Basic Research on Adaptive Model Algorithmic Control

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).

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1985
Accession Number
ADA168016

Entities

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  • Shahjahan Mahmood
  • Wallace E. Larimore

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  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

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  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Control Systems Engineering.