Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms

Abstract

The present research develops the parametric estimation of a second-order transfer function in its standard form, employing metaheuristic algorithms. For the estimation, the step response with a known amplitude is used. The main contribution of this research is a general method for obtaining a second-order transfer function for any order stable systems via metaheuristic algorithms. Additionally, the Final Value Theorem is used as a restriction to improve the velocity search. The tests show three advantages in using the method proposed in this work concerning similar research and the exact estimation method. The first advantage is that using the Final Value Theorem accelerates the convergence of the metaheuristic algorithms, reducing the error by up to 10 times in the first iterations. The second advantage is that, unlike the analytical method, it is unnecessary to estimate the type of damping that the system has. Finally, the proposed method is adapted to systems of different orders, managing to calculate second-order transfer functions equivalent to higher and lower orders. Response signals to the step of systems of an electrical, mechanical and electromechanical nature were used. In addition, tests were carried out with simulated signals and real signals to observe the behavior of the proposed method. In all cases, transfer functions were obtained to estimate the behavior of the system in a precise way before changes in the input. In all tests, it was shown that the use of the Final Value Theorem presents advantages compared to the use of algorithms without restrictions. Finally, it was revealed that the Gray Wolf Algorithm has a better performance for parametric estimation compared to the Jaya algorithm with an error up to 50% lower.

Parametric Identification of a Countercurrent Aqueous Hydrolysis Process for the Production of Glycerine under Higher Pressure and Temperature

“…A fermenter for the production of beer was modeled and controlled with the objective to restore flavor and improving performance [30]. The final value theorem was used to accelerate the search for parameters over the Gray Wolf Optimizer through a metaheuristic algorithm for electrical and electro-mechanical systems [31]. Most of the physical systems show complex behavior and can be divided into subsystems, for which transfer functions may be developed separately.…”