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MIMO SYSTEM Contributions to Advanced Control and

02-06-2012, 18:34
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                    19
      1. INTRODUCTION
      The plethora of available theoretical and practical solutions to the control problem for linear(ized) multivariable discrete-time systems has brought this control branch to the status of a matured, if not classical, discipline. However, in spite of the unprecedented progress in the multivariable control field for square systems (equal numbers of inputs and outputs), for which the system analysis and control design results are unquestionable fAnM, BrY, CaD, Cha, Che, DFT, Fri. Kac, Kai, Kuc, Lew, Mac, MoZ, Nil, Roi, SkP, Tsu, Wo2, ZDGJ, there have been some controversies when analyzing non-square systems and synthesizing their multivariable controllers. There have appeared certain misconstructions resulted from ambiguity of an intriguing notion of zeros for non-square systems. A number of not necessarily equivalent definitions of zeros have been spread throughout the control literaturę, raising discussions, polemics and quarrels. The first goal of this monograph is to present a new, original, control-oriented insight into the problem of zeros for MIMO systems. As a result, a new definition of zeros of a multivariable system is introduced. The so-called Control zeros1 are shown to perfectly extend and generalize the notion of zeros of SISO systems. The control zeros provide a new insight into modelling and control of non-sąuare multivariable systems.
      Nowadays, a typical approach to a linear control design problem for multivariable systems is to start from either state-space or AR(I)XZAR(I)MAX models and employ LQR7LGQ or UPC/GPC controllers, respectively. In morę ^mbitious1 attempts, FL-related concepts are often applied, with morę involving computations and a shortage of working full-scale implementations fSPGJ. It sometimes happens that a burdensome numerical maehinery is used to design an advanced controller for quite a simple plant, which might have not necessarily required such sophisticated algorithms for its effective control. An example of a Simple1 plant is an open-loop stable minimum phase one, which can be effectively stabilized with e.g. a (slightly modified) minimum variance controller or IMC-structured controller. And these are very simple controllers which can be implemented using the single-chip microprocessor technology. Numerical simplicity of a control algorithm may be essential for !fasf dynamie systems where ^hort1 sampling periods should be selected.
      However, in practical control situations we often face nonminimum phase systems. Moreover, discrete-time representations of minimum phase continuous plants may be nonminimum phase. Therefore it is necessary to account for nonminimum phase behaviour in many realistic control design schemes.

                    Tytuł: 

      STUDIA I MONOGRAFIE
      z. 102

      Contributions to Advanced Control and Estimation for Linear Discrete-Time MIMO Systems


                    Autor: 
      KRZYSZTOF J. LATAWIEC


                    Wydawnictwo i rok wydania: 

      OFICYNA WYDAWNICZA
      POLITECHNIKA OPOLSKA 1998
      170+20+10 egz.


                    Stan: -bdb wyblakla plamka na gornym rogu stron

                    Oprawa: miękka

                    Ilość stron i format: 150str.

                    16,5x24 cm

                    Ilustracje: rys.

                    Spis treści: 
      62 6. CONCLUSIONS
      64 APPENDIX A.l
      65 APPENDIX A.II
      66
      68 REFERENCE
      70
      73
      74
      77
      78
      Contents:
      6
      17 19
      SYMBOLS ABBREVIATIONS 1. INTRODUCTION
      2. CONTROL ZEROS AND MINIMUM PHASE SYSTEMS 23
      2.1. Preliminaries 24
      Rationał matńces 24
      Generalized inverses, right/left irwerses 25
      Prerogative on right-invertible systems 27
      System representations 28

      Suryeyofdefinitionsofzeros1 29
      Constructive counterexamples 32
      Output zeroing problem vs. transmission blocking
      problem 37
      Why not DZ1 through DZ8 when controlling nonsąuare
      systems 40
      Controlzeros 41
      Minimum variance control 46
      The case of multiple delays 51
      The nonuniąueness issue 53

      Perfect regulation 53
      Perfect filtering 60
      Infimum time control
      Closing remarks
      3. EXTENDED HORIZON MODEL ALGORITHMIC CONTROL
      Extended Horizon Predictive Control
      Internal Model Control

      Central IMC theorem
      Model Algorithmic Control

      Open-loop design of feedback EHPC systems
      RobustEHMAC

      Robustness to parametric uncertainties
      Robustness to uncertainties in estimation
      oftime delay yg
      3.5. Tuning in EHMAC
      Tuning the prediction horizon
      Tuning the IMC filter parameter
      3.6. Closing remarks
      4. ROBUST ADAPTIVE L.S. ESTIMATION
      Parameter estimation problem
      Normalization with covariance matrix
      New ALS estimation strategy

      Error propagation system
      Single-loop vs. cascade estimation
      Sąuare-root update of covariance matrbc
      Convergence measures
      Forgetting methodology
      Persistency of excitation
      Offset model formulation
      Bounded influence regression

      New estimation algorithm
      Convergence of the new adaptive estimator

      Stochastic framework ofanalysis
      Conditions for the boundedness ofthe
      tracking error

      Extended horizon adaptive model algorithmic control
      Closing remarks
      5. OPEN PROBLEMS
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      107 [zasłonięte] 114
      123 [zasłonięte] 129 131 135

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