7 research outputs found
A model predictive controller for robots to follow a virtual leader
SUMMARYIn this paper, we develop a model predictive control (MPC) scheme for robots to follow a virtual leader. The stability of this control scheme is guaranteed by adding a terminal state penalty to the cost function and a terminal state region to the optimization constraints. The terminal state region is found by analyzing the stability. Also a terminal state controller is defined for this control scheme. The terminal state controller is a virtual controller and is never used in the control process. Two virtual leader-following formation models are studied. Simulations on different formation patterns are provided to verify the proposed control strategy.</jats:p
Bewegungsregelung mobiler Manipulatoren für die Mensch-Roboter-Interaktion mittels kartesischer modellprädiktiver Regelung
Für die Mensch-Roboter-Interaktion wird in dieser Arbeit eine Methode zur Überwachung der komplexen, dynamischen Roboterumgebung vorgestellt. Die Roboterbewegung wird basierend auf dem Konzept der modellprädiktiven Regelung unter Berücksichtigung der detektierten Hindernisse und der stattfindenden Kontakte des Roboters mit seiner Umgebung geregelt, um Kollisionen zu vermeiden und angemessen auf Kontakte zu reagieren. Die Ansätze werden auf einem mobilen Manipulator validiert
Optimization based solutions for control and state estimation in non-holonomic mobile robots: stability, distributed control, and relative localization
Interest in designing, manufacturing, and using autonomous robots has been rapidly growing
during the most recent decade. The main motivation for this interest is the wide range
of potential applications these autonomous systems can serve in. The applications include,
but are not limited to, area coverage, patrolling missions, perimeter surveillance, search
and rescue missions, and situational awareness. In this thesis, the area of control and
state estimation in non-holonomic mobile robots is tackled. Herein, optimization based
solutions for control and state estimation are designed, analyzed, and implemented to such
systems. One of the main motivations for considering such solutions is their ability of
handling constrained and nonlinear systems such as non-holonomic mobile robots. Moreover,
the recent developments in dynamic optimization algorithms as well as in computer
processing facilitated the real-time implementation of such optimization based methods
in embedded computer systems.
Two control problems of a single non-holonomic mobile robot are considered first; these
control problems are point stabilization (regulation) and path-following. Here, a model
predictive control (MPC) scheme is used to fulfill these control tasks. More precisely, a
special class of MPC is considered in which terminal constraints and costs are avoided.
Such constraints and costs are traditionally used in the literature to guarantee the asymptotic
stability of the closed loop system. In contrast, we use a recently developed stability
criterion in which the closed loop asymptotic stability can be guaranteed by appropriately
choosing the prediction horizon length of the MPC controller. This method is based on finite time controllability as well as bounds on the MPC value function.
Afterwards, a regulation control of a multi-robot system (MRS) is considered. In this
control problem, the objective is to stabilize a group of mobile robots to form a pattern.
We achieve this task using a distributed model predictive control (DMPC) scheme based
on a novel communication approach between the subsystems. This newly introduced
method is based on the quantization of the robots’ operating region. Therefore, the
proposed communication technique allows for exchanging data in the form of integers
instead of floating-point numbers. Additionally, we introduce a differential communication
scheme to achieve a further reduction in the communication load.
Finally, a moving horizon estimation (MHE) design for the relative state estimation
(relative localization) in an MRS is developed in this thesis. In this framework, robots
with less payload/computational capacity, in a given MRS, are localized and tracked
using robots fitted with high-accuracy sensory/computational means. More precisely,
relative measurements between these two classes of robots are used to localize the less
(computationally) powerful robotic members. As a complementary part of this study, the
MHE localization scheme is combined with a centralized MPC controller to provide an
algorithm capable of localizing and controlling an MRS based only on relative sensory
measurements. The validity and the practicality of this algorithm are assessed by realtime
laboratory experiments.
The conducted study fills important gaps in the application area of autonomous navigation
especially those associated with optimization based solutions. Both theoretical as
well as practical contributions have been introduced in this research work. Moreover, this
thesis constructs a foundation for using MPC without stabilizing constraints or costs in
the area of non-holonomic mobile robots