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A state space self‐tuning controller with integral action
Author(s) -
Desai Premal,
Mahalanabis A. K.
Publication year - 1992
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.4660130404
Subject(s) - control theory (sociology) , controller (irrigation) , weighting , estimator , observer (physics) , linear quadratic gaussian control , mathematics , state space , full state feedback , separation principle , minimum phase , state vector , state (computer science) , computer science , optimal control , state observer , mathematical optimization , phase (matter) , nonlinear system , control (management) , algorithm , artificial intelligence , biology , classical mechanics , quantum mechanics , agronomy , radiology , medicine , statistics , physics , chemistry , organic chemistry
The paper is concerned with the problem of designing a state space self‐tuning controller with integral action for a class of unknown linear stochastic systems. The observer form of the innovations model of the system is used along with a single‐stage quadratic‐in‐state‐and‐control performance index which includes a cross‐weighting between system states and inputs. This results in a proportional estimated state plus integral output (P + I) controller in an incremental form. While it is analogous to the LQG‐theory‐based optimal adaptive controller in structure, computational simplification is achieved by the choice of a single‐stage performance index and a direct estimation of the state estimator gain matrix. The new controller has the advantages of being applicable to both single‐input/single output (SISO) and multi‐input/multi‐output (MIMO) minimum phase and non‐minimum phase systems. The results are illustrated numerically through two simulation examples.