Algorithm to Find New Identifiable Reparameterizations of Parameteric Rational ODE Models

Identifiable reparametrizations of linear compartment models

Long memory models: a first solution to the infinite energy storage ability of linear time-invariant fractional models

Stiction is one of the most common problems in the spring-diaphragm type control valves, which are widely used in the process industry. In this paper, a procedure for single curve piecewise fitting stiction detection method and quantifying valve stiction in control loops based on ant colony optimization has been proposed. The single curve piecewise fitting method of detecting valve stiction is based on the qualitative analysis of the control signals. The basic idea of this method is to fit two different functions, triangular wave and sinusoidal wave, to the controller output data. The calculation of stiction index (SI) is introduced based on the proposed method to facilitate the automatic detection of stiction. A better fit to a triangular wave indicates valve stiction, while a better fit to a sinusoidal wave indicates nonstiction. This method is time saving and easiest method for detecting the stiction. Ant colony optimization (ACO), an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model estimated using ant colony optimization, from the input–output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.

The design of a new device called Tissue Resonator Indenter Device (TRID) for measuring soft tissue viscoelastic properties is presented. The two degrees-of-freedom device works based on mechanical vibration principles. When TRID comes into contact with a soft tissue, it can identify the tissue’s viscoelastic properties through the change of the device’s natural frequencies and damping ratios. In this paper, the deign of TRID is presented assuming Kelvin model for tissues. By working in the linear viscoelastic domain, TRID is designed to identify tissue properties in the range of 0–100 Hz. Assuming Kelvin model for tissues, the current paper develops a method for determining unknown tissue parameters using input-output data from TRID. Moreover, it is proved that the TRID’s parameters as well as the Kelvin tissue model parameters are globally identifiable. A parametric identification method using the prediction error approach is proposed for identifying the unknown tissue parameters in a grey-box state-space model. The reliability and effectiveness of the method for measuring soft tissue’s viscoelastic properties is demonstrated through simulation in the presence of considerable input and output noises.

In this paper, a new model reference adaptive output feedback controller is proposed for general multi-input multi-output (MIMO) systems with unknown parameters such that the tracking errors as well as the control gains remain uniformly bounded. First an adaptive observer is designed to estimate the state variables and system parameters from input-output data. Then a linear combination of the estimated states and the model reference states are used to design a robust adaptive output feedback controller. Several theorems are given to show the convergence of the modeling errors and the gains. Examples are also given to illustrate the effectiveness of the proposed method.

This paper presents the development of several multilayer feed-forward artificial neural network (ANN) models aimed at the simulation and parametrization of semiconductor optical amplifiers (SOAs). In the direct approach, it is assumed that the SOA physical parameters are known, and the ANNs can reproduce the results of detailed SOA models up to 20 times faster. In the inverse approach, it is assumed that the SOA is a grey-box with a known model but unknown parameters, and the ANNs infer the SOA model parameters from the input-output data. These results provide efficient surrogate models to represent SOAs in future digital twins for on-line optimization of ultra-wide band transmission systems that employ the SOA as the repeater element.

In this paper, we consider a retrial queue with a state-dependent service rate as a mathematical model of a node of FANET communications. We suppose that the arrival process is Poisson, the delay duration is exponentially distributed, the orbit is unlimited, and there is multiple random access from the orbit. There is one server, and the service time of every call is distributed exponentially with a variable parameter depending on the number of calls in the orbit. The service rate has an infinite number of values. We propose the asymptotic diffusion method for the model study. The asymptotic diffusion approximation of the probability distribution of the number of calls in the orbit is derived. Some numerical examples are demonstrated.

Guaranteed approximation error estimation of neural networks and model modification

A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models

A Global-Local Mapping Approximation method is presented in this paper for identifying discrete systems using input-output data. The method is based on the idea that any nonlinear system can be represented as a sum of a discrete linear model and unmodeled nonlinearities. Linear system is then perturbed by a nonlinear term which represents the system nonlinearities that are not captured by the linear model. To identify the discrete systems, discrete learning laws are derived using Lyapunov stability analysis. Numerical examples show the successful application of this technique for identification of nonlinear discrete models using data obtained from the simulations.

Teaching Aids for Modeling and Control of Hybrid Systems (CAMCHS)

Guaranteed nonlinear parameter estimation in knowledge-based models

This article develops a new method of data-driven modeling for a class of multiple-transmitter single-receiver wireless power transfer (WPT) systems. A continuous-time multiple-input single-output (MISO) model with pure time delays is used to characterize the input–output behavior of the system, where the transfer functions associated with each input channel are not constrained to have the same denominator. Moreover, the time delays are allowed to be a fraction of the sampling interval in order to account for the delay effects that stem from circuit components and wireless communication, which are, by nature, often a fraction of the sampling interval. An optimal refined instrumental variable method is proposed to estimate the parameters and time delays of the MISO model based on sampled input–output data. In contrast to the conventional circuit-theory-based modeling methods that rely on circuit parameters and result in models which are often complex, the proposed data-based method yields parsimonious models, whose parameters are directly estimated from input–output data. Due to the easy availability of sampled data in control engineering applications, the proposed method is clearly more user-friendly, having a broad prospect for efficient operation of WPT systems, such as prediction, optimization, and control. Numerical and experimental results are presented to validate the effectiveness and merit of the proposed method.

Fuzzy linear regression analysis for fuzzy input-output data

When we attempt to model a complex system including a human as an important component, it may be difficult to represent the system by a deterministic mathematical model. The main reason of this difficulty is that the system itself inherently has some fuzziness concerning subjective judgement of a human. In this paper, we propose a fuzzy nonlinear regression method with RFLN (RCE-based fuzzy learning network), which is capable of extracting knowledge of the experts automatically. RFLN is an extended RCE (restricted Coulomb energy) model, hence it needs few iterations in learning and its additional learning is easy. The proposed method has higher flexibility than fuzzy linear regression models. We propose learning algorithms to identify a nonlinear interval model which approximately includes all the given input-output data. The proposed method has characteristics of faster learning and of easier additional learning. The effectiveness of the method is shown by numerical experiments.