Minimum Swept-Path Control for Autonomous Reversing of a Tractor Semi-Trailer

Abstract

This paper presents a new approach for autonomous reversing of a tractor-semitrailer vehicle called minimum swept path control (MSPC). MSPC improves the performance of previous path following control (PFC) strategies, by minimizing the maximum excursion of the vehicle, while guaranteeing acceptable path following accuracy. A linear controller is devised, combining state feedback control with an optimized preview distance. A relationship between the maximum lateral offsets of the front axle of the tractor unit and the rear axle of the last trailer and the corresponding weights in the control cost function is established, and the controller is tuned to provide the best performance compromise. Several simulations of a tractor-semitrailer case are run, and the results show that the overall swept path width is reduced by more than 40% when compared with PFC methods.

Simulator Kontrol Manual Dan Swa-tala Untuk Dehydration Glycol Unit Berbasis JavaScript

“…Rasio penyaluran hibah traktor oleh pihak pemerintahjuga terus mengalami peningkatan hingga mencapai kisaran 263% untuk roda dua dan 100% untuk roda empat pada tahun 2015 [3]. Menurut [4] traktor otonom membutuhkan keakuratan menyetir selama beroperasi untuk mengikuti lintasan agar dapat mengolah lahan dengan efektif.Akan tetapisebagian besar pengembangan traktor otonom adalah untuk pertanian skala besar yaitu traktor empat roda [5,6,7,8,9,10]. Sedangkan mayoritas penggunaan traktor di Indonesiaadalah traktor roda dua atau traktor roda differential [3].Oleh karena itu, pada penelitian ini akan berfokus pada pengembangan traktor otonom roda differential atau otomatisasi traktor tangan.…”

Research on Trajectory Tracking Control of a Semi-Trailer Train Based on Differential Braking

“…Automated driving technology mainly includes four significant parts: perception, positioning [2], planning, and control, of which the trajectory tracking control module is to solve the problem of how the vehicle travels by the planned path, which is the critical link to realizing automatic driving of the car [3]. Compared with the general single car, a semi-trailer train has a large turning radius and is difficult to pass in the narrow channel [4]. When driving at high speed, it is likely to occur side-slip, tailing, folding, and other dangerous conditions, increasing the difficulty of semi-trailer train trajectory tracking control.…”

Observer‐based neural adaptive control of a platoon of autonomous tractor–trailer vehicles with uncertain dynamics

“…A tracking controller was designed in [20] by considering the friction influence. A minimum swept path control design was discussed for TTWMRs in [21] and a robust path following control design was studied in [22]. From a deep literature review, the platoon formation control of TTWMRs has not been paid attention in the literature at all and it is the main topic of this study.…”

“…Then, a saturated filtered error variable is defined to design our controller while bounding the unconstrained errors as well. After a deep literature review including [1–22], the main novelties of the proposed controller are listed as follows: (1) This is the first attempt to design a tracking controller for a convoy‐like motion of multiple off‐axle hitching tractor–trailers. (2) The proposed controller does not require the velocity and acceleration measurements in real‐time. (3) The actuator saturation risk is reduced by considering a hyperbolic tangent function and projection‐type neural adaptive rules. (4) The collisions between successive tractor–trailers are avoided by constraining the relative distance error. (5) The consecutive vehicles' connectivity is ensured by considering the limited communication range of vehicle transmitter–receivers. (6) The possible singularity of the tracking controller is avoided by constraining the relative angle error. (7) The desired prescribed transient and steady‐state performance criteria are guaranteed in advance. (8) All types of TTWMR model uncertainties are compensated by neural adaptive robust techniques. …”