Accession Number : ADA168016


Title :   Basic Research on Adaptive Model Algorithmic Control


Descriptive Note : Final rept. Feb 1982-Jun 1985


Corporate Author : SCIENTIFIC SYSTEMS INC CAMBRIDGE MA


Personal Author(s) : Larimore, Wallace E ; Mahmood, Shahjahan


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a168016.pdf


Report Date : Dec 1985


Pagination or Media Count : 204


Abstract : The Model Algorithmic Control (MAC) method is investigated in terms of robustness and adaption to unknown or changing plants. The adaption method used is Canonical Variate Analysis (CVA) system identification. CVA is shown to provide system identification accuracy comparable to maximum likelihood and to provide an optimal selection of instrumental variables. Computationally CVA is a noniterative procedure that gives a numerically and statistically well conditioned solution to the system identification problem. A one-step-ahead MAC is explained using the classical root locus techniques. Conditions are developed for robustness of the controller to perturbations in the plant due to error in plant identification. Selection of an optimal sampling rate is based upon the control-ability and observability matrices. Simulations illustrating the above theory are presented using a Multi-Input Multi-Output (MIMO) missile aerodynamic model. (Keywords: multivariate analysis; Digital control systems).


Descriptors :   *ADAPTIVE CONTROL SYSTEMS , *ALGORITHMS , ACCURACY , AERODYNAMICS , DIGITAL SYSTEMS , IDENTIFICATION , INPUT , LOCUS , MATHEMATICAL MODELS , MAXIMUM LIKELIHOOD ESTIMATION , MULTIPLE OPERATION , MULTIVARIATE ANALYSIS , OPTIMIZATION , OUTPUT , PERTURBATIONS , RATES , SAMPLING , SELECTION , SOLUTIONS(GENERAL)


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE