The learning-based model predictive control (LB-MPC) is an effective and critical method to
solve the path tracking problem in mobile platforms under uncertain disturbances. It is well …

Many robotic applications, such as milling, gluing, or high precision measurements, require
the precise following of a predefined geometric path. We investigate the real-time feasible …

We consider the tracking of geometric paths in output spaces of nonlinear systems subject to
input and state constraints without pre-specified timing requirements, commonly referred to …

In this paper, a model predictive path following control (MPFC) for holonomic mobile robots
is considered. The MPFC is aimed to control a mobile robot to follow a geometric path …

In this paper, a new approach for lane change and double-lane change planning and
following for autonomous driving is proposed. Herein, we introduce a novel technique …

Within the scope of highly automated driving, steering a vehicle autonomously along a pre-
specified geometric reference path is an important control task. In this article we present an …

This study aims to propose an adaptive state-dependent gain finite-time convergent
controller (using the fundamentals of the sliding mode theory) that solves the trajectory …

This paper presents synchronous as well as asynchronous formulations of the alternating
direction method of multipliers (ADMM) for solving continuous‐time nonlinear distributed …

In many robotic applications, a predefined path must be followed while maintaining contact
with a surface or applying a given force. If size, position, or flexibility of the surface are not …

For redundant fixed or mobile manipulators in shared human-robot workspaces, control
algorithms are necessary that allow the robot to perform a task defined in the Cartesian …