Discrete state-space modeling for linear \(n\)th-order constant coefficient distributed-parameter systems. (English) Zbl 0768.93005

Moshfegh, Allen

Dyn. Control 3, No. 1, 71-90 (1993).

Summary: A systematic procedure is developed for state-space modeling and solving the dynamic behavior of any linear \(n\) order constant coefficient distributed-parameter system with two or more independent variables. The state-space model is a set of first-order linear difference equations and is also referred to a discrete multidimensional state-space model. Transformation of a continuous distributed-parameter system into a discrete state-space model is based on the multidimensional Laplace- bilinear mapping technique. A procedure is outlined for converting the initial and boundary conditions of the system into a set of discrete conditions appropriate for the state-space model. Convergence of the state-space model’s solution to the exact solution depends on the sampling rates of the independent variables and the ratio of increments. A few examples when state-space modeling of a distributed-parameter system is useful are: to estimate optimal feedback or optimal feedforward gains in active control applications; model reference optimal-distributed tracking systems; optimal tracking of desired trajectories; real-time system identification.

MSC:

93A30	Mathematical modelling of systems (MSC2010)
93C05	Linear systems in control theory

Keywords:

state-space modeling; multidimensional Laplace-bilinear mapping technique

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