Control of a tractor-trailer robot subjected to wheel slip

Abstract

Tractor-trailer wheeled robots (TTWRs) are highly nonlinear and underactuated dynamical systems. It is necessary to use nonlinear control methods, for the control of wheeled robots. Back-stepping method is a Lyapunov-based systematic technique for designing nonlinear control algorithms. In this paper, an adaptive back-stepping controller is proposed for the TTWRs. The proposed algorithm uses an adaptive layout for the compensation of the system wheel slips, which updates controller parameters based on a combination of error signals and estimated uncertainties. This paper is one of the firsts to propose a control algorithm for off-axle TTWRs in the presence of wheel slips. The control algorithm is designed to track the reference trajectories and make the robot asymptotically stable around the reference trajectories. The stability of the method is proved using Lyapunov theory. In order to compensate the sliding of wheels as the system uncertainties, appropriate adaptive rules have been investigated. Obtained results demonstrate the efficiency of the proposed method. The results for the tracking control of the TTWR in the presence of wheel slips show that the slip effects are effectively compensated using the proposed adaptive back-stepping control algorithm.

Control of a group of uncertain wheeled robots with global inputs in the presence of obstacles

“…23 Using this property and the scriptS T false( q false) product from the left side can remove the term containing Lagrange multipliers in the system dynamic equation ( scriptA T false( q false) λ) and leads to where Finally, the system torque vector for a group of WMRs can be written as: where These mathematical models are used in previous references such as Keymasi Khalaji, 24 Keymasi Khalaji and Moosavian 25 and Keymasi Khalaji and Jalalnezhad. 26…”

Robust control strategy for generalized N-trailer vehicles based on a dual-stage disturbance observer

“…Just few works have been addressing a general control solution for truly N-Trailers Micha lek and Kie lczewski (2014); Michalek (2017); Lashkari et al (2018); Keymasi Khalaji (2019). In the robustness sense, the works Keymasi Khalaji (2019); Lashkari et al (2017); Micha lek and Kie lczewski (2017) presented interesting solutions for SNT and nSNT to compensate the effects of parametric uncertainties in the model while the approches in Keymasi Khalaji (2019); Alipour et al (2019); Keymasi Khalaji and Jalalnezhad (2019) addressed the slipping of the wheels problem. However, the works Alipour et al (2019); Keymasi Khalaji and Jalalnezhad (2019) were designed just for nSNT and validated only in simulations, while Keymasi Khalaji (2019); Lashkari et al (2017); Micha lek and Kie lczewski (2017) were tested on laboratory-scale vehicles under ideal conditions.…”

“…In the robustness sense, the works Keymasi Khalaji (2019); Lashkari et al (2017); Micha lek and Kie lczewski (2017) presented interesting solutions for SNT and nSNT to compensate the effects of parametric uncertainties in the model while the approches in Keymasi Khalaji (2019); Alipour et al (2019); Keymasi Khalaji and Jalalnezhad (2019) addressed the slipping of the wheels problem. However, the works Alipour et al (2019); Keymasi Khalaji and Jalalnezhad (2019) were designed just for nSNT and validated only in simulations, while Keymasi Khalaji (2019); Lashkari et al (2017); Micha lek and Kie lczewski (2017) were tested on laboratory-scale vehicles under ideal conditions. For implementation in field conditions, feedback errors due to noisy or inaccurate sensor readings are important issues to be solved Myoungkuk et al (2005).…”

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

“…By integrating the polynomial function and a model predictive controller, a trajectory planning obstacle avoidance controller has been proposed in [18]. In [19], a non‐linear state feedback controller was proposed for TTWMRs by using the adaptive back‐stepping method. A tracking controller was designed in [20] by considering the friction influence.…”

“…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. …”