Paper: Predictor-based self-tuning control

Authors:
V. Peterka
Affiliations:
Institute of Information Theory and Automation, Czechoslovak Academy of Sciences, Prague 8, Pod vodárenskou vi 4, Czechoslovakia
Venue:
Automatica (Journal of IFAC)
Year:
1984

Citing 0
Cited 2

Paper: Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach

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Survey Constrained model predictive control: Stability and optimality

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Abstract

Linear finite-memory output predictors updated in real-time appear to be a suitable internal representation of the system in a digital self-tuning controller. A new time-domain method of quadratic-optimum control synthesis for systems described by such predictors is presented. The synthesis covers both the servo problem and the regulation problem, including the program control (with preprogrammed command signal) and the feedforward from measurable external disturbances. Unlike the standard Riccati equation, the method leads to algorithms (or explicit formulae in low-order cases) which are numerically robust and therefore suitable for real-time computation using microprocessors with reduced wordlength.