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Trajectory Tracking, Path Following, and Learning in Model Predictive Control by

grandeagleretail
  • (924271)
US $85.25
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eBay item number:395324078168
Last updated on Jun 26, 2024 16:37:51 PDTView all revisionsView all revisions

Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
ISBN-13
9783832557058
Book Title
Trajectory Tracking, Path Following, and Learning in Model Predic
ISBN
9783832557058
Subject Area
Mathematics
Publication Name
Trajectory Tracking, Path Following, and Learning in Model Predictive Control
Publisher
Logos Verlag Berlin
Item Length
8.3 in
Subject
General
Publication Year
2023
Type
Textbook
Format
Trade Paperback
Language
English
Author
Fabian Russell Pfitz
Item Weight
7.9 Oz
Item Width
5.7 in
Number of Pages
157 Pages

About this product

Product Identifiers

Publisher
Logos Verlag Berlin
ISBN-10
3832557059
ISBN-13
9783832557058
eBay Product ID (ePID)
16062934947

Product Key Features

Number of Pages
157 Pages
Publication Name
Trajectory Tracking, Path Following, and Learning in Model Predictive Control
Language
English
Publication Year
2023
Subject
General
Type
Textbook
Subject Area
Mathematics
Author
Fabian Russell Pfitz
Format
Trade Paperback

Dimensions

Item Weight
7.9 Oz
Item Length
8.3 in
Item Width
5.7 in

Additional Product Features

Intended Audience
College Audience
Synopsis
In this thesis, we present novel model predictive control (MPC) formulations based on a convex open-loop optimal control problem to tackle the problem setup of trajectory tracking and path following as well as the control of systems with unknown system dynamic. In particular, we consider the framework of relaxed barrier function based MPC (rbMPC). We extend the existing stability theory to the trajectory tracking and the path following problem. We establish important system theoretic properties like closed-loop stability and exact constraint satisfaction under suitable assumptions. Moreover, we evaluate the developed MPC algorithms in the area of automated driving in simulations as well as in a real-world driving scenario. Further, we consider the control of completely unknown systems based on online optimization. We divide the overall problem into the design of an estimation algorithm and a control algorithm. The control algorithm is a model-independent receding horizon control algorithm in which important system theoretic properties like convergence to the origin are guaranteed without the knowledge of the true system parameters. The estimation and control algorithm are combined together and convergence to the origin of the closed-loop system for fully unknown linear time-invariant discrete-time systems is shown.

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grandeagleretail

grandeagleretail

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