Self-tuning regulators—a state space approach

DOI10.1080/00207178108922958zbMath0467.93046OpenAlexW2107631263WikidataQ56050445 ScholiaQ56050445MaRDI QIDQ3921101

No author found.

Publication date: 1981

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207178108922958

zbMATH Keywords

state feedbackclosed loop polesself-tuning controlstate space systemsautoregressive moving average system modelpole placement criterionself-tuning regulartors

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) Canonical structure (93B10) Observability (93B07) Stochastic systems in control theory (general) (93E03)

Related Items (20)

Application of the minimum state error variance approach to nonlinear system control ⋮ PID self-tuning control combining pole-placement and parameter optimization features ⋮ Adaptive deadbeat control of stochastic systems ⋮ A low-order active fault-tolerant state space self-tuner for the unknown sampled-data nonlinear singular system using OKID and modified ARMAX model-based system identification ⋮ Multivariable state-feedback self-tuning controllers^∗ ⋮ A NARMAX model-based state-space self-tuning control for nonlinear stochastic hybrid systems ⋮ Self-tuning control of bilinear systems ⋮ Optimal observers for ARMA models ⋮ Model transformations for state–space self–tuning control of multivariable stochastic systems ⋮ An alternative Kalman innovation filter approach for receiver position estimation based on GPS measurements ⋮ Canonical model transformations via long division and continued fraction methods ⋮ State-space design of multivariable self-tuning regulators ⋮ Simplified parameter adaptive control ⋮ Digital modelling and robust digital redesign of sampled-data uncertain systems via the interval tuning bilinear approximation method ⋮ An active fault-tolerant PWM tracker for unknown nonlinear stochastic hybrid systems: NARMAX model and OKID-based state-space self-tuning control ⋮ Modeling and tracker for unknown nonlinear stochastic delay systems with positive input constraints ⋮ A new neural network-based approach for self-tuning control of nonlinear SISO discrete-time systems ⋮ Using the filtered CARMA and CARIMA models for state-space self-tuning control ⋮ State-space formulations for the tittered CARMA model ⋮ Cost function methods of designing adaptive controllers for non-minimum phase plant

Cites Work

• Factorization methods for discrete sequential estimation
• On self tuning regulators

This page was built for publication: Self-tuning regulators—a state space approach