Robust sideslip angles observer for accurate off-road path tracking control

Lenain, Roland; Deremetz, Mathieu; Braconnier, Jean-Baptiste; Thuilot, Benoı̂t; Rousseau, Vincent

doi:10.1080/01691864.2017.1280414

Cited by 22 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To ensure a suitable convergence of the tracking and angular errors along the trajectory when facing poor grip conditions, it is compulsory to know accurately the values of the sideslip angles. Because there is no direct perception system allowing to measure such variables, it has been shown in [10] that an observer may be built to estimate the two sideslip angles with a satisfying accuracy. To achieve this estimation, the robot must be equipped with on-board sensors that permit to measure the position, the orientation and the velocity of the robot at a given point, here R. Moreover, according to the model (1), defined above, the velocity of the robot at F , has to be measured.…”

Section: Estimation Of Sideslip Anglesmentioning

confidence: 99%

“…In this paper, the proposed observer in [10] built for a two-wheel steering mobile robot is extended to a four-wheel steering one, according to the equations of the model (1). Because this approach is a version close to [10], only a short presentation is proposed in this section.…”

Section: A Observer Statementioning

confidence: 99%

“…As shown in [10] and extended to a four-wheel steering mobile robot, the observer defined by (5) and (6) allows the convergence of the whole observed stateξ to the actual one ξ. As a consequence, the values for the sideslip angles,β F andβ R are obtained and can feed a control law appropriately in order to follow a path while keeping the same absolute orientation.…”

Section: B Observer Equationsmentioning

confidence: 99%

“…with k θ a negative scalar defining the settling time for the exponential convergence of θ to θ d imposed by (10).…”

Section: Control Algorithm For the Front Steering Anglesmentioning

confidence: 99%

“…More precisely, a backstepping approach has been developed in order to compute the front steering angles and the rear steering angles based on an extended kinematic model. To compensate for the effects of the grip conditions, an absolute observer, based on prior work [10], estimates the sideslip angles and feeds the proposed control laws in terms of sliding.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Path tracking of a four-wheel steering mobile robot: A robust off-road parallel steering strategy

Deremetz¹,

Lenain²,

Couvent³

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

Self Cite

View full text Add to dashboard Cite

In this paper, the problem associated with accurate control of a four-wheel steering mobile robot following a path, while keeping different desired absolute orientations and ensuring different desired lateral deviations, is addressed thanks to a backstepping control strategy. In particular, the control of each steering angle is investigated through a new parallel steering approach based on an extended kinematic model of a bicycle-model robot assuming that the two front steering angles are equal and likewise for the two rear ones. Two control laws are then proposed to ensure a suitable path following according to orientation and position conditions. In order to balance the lateral effects, notably the sideslip angles, an observer has been used to estimate the sliding. This estimation permits to feed the proposed control laws appropriately, enabling an accurate path tracking and orientation keeping along the trajectory. This new point of view permits to achieve difficult manoeuvres in narrow environments such as a parallel parking or sharp turns. Previous approaches have focused on the control of four-wheel steering mobile robots with respect to the trajectory but do not combine path following with independent heading angle control and slippery conditions.

show abstract

Section: Estimation Of Sideslip Anglesmentioning

confidence: 99%

Section: A Observer Statementioning

confidence: 99%

Section: B Observer Equationsmentioning

confidence: 99%

“…with k θ a negative scalar defining the settling time for the exponential convergence of θ to θ d imposed by (10).…”

Section: Control Algorithm For the Front Steering Anglesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Path tracking of a four-wheel steering mobile robot: A robust off-road parallel steering strategy

Deremetz¹,

Lenain²,

Couvent³

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

Self Cite

View full text Add to dashboard Cite

show abstract

Data learning‐based model‐free adaptive control and application to an NAO robot

Liu

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, an improved model-free adaptive control method based on the controller dynamic linearization technique combined with the locally weighted regression-based lazy learning method (cMFAC-LL) is presented, and it is applied to solve the path-tracking control problem for an NAO robot. In the proposed cMFAC-LL method, two dynamic linearization techniques are first applied on the controlled plant, and then the cMFAC-LL controller is further designed with the time-varying parameters estimated using a novel locally weighted regression-based lazy learning (LWR-LL) technique. The greatest advantage of the cMFAC-LL method is that it is a pure data-driven control method, and the designed controller makes full use of both the online and offline measurement data of the plant. Moreover, the introduction of the local learning (LL) method gives the cMFAC-LL method strong data learning ability and satisfactory control performance. The stability of cMFAC-LL is proven via rigorous mathematical analysis. Furthermore, cMFAC-LL is applied to the path-tracking control of the NAO robot under different walking environments, by which the applicability of cMFAC-LL is further verified.

show abstract