Predictor-based self-tuning control. (English) Zbl 0539.93054

This paper deals with a single-input single-output stochastic system driven by a digital self-tuning controller. It is required that the controller is automatically tuned also with respect to possible changes of the setpoint (command signal), which may be either a-priori known (program control) or uncertain (servo), or it may be required that the process output follows the output of a reference model (model following). The self-tuning controller may include the feedforward from the measurable external disturbance, if available.
Linear finite-memory output predictors updated in real-time appear to be a suitable internal representation of the system in the digital self- tuning controller. In most cases of industrial process control the Incremental-predictors should be preferred to Positional-predictors. Two main arguments for this recommendation are presented. A new time- domain approach to the quadratic-optimum control synthesis for systems described by such predictors is presented. 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.
Reviewer: M.Tibaldi


93C40 Adaptive control/observation systems
62M20 Inference from stochastic processes and prediction
93B50 Synthesis problems
60G25 Prediction theory (aspects of stochastic processes)
93E25 Computational methods in stochastic control (MSC2010)
93E20 Optimal stochastic control
Full Text: DOI


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