Identification Of Continuous-time Model Research Articles

Identification of continuous-time Hammerstein model using improved Archimedes optimization algorithm

Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. The proposed algorithm employed two principal modifications to mitigate these issues and enhance identification accuracy: (i) exploration and exploitation phase recalibrations using a revised density decreasing factor and (ii) local optima entrapment alleviation utilizing safe experimentation dynamics. Various advantages were observed with this proposed algorithm, including a lower number of coefficient criteria, improved accuracy in Hammerstein model identification, and diminished processing demands by reducing gain redundancy between nonlinear and linear subsystems. This proposed algorithm also discerned linear and nonlinear subsystem variables within a continuous-time Hammerstein model utilizing input and output data. The process was evaluated using a numerical example and two practical experiments [twin-rotor system (TRS) and electro-mechanical positioning system (EMPS)]. Several parameters were then analyzed, such as the convergence curve of the fitness function, frequency and time domain-related responses, variable deviation index, and Wilcoxon's rank-sum test. Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74 % mean fitness function and 75.34 % variable deviation indices enchantments compared to the traditional AOA. Improved mean fitness function values were also revealed in the TRS (11.63 %) and EMPS (69.63 %) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm).

Identification of a MIMO continuous-time transfer function model with different denominators

This paper is about the identification of a MIMO continuous-time (CT) transfer function model with different denominators. A new algorithm is proposed with two main steps. The first step consists of the estimation of an overparameterized MIMO CT reinitialized partial moment model and the second one performs a moment-based transfer function model reduction. For the unknown structure case, a system structure estimation procedure is given.

部分空間法を用いた連続時間ウィナー型非線形モデルの同定

This paper is concerned with identification of continuous-time Wiener-type nonlinear model. A subspace method is applied to identify the linear dynamic element represented by δ-operator model transformed through Laguerre filters. The nonlinear part is estimated via least-squares approximation. Numerical simulation results are provided to illustrate the proposed identification method.

CONTINUOUS-TIME IDENTIFICATION USING LQG-BALANCED MODEL REDUCTION

System identification of continuous-time model based on discrete-time data can be performed using a algorithm combining linear regression and LQG-balanced model reduction. The approach is applicable also to unstable system dynamics and it provides balanced models for optimal linear prediction and control.

Identification of a Nonlinear Model for State Estimation in an Airplane

Abstract The requirements, the design and particularly the identification of a nonlinear continuous-time aircraft model to be used in a deterministic observer will be shown in this paper. The model is to be used in a LUENBERGER-observer that computes the aircraft velocity vector (in polar coordinates) from inertial data, provided by a laser-strap-down platform. Derived from an aircraft model using six degrees of freedom, the observer model is reduced to third order (local observer). Thereby, insufficiently known parts of the model are replaced by precisely measured data and the computing time is reduced. The identification of the model and its parameters is achieved by two main steps according to the Estimation - Before - Modelling Technique. First, the measurement errors are identified in a data - correction step by a Maximum Likelihood - identification using the kinematic part of the aircraft model. The remaining parameters (especially aerodynamic coefficients and derivatives) are estimated in a second step by a Least Squares - optimizer using the force equations. For the identification of the aircraft model and the verification of the observer, flight - test data from the twin engine aircraft Dornier DO128 are used. As shown in this paper, the identified model is a very good approximation to the real system, and the resulting observer is a good instrument for sensor fault detection and analytic redundancy.

Augmented hybrid method for continuous process identification from sampled data with coloured noise

The identification of a linear continuous-time model for a multivariable dynamic system from sampled input-output observations is considered. An augmented hybrid parametric method is proposed to overcome the interference of the coloured output noise in the sampled output. The parameters of the continuous-time process model are estimated from an augmented input-output realization which utilizes the dynamic information of the discrete-time noise model. Numerical examples are presented to illustrate how to obtain an adequate dynamic process model, considering the coloured output noise, by using a discrete-time noise model.

Identification of continuous-time state-space model from samples of input-output data

The identification of the parameters of a state-space model for a single-input single-output system from samples of the input-output data is considered. Use of the trapezoidal rule of integration, together with the least-squares algorithm, leads to the continuous-time model directly without assuming that the input is held constant between sampling instants.