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Control Theory for Automation – Advanced Techniques

Chapter
Part of the Springer Handbooks book series (SHB)

Abstract

Analysis and design of control systems is a complex field. In order to develop appropriate concepts and methods to cover this field, mathematical models of the processes to be controlled are needed to apply. In this chapter mainly continuous-time linear systems with multiple input and multiple output (MIMO systems) are considered. Specifically, stability, performance, and robustness issues, as well as optimal control strategies are discussed in detail for MIMO linear systems. As far as system representations are concerned, transfer function matrices, matrix fraction descriptions, and state-space models are applied in the discussions. Several interpretations of all stabilizing controllers are shown for stable and unstable processes. Performance evaluation is supported by applying H 2 and H norms. As an important class for practical applications, predictive controllers are also discussed. In this case, according to the underlying implementation technique, discrete-time process models are considered. Transformation methods using state variable feedback are discussed, making the operation of nonlinear dynamic systems linear in the complete range of their operation. Finally, the sliding control concept is outlined.

Keywords

Robust Stability Linear Quadratic Regulator Recede Horizon Control Transfer Function Matrix Generalize Predictive Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

DMC

dynamic matrix control

EVD

eigenvalue–eigenvector decomposition

GPC

generalized predictive control

HJB

Hamilton–Jacobi–Bellman

IMC

instrument meteorological condition

IMC

internal model controller

LMFD

left matrix fraction description

LQ

linear quadratic

LQR

linear quadratic regulator

MFD

multifunction display

MIMO

multi-input multi-output

MPC

model-based predictive control

NP

nominal performance

NP

nondeterministic polynomial-time

NS

nominal stability

RHC

receding horizon control

RMFD

right matrix fraction description

RP

reward–penalty

RS

robust stability

SISO

single-input single-output

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Automation and Applied InformaticsBudapest University of Technology and EconomicsBudapestHungary
  2. 2.Department of Automation and Applied InformaticsBudapest University of Technology and EconomicsBudapestHungary
  3. 3.Department of Automation and Applied InformaticsBudapest University of Technology and EconomicsBudapestHungary

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