Nonlinear system identification employing automatic differentiation (original) (raw)

Parameter Estimation and Identification for Systems with Delays

SIAM Journal on Control and Optimization, 1981

Parameter identification problems for delay systems motivated by examples from aerodynamics and biochemistry are considered. The problem of estimation of the delays is included. Using approximation f results from semigroup theory, a class of theoretical approximation schemes is developed and two specific cases (baveraging and 4splined methods) are shown to be included in this treatment. Convergence results, error estimates, and a sample of numerical findings are given.-CCESSION for o NTIS White Section DDC Buff Section CUNANNOUNCED JUSTIFICATION Dist.

Online parameter identification of linear time-delay systems

Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2002

Synthesis of an adaptive parameter identifier is developed for linear dynamic systems with finitely many lumped delays in the state vector and control input. The state of the system is assumed to be available for measurements. Once the parameter identifiability is guaranteed the simultaneous on-line identification of the system parameters and delays is achieved by the adaptive identifier proposed. Theoretical results are supported by numerical simulation.

On-line identification of systems with delayed inputs

2006

Résumé: This communication deals with on-line identification of systems with delayed inputs. It is based on new non-asymptotic algebraic estimation techniques. A concrete case-study and an application to transmission delays are discussed. Several successful ...

Optimal time delay estimation for system identification

2013 American Control Conference, 2013

ABSTRACT This paper presents a novel strategy for optimal time delay estimation for system identification purposes. A brief review of classical time delay estimation techniques is presented and the problem of time delay estimation is further inspected from a system identification perspective. A solution based on optimization of the fit index is then proposed to find the best time delay, so that a given model structure with a certain order and adjusted for an identification dataset best represents the true system. A set of examples illustrates and validates the application of the proposed technique.

Easy-Implementable On-line Identification Method for a First-Order System Including a Time-Delay

2010

This paper proposes a simple yet effective on-line identification method for a first-order system including a time-delay. This method is based on the Laplace transformation in a real number domain and is able to estimate both coefficients of the first-order system and the time-delay simultaneously. An accuracy of the identification was investigated through a simulation. As a result, precise estimation of the method was confirmed compared to an orthodox on-line estimation technique that utilized a bilinear-model. Moreover, a guideline for a tuning of their parameters used in the method is shown. Applying the method to an actual sensor identification, issues under the practical usage were investigated, and the countermeasure was mentioned.

Model parameter and time delay estimation using gradient methods

Conference papers, 1994

A number of approaches have been proposed for parameter and time delay estimation of process models in single input, single output (SISO) control systems using gradient descent algorithms; some of these approaches involve the selection of a rational polynomial that is used to approximate time delay variations. This paper takes a generalised approach to the investigation of the most appropriate choice of the rational polynomial, and the gradient descent algorithm, to be used.

On-line Parameter and Delay Estimation of Continuous-Time Dynamic Systems

International Journal of Applied Mathematics and Computer Science, 2015

The problem of on-line identification of non-stationary delay systems is considered. The dynamics of supervised industrial processes are usually modeled by ordinary differential equations. Discrete-time mechanizations of continuous-time process models are implemented with the use of dedicated finite-horizon integrating filters. Least-squares and instrumental variable procedures mechanized in recursive forms are applied for simultaneous identification of input delay and spectral parameters of the system models. The performance of the proposed estimation algorithms is verified in an illustrative numerical simulation study.

Identification of the delay parameter for nonlinear time-delay systems with unknown inputs

Automatica, 2013

By using the theory of non-commutative rings, this paper studies the delay identification of nonlinear time-delay systems with unknown inputs. A sufficient condition is given in order to deduce an output delay equation from the studied system. Then necessary and sufficient conditions are proposed to judge whether the deduced output delay equation can be used to identify delay involved in this equation. Two different cases are discussed for the dependent and independent outputs, respectively. The presented result is applied to identify delay in a biological system.

Neural network based time-delay estimation for nonlinear dynamic systems

Proceedings of the 15th IFAC World Congress, 2002, 2002

The estimation for the nonlinear dynamic system with time-varying input timedelay is an important issue for system identification. In order to estimate the dynamics of the process, a dynamic neural network with external recurrent structure is applied to the modelling procedure. In the case where time-delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to track the input time-delay variation. In this paper, two schemes respectively called direct as well as indirect time-delay estimators are proposed. Finally, two numerical examples are illustrated for the test of the proposed methods.

A fast identification algorithm for systems with delayed inputs

2011

A fast identification algorithm is proposed for systems with delayed inputs. It is based on a non-asymptotic distributional estimation technique initiated in the framework of systems without delay. Such technique leads to simple realization schemes, involving integrators, multipliers and piecewise polynomial or exponential time functions. Thus, it allows for a real time implementation. In order to introduce a generalization to systems with input delay, three simple examples are presented here. The first illustration is a first order model with delayed input and noise. Then, a second order system driven through a transmission line is considered. A third example shows a possible link between simultaneous identification and generalized eigenvalue problems.