726 research outputs found
Integrating Vehicle Slip and Yaw in Overarching Multi-Tiered Automated Vehicle Steering Control to Balance Path Following Accuracy, Gracefulness, and Safety
Balancing path following accuracy and error convergence with graceful motion
in steering control is challenging due to the competing nature of these
requirements, especially across a range of operating speeds and conditions.
This paper demonstrates that an integrated multi-tiered steering controller
considering the impact of slip on kinematic control, dynamic control, and
steering actuator rate commands achieves accurate and graceful path following.
This work is founded on multi-tiered sideslip and yaw-based models, which allow
derivation of controllers considering error due to sideslip and the mapping
between steering commands and graceful lateral motion. Observer based sideslip
estimates are combined with heading error in the kinematic controller to
provide feedforward slip compensation. Path following error is compensated by a
continuous Variable Structure Controller (VSC) using speed-based path manifolds
to balance graceful motion and error convergence. Resulting yaw rate commands
are used by a backstepping dynamic controller to generate steering rate
commands. A High Gain Observer (HGO) estimates sideslip and yaw rate for output
feedback control. Stability analysis of the output feedback controller is
provided, and peaking is resolved. The work focuses on lateral control alone so
that the steering controller can be combined with other speed controllers.
Field results provide comparisons to related approaches demonstrating
gracefulness and accuracy in different complex scenarios with varied weather
conditions and perturbations
TS-MPC for autonomous vehicles Including a TS-MHE-UIO estimator
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno-Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno-Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi-Sugeno estimator-Moving Horizon Estimator-Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 10-20 times. To demonstrate the potential of the TS-MPC, we propose a comparison between three methods of solving the kinematic control problem: Using the nonlinear MPC formulation (NL-MPC) with compensated friction force, the TS-MPC approach with compensated friction force, and TS-MPC without compensated friction force.This work was supported by the Spanish Min-istry of Economy and Competitiveness (MINECO) and FEDER through theProjects SCAV (ref. DPI2017-88403-R) and HARCRICS (ref. DPI2014-58104-R). The corresponding author, Eugenio Alcalá, is supported under FI AGAURGrant (ref 2017 FI B00433).Peer ReviewedPostprint (author's final draft
Nonlinear adaptive filter design for integrated vehicle handling dynamics state estimation
This thesis considers nonlinear filter design for integrated vehicle handling dynamics state estimation. Such
a state estimator is needed as not all of the vehicle states can be measured directly by the existing sensors,
mostly due to reliability and economical reasons. Accurate information about vehicle handling states is
essential for vehicle chassis control and chassis design evaluation.
This study considers mathematical model-based filtering methods. A nonlinear 6DoF vehicle model
employing an intermediate tyre magic formula is developed for the filter basis. The main problem faced by
such a model-based filter is model uncertainties, especially in tyre parameters. The main objective of this
study is to design filters which are robust against model uncertainties. Two nonlinear filtering methods are
investigated: extended Kalman filter (EKF) and nonlinear robust filter (NRF). The EKF relies on accurate
nominal model and ideal white/time uncorrelated assumption about model error noises. In contrast, the
NRF tolerates inaccuracy of the nominal model as it accounts for the time-correlated behaviour of the
model errors more properly. [Continues.
Gain-scheduling LPV control for autonomous vehicles including friction force estimation and compensation mechanism
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This study presents a solution for the integrated longitudinal and lateral control problem of urban autonomousvehicles. It is based on a gain-scheduling linear parameter-varying (LPV) control approach combined with the use of anUnknown Input Observer (UIO) for estimating the vehicle states and friction force. Two gain-scheduling LPV controllers are usedin cascade configuration that use the kinematic and dynamic vehicle models and the friction and observed states provided bythe Unknown Input Observer (UIO). The LPV–UIO is designed in an optimal manner by solving a set of linear matrix inequalities(LMIs). On the other hand, the design of the kinematic and dynamic controllers lead to solve separately two LPV–LinearQuadratic Regulator problems formulated also in LMI form. The UIO allows to improve the control response in disturbanceaffected scenarios by estimating and compensating the friction force. The proposed scheme has been integrated with atrajectory generation module and tested in a simulated scenario. A comparative study is also presented considering the casesthat the friction force estimation is used or not to show its usefulnessPeer ReviewedPostprint (author's final draft
Kinematic control design for wheeled mobile robots with longitudinal and lateral slip
The motion control of wheeled mobile robots at high speeds under adverse
ground conditions is a difficult task, since the robots' wheels may be subject
to different kinds of slip. This work introduces an adaptive kinematic
controller that is capable of solving the trajectory tracking problem of a
nonholonomic mobile robot under longitudinal and lateral slip. While the
controller can effectively compensate for the longitudinal slip, the lateral
slip is a more involved problem to deal with, since nonholonomic robots cannot
directly produce movement in the lateral direction. To show that the proposed
controller is still able to make the mobile robot follow a reference trajectory
under lateral and longitudinal time-varying slip, the solutions of the robot's
position and orientation error dynamics are shown to be uniformly ultimately
bounded. Numerical simulations are presented to illustrate the robot's
performance using the proposed adaptive control law
A path planning and path-following control framework for a general 2-trailer with a car-like tractor
Maneuvering a general 2-trailer with a car-like tractor in backward motion is
a task that requires significant skill to master and is unarguably one of the
most complicated tasks a truck driver has to perform. This paper presents a
path planning and path-following control solution that can be used to
automatically plan and execute difficult parking and obstacle avoidance
maneuvers by combining backward and forward motion. A lattice-based path
planning framework is developed in order to generate kinematically feasible and
collision-free paths and a path-following controller is designed to stabilize
the lateral and angular path-following error states during path execution. To
estimate the vehicle state needed for control, a nonlinear observer is
developed which only utilizes information from sensors that are mounted on the
car-like tractor, making the system independent of additional trailer sensors.
The proposed path planning and path-following control framework is implemented
on a full-scale test vehicle and results from simulations and real-world
experiments are presented.Comment: Preprin
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