Parameter estimation methods for time-invariant continuous-time systems from dynamical discrete output responses based on the Laplace transforms

In industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous-time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous-time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example.