Abstract
This paper proposes a theoretical framework for generalization of the well established first order plus dead time (FOPDT) model for linear systems. The FOPDT model has been broadly used in practice to capture essential dynamic response of real life processes for the purpose of control design systems. Recently, the model has been revisited towards a generalization of its orders, i.e., non-integer Laplace order and fractional order delay. This paper investigates the stability margins as they vary with each generalization step. The relevance of this generalization has great implications in both the identification of dynamic processes as well as in the controller parameter design of dynamic feedback closed loops. The discussion section addresses in detail each of this aspect and points the reader towards the potential unlocked by this contribution.
Highlights
The first order plus dead time (FOPDT) (First Order Plus Dead Time) model is a trademark approximation of process dynamic response for the purpose of control design
This paper proposes a theoretical framework for generalization of the well established first order plus dead time (FOPDT) model for linear systems
This paper investigates the stability margins as they vary with each generalization step
Summary
The FOPDT (First Order Plus Dead Time) model is a trademark approximation of process dynamic response for the purpose of control design. Derivative) is still the frontline feedback algorithm and that identification is still responsible for large costs [2] Approximations such as FOPDT are useful to allow first hand control design methods for non-control-expert process operators such as broadly exemplified in [3]. Stability margins are imposed as part of the design, such as gain and phase margin These are intrinsically used to determine the amount of robustness one aims for the closed loop characteristic behavior. Dead time variability is an important factor in determining the amount of fragility of a process, and fractional order control has proven to be intrinsically robust to these variations [15]
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