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 language | English (US) |
---|---|
Pages (from-to) | 59-86 |
Number of pages | 28 |
Journal | International Journal of Robust and Nonlinear Control |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2001 |
ASJC Scopus subject areas
- Control and Systems Engineering
- General Chemical Engineering
- Biomedical Engineering
- Aerospace Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering