 99%

Section: Algorithm For the First-stage Model: Dn-gamentioning

confidence: 99%

A two‐stage robust optimal traffic signal control with reversible lane for isolated intersections

Sun,

Wang,

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

Stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions

Nakahara

Sekiguchi

Nonaka

et al. 2022

Control Theory Technol.

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When heavy-duty commercial vehicles (HDCVs) must engage in emergency braking, uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control. To minimize these uncertainties, we propose a combined longitudinal and lateral controller method based on stochastic model predictive control (SMPC) that is achieved via Chebyshev–Cantelli inequality. In our method, SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements, including chance constraints. To accomplish this, SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy. The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects. More specifically, the use of Chebyshev–Cantelli inequality suppresses road profile influences, which have characteristics that are different from the Gaussian distribution, thereby improving both braking robustness and control performance against statistical disturbances. Additionally, since the Kalman filtering (KF) algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints, the SMPC is reformulated as a KF embedded deterministic MPC. Herein, the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.

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A recognition method of road adhesion coefficient based on Recurrent Neural Network

Jin-liang

Liang

Yang

et al. 2021

2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI)

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A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions

Cited by 4 publications

References 27 publications

A two‐stage robust optimal traffic signal control with reversible lane for isolated intersections

A two‐stage robust optimal traffic signal control with reversible lane for isolated intersections

Stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions

A recognition method of road adhesion coefficient based on Recurrent Neural Network

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