H prediction and unconstrained H predictive control: Multi-input-multi-output case

Haipeng Zhao, Wei Li, Joseph Bentsman

Research output: Contribution to journalArticlepeer-review

Abstract

Through the combination of the sequential spectral factorization and the coprime factorization, a k-step ahead MIMO H (cumulative minimax) predictor is derived which is stable for the unstable noise model. This predictor and the modified internal model of the reference signal are embedded into the H optimization framework, yielding a single degree of freedom multi-input-multi-output H predictive controller that provides stochastic disturbance rejection and asymptotic tracking of the reference signals described by the internal model. It is shown that for a plant/disturbance model, that represents a large class of systems, the inclusion of the H predictor into the H control algorithm introduces a performance/robustness tuning knob: an increase of the prediction horizon enforces a more conservative control effort and, correspondingly, results in deterioration of the transient and the steady-state (tracking error variance) performance, but guarantees large robustness margin, while the decrease of the prediction horizon results in a more aggressive control signal and better transient and steady-state performance, but smaller robustness margin.

Original languageEnglish (US)
Pages (from-to)59-86
Number of pages28
JournalInternational Journal of Robust and Nonlinear Control
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2001

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Chemical Engineering(all)
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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