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Class of methods for discrete-time system identification and parameter tracking of sampled-data systems

University of British Columbia

In this work, methods for on-line identification of discrete-time systems and for parameter tracking of sampled-data systems are presented. These methods are suitable for implementation using small computers. A class of methods for the identification of the coefficients of linear and nonlinear difference equations is developed. The philosophy of identification is divided into three parts based on the norm of the error to be minimized. Techniques are derived using a common framework of minimization of these error functions, incorporating uniqueness and stability properties. Practical examples are included which demonstrate that among these proposed methods the identification error method solves the problem successfully. Extensions of these methods to continuous systems are briefly outlined. Methods are proposed for the generation of parameter sensitivity functions for sampled-data systems on a hybrid or a digital computer. Both linear and nonlinear systems are considered, and for a class of linear, systems,, an economical approach for the generation is developed, making extensive use of signal flow graph techniques. A new technique is devised for solving the problem of parameter tracking of linear and nonlinear sampled-data systems using the sensitivity functions. Examples are presented to demonstrate that the proposed techniques solve the problem.

Text

2011-05-31

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http://hdl.handle.net/2429/34969

Electrical and Computer Engineering

University of British Columbia

Graduate