Check order status

Become part of a community of book lovers from all over the world and get access to a whole bunch of benefits. Create an account for free

DPD courier 4.99 € GLS courier 9.99 €

Contact

How to shop

Help

My account

▸ Empty :-(

Model-Free Predictive Control

Name: Model-Free Predictive Control
Brand: VDM Verlag Dr. Müller
SKU: 06830522
Price: 68.27 EUR
Availability: InStock
Author: Tim Barry
ISBN: 9783639227406

An Algorithmic Approach

Tim Barry

Language

English

Book Paperback

Libristo code: 06830522

Publishers VDM Verlag Dr. Müller, November 2009

This book presents a data-driven approach to constrained control in the form of a subspace-based sta... Full description

Libristo code: 06830522

166 b

68.27 €

Print on demand Shipping in 17-27 days

Up to 30 days for returns

Customers also purchased

Predictive Control for Linear and Hybrid Systems Francesco Borrelli

Paperback

87.17 €

Introduction to Applied Linear Algebra Boyd

Hardback

64.04 €

Convex Optimization Stephen Boyd

Hardback

109.90 €

Numerical Optimization Jorge Nocedal

Hardback

83.35 €

Computer-Controlled Systems Karl J Astrom

Paperback

22.92 €

Adaptive Control Karl J Astrom

Paperback

25.13 €

Introduction to Stochastic Control Theory Karl J Astrom

Paperback

14.17 €

Optimization Models Giuseppe C. Calafiore

Hardback

95.22 €

Optimal State Estimation - Kalman, H Infinity and Nonlinear Approaches Simon

Hardback

161.18 €

Fault Tolerant Control Design for Hybrid Systems Hao Yang

Paperback

115.02 €

Adaptive Dynamic Programming with Applications in Optimal Control Derong Liu

Hardback

235.09 €

Active Fault Tolerant Control Systems Mufeed M. Mahmoud

Paperback

102.35 €

Fault Detection and Diagnosis in Industrial Systems Leo H. Chiang

Paperback

75.21 €

Quellen zur Reform des Straf- und Strafprozessrechts, Teil 2, 1. Lesung Jürgen Regge

Hardback

393.36 €

This book presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset set-point tracking and non-stationary disturbance rejection. Parameterisation with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterisation with Laguerre polynomials. The discussed algorithms have also been presented in Matlab code.