Control Method Designs and Comparisons for Tractor-Trailer Vehicle Backward Path Tracking

Jing, Junbo; Maroli, John M.; Salamah, Yasser Bin; Hejase, Mohammad; Fiorentini, Lisa; Özgüner, Ü.

doi:10.23919/acc.2019.8815188

Cited by 10 publications

(22 citation statements)

References 24 publications

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“…Since a tractor-trailer vehicle consists of (NV+1) rigid bodies, we plan a coarse path for each body independently. the initial and terminal locations of the center, denoted as ( , ) ii xc yc , can be easily identified as per ( 4), (8), and (9). A* algorithm is deployed to search for a sequence of nodes connecting the initial and terminal locations in the grid map.…”

Section: Generation Of Coarse Pathsmentioning

confidence: 99%

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Acarman

et al. 2022

IEEE Robot. Autom. Lett.

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This study is focused on the maneuver planning problem for a tractor-trailer vehicle in a curvy and tiny tunnel. Due to the curse of dimensionality, the prevalent sampling-andsearch-based planners used to handle a rigid-body vehicle well become less efficient when the trailer number grows or when the tunnel narrows. This fact also has impacts on an optimizationbased planner if it counts on a sampling-and-search-based initial guess to warm-start. We propose an optimization-based maneuver planner that weakly relies on the sampling and search, hoping to get rid of the curse of dimensionality and thus find optima rapidly. The proposed planner comprises three stages: stage 1 identifies the homotopy class via A* search in a 2D grid map; stage 2 recovers the kinematic feasibility with softened intermediate problems iteratively solved; stage 3 finds an optimum that strictly satisfies the nominal collision-avoidance constraints. Optimization-based planners are commonly known to run slowly, but this work shows that they have obvious advantages over the prevalent sampling-and-search-based planners when the solution space dimension is high and/or the constraints are harsh.

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Section: Generation Of Coarse Pathsmentioning

confidence: 99%

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Acarman

et al. 2022

IEEE Robot. Autom. Lett.

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“…In addition, the constraint on the longitudinal acceleration is introduced. Since the proposed MPC is based on the discrete model, the inequality constraint on acceleration is defined as (31). In (31), a x,min and a x,max are the minimum and maximum accelerations, which are derived to provide the smooth operation of the AERS, respectively.…”

Section: Mpc-based Motion Controllermentioning

confidence: 99%

“…An algorithm for articulated vehicle stability was proposed that determines the target yaw moment with linear Model Predictive Control (MPC) and distributes control effort to brake actuators through another optimization [29]. In contrast to tractor-trailers, the AERS can control the articulation angle and has non-steerable wheels [30][31][32]. Therefore, it is difficult to utilize a controller based on FWS.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Based Path Tracking and Velocity Control with Rollover Prevention Function for Autonomous Electric Road Sweeper

2022

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This paper presents a model predictive control (MPC)-based algorithm for rollover prevention of an autonomous electric road sweeper (AERS). For AERS, the basic function of autonomous driving is a path- and velocity-tracking control needed to make a vehicle follow given path and velocity profiles. On the other, the AERS adopts an articulated frame steering (AFS) mechanism which can make cornering behavior agile. Moreover, the tread of the AERS is narrow, and the height of the mass center is high. As a result, it is prone to roll over. For this reason, it is necessary to design a controller for path and velocity tracking and rollover prevention in order to improve maneuverability and roll safety of the AERS. A kinematic model was adopted as a vehicle one for the AERS. With the vehicle model, reference states of position and velocity were determined that are needed to make the AERS track the reference path and prevent rollover. With the vehicle model and reference states, an MPC-based motion controller was designed to optimize articulation angle and velocity commands. The load-transfer ratio (LTR) was used to measure a rollover propensity. To evaluate the proposed algorithm, a simulation was conducted for the U-turn scenario. Simulation results show that the proposed algorithm improves path tracking and prevents the rollover of the AERS.

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“…e number of collocations, to which the polynomial order, P, is increased can be obtained from (26) and (27). If σ < σ, it indicates that the error convergence is slow, and the time domain needs to be redivided.…”

Section: Hp-adaptive Update Algorithmmentioning

confidence: 99%

“…Liu et al proposed an adaptive control algorithm for path tracking control by considering time delays [25]. Jing et al designed three different controllers for the reverse motion of a tractor-trailer to solve the tractor-trailer path tracking problem for backwards motion [26]. Zhang et al designed a novel path tracking controller for ground vehicles [27].…”

Section: Introductionmentioning

confidence: 99%

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

Liu

Sun

Cui

2021

Mathematical Problems in Engineering

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We propose a vehicle path-tracking method based on the hp-adaptive Gaussian pseudospectral method (GPM), which tackles the problem of the slow convergence speed of the optimal control of vehicle path tracking. First, we establish a kinematic vehicle model by considering the path constraints and boundary constraints during the process of tracking the described path of a vehicle. Subsequently, finding the minimum error of the lateral distance between the prescribed path and the expected trajectory is set as the performance objective function. Finally, the vehicle path tracking problem is transformed into an optimal control problem. The optimization algorithm is combined with the sequential quadratic programming algorithm to optimize problems related to the control and state variables and the boundary and path constraints. The simulated results show that the hp-adaptive GPM can improve the convergence rate of the optimal control problem for vehicle path tracking. The proposed hp-adaptive pseudospectral method has a higher solving efficiency compared with traditional approaches for the vehicle path-tracking problem. Concurrently, the verification results of a real vehicle test indicate the feasibility of the proposed algorithm for solving the vehicle path tracking problem. This research provides valuable insight into the design work of lane changes and is an important step towards the design of feedback control laws for path tracking.

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Control Method Designs and Comparisons for Tractor-Trailer Vehicle Backward Path Tracking

Cited by 10 publications

References 24 publications

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Model Predictive Control Based Path Tracking and Velocity Control with Rollover Prevention Function for Autonomous Electric Road Sweeper

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

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