Doprava zdarma při nákupu nad 1 499 Kč přes Zásilkovnu nebo PPL Box.

Zjistit stav objednávky

Staňte se součástí komunity milovníků knih z celého světa a získejte hromadu výhod. Založit účet zdarma

Doprava zdarma se Zásilkovnou nad 1 499 Kč

Kurýr DPD 69 Kč Balíkovna 69 Kč PPL kurýr 74 Kč PPL box 39 Kč Zásilkovna 39 Kč Výdejní místo DPD 49 Kč PPL shop 49 Kč Balíkovna 49 Kč

Kontakt

Jak nakupovat

Pomoc

Můj účet

▸ Prázdný

Doprava zdarma při nákupu nad 1 499 Kč přes Zásilkovnu nebo PPL Box.

Model-Free Predictive Control

Name: Model-Free Predictive Control
Brand: VDM Verlag Dr. Müller
SKU: 06830522
Price: 1662 CZK
Availability: InStock
Author: Tim Barry
ISBN: 9783639227406

An Algorithmic Approach

Tim Barry

Jazyk

Angličtina

Kniha Brožovaná

Libristo kód: 06830522

Nakladatelství VDM Verlag Dr. Müller, listopad 2009

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

Libristo kód: 06830522

166 b

1 662 Kč

U nakladatele na objednávku Odesíláme za 17-27 dnů

Až 30 dní na vrácení zboží

Zákazníci také koupili

Predictive Control for Linear and Hybrid Systems Francesco Borrelli

Brožovaná

1 944 Kč

Introduction to Applied Linear Algebra Boyd

Pevná

1 535 Kč

Convex Optimization Stephen Boyd

Pevná

2 494 Kč

Numerical Optimization Jorge Nocedal

Pevná

1 613 Kč

Computer-Controlled Systems Karl J Astrom

Brožovaná

502 Kč

Adaptive Control Karl J Astrom

Brožovaná

550 Kč

Introduction to Stochastic Control Theory Karl J Astrom

Brožovaná

310 Kč

Optimization Models Giuseppe C. Calafiore

Pevná

2 123 Kč

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

Pevná

3 836 Kč

Fault Tolerant Control Design for Hybrid Systems Hao Yang

Brožovaná

2 749 Kč

Adaptive Dynamic Programming with Applications in Optimal Control Derong Liu

Pevná

5 410 Kč

Active Fault Tolerant Control Systems Mufeed M. Mahmoud

Brožovaná

2 356 Kč

Fault Detection and Diagnosis in Industrial Systems Leo H. Chiang

Brožovaná

1 728 Kč

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.