Proposed Identification Algorithm Research Articles

Robotic assembly of threaded fasteners in a non-structured environment

Over the past two decades, a major part of the manufacturing and assembly market has been driven by the increasing demand for customised products. This has created the need for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products—or to assemble different parts—with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In high-volume production, fastening operations are commonly automated using jigs, fixtures, and semi-automated tools. However, in low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This motivated the development of a flexible solution, which does not require fixtures and is adaptable to variation in part locations and lighting conditions. As a part of this research, a novel 3D threaded hole detection and a fast bolt detection algorithms are proposed and reported in this article, which offer substantial enhancement to the accuracy, repeatability, and the speed of the processes in comparison with the existing methods. Hence, the proposed method is more suitable for industrial applications. The development of an automated bolt fastening demonstrator is also described in this article to test and validate the proposed identification algorithms on complex components located in 3D space.

A time-domain system identification numerical procedure for obtaining linear dynamical models of multibody mechanical systems

This paper is focused on the development of a numerical procedure for solving the system identification problem of linear dynamical models that mathematically describe multibody mechanical systems. To this end, an input–output representation of the time evolution of a general mechanical system based on a sequence of matrices referred to as Markov parameters is employed. The set of Markov parameters incorporate the state-space matrices that allow for describing the dynamic behavior of a general mechanical system considering the assumption of structural linearity. The system Markov parameters are defined by means of a discretization process applied to the analytical description of a mechanical system, and therefore, they are difficult to obtain directly from observable measurements. However, a state observer can be introduced in order to define a set of observer Markov parameters that can be readily recovered from input–output experimental data. The observer Markov parameters obtained by using a least-square approach allow for computing in a recursive manner the system Markov parameters as well as another discrete sequence of matrices referred to as observer gain Markov parameters. Subsequently, the system and observer gain Markov parameters identified from observable input–output data are used for constructing a sequence of generalized Hankel matrices from which a state-space model of the mechanical system of interest can be extracted. This fundamental step of the identification procedure is performed in the algorithm elaborated in this work employing a numerical procedure which relies on the use of the Moore–Penrose pseudoinverse matrix obtained by means of the singular value decomposition. In the paper, the principal analytical and numerical aspects of the proposed identification algorithm are described in detail. Furthermore, a numerical example based on a simple vehicle model is discussed in order to verify by means of numerical experiments the effectiveness of the identification procedure developed in this work.

On-line Inertia Measurement of Unmanned Aerial Vehicles using on board Sensors and Bifilar Pendulum

Identification of a dynamical model and its parameter estimation is one of the fundamental problems in field of robotics and system dynamics modelling. For the general case of object motion with six degrees of freedom (6- DOF), such as in the case of Unmanned Aerial Vehicle (UAV), the key physical parameters are its mass and tensor of inertias. Even though UAV mass and its geometry/topology are easily obtainable, an identification of inertia tensor is difficult considering that is not measurable by static tests. This paper presents a simple and effective method for on-line estimation of a rigid-body inertia based on two-wire pendulum and integrated sensor system. Herein, the test subject (i.e. UAV) is suspended by two thin parallel wires in such a way to form a bifilar torsional pendulum about the vertical axis. Using on-board sensors from UAV flight controller (FC) unit, the pendulum oscillations are recorded and processed to obtain trend-free and noise-free signals used in the final inertia estimation phase based on inertia- related properties of pendulum oscillations. The proposed identification algorithm is verified experimentally for two typical cases of suspended objects related to UAV control box and full UAV configuration.

ИДЕНТИФИКАЦИЯ СИСТЕМ ГАММЕРШТЕЙНА ДРОБНОГО ПОРЯДКА С ПОЛИНОМИАЛЬНОЙ НЕЛИНЕЙНОСТЬЮ ПРИ НАЛИЧИИ ДРОБНОГО БЕЛОГО ШУМА

An identification algorithm is proposed nonlinear dynamical systems of fractional order of the Hammerstein class. Designed algorithm allows you to get strongly consistent parameter estimates in the presence of observation noise in the form of fractional white noise. The results of numerical experiments showed high efficiency of the proposed identification algorithm in comparison with the least squares method (LS).

A bias-correction method for closed-loop identification of Linear Parameter-Varying systems

Due to safety constraints and unstable open-loop dynamics, system identification of many real-world processes often requiresgathering data from closed-loop experiments. In this paper, we present a bias-correction scheme for closed-loop identification of Linear Parameter-Varying Input–Output (LPV-IO) models, which aims at correcting the bias caused by the correlation between the input signal exciting the process and output noise. The proposed identification algorithm provides a consistent estimate of the open-loop model parameters when both the output signal and the scheduling variable are corrupted by measurement noise. The effectiveness of the proposed methodology is tested in two simulation case studies.

Modeling study of nonlinear dynamic soft sensors and robust parameter identification using swarm intelligent optimization CS-NLJ

In order to give a sufficient description of system characteristics, this paper proposes a Wiener model for nonlinear dynamic soft sensors. Based on the model, an improved swarm intelligent identification algorithm is proposed by combining the global cuckoo search (CS) and the local new Luus-Jaakola (NLJ) optimization. The Levy flight in the CS helps the search escape from local optima, and the NLJ search achieves the exploitation of local areas. Thus, a good balance is guaranteed between algorithmic exploration and exploitation. Because of the strong search ability of CS-NLJ algorithm, the approximation accuracy of identification can be improved in the presence of stochastic Gaussian or heavy-tailed noises. Further, the robust identification for dual-rate soft sensors is achieved by using the proposed model and the proposed identification algorithm. Finally, the simulation results verify the theoretical findings.

Identification of higher-order continua equivalent to a Cauchy elastic composite

A heterogeneous Cauchy elastic material may display micromechanical effects that can be modeled in a homogeneous equivalent material through the introduction of higher-order elastic continua. Asymptotic homogenization techniques provide an elegant and rigorous route to the evaluation of equivalent higher-order materials, but are often of difficult and awkward practical implementation. On the other hand, identification techniques, though relying on simplifying assumptions, are of straightforward use. A novel strategy for the identification of equivalent second-gradient Mindlin solids is proposed in an attempt to combine the accuracy of asymptotic techniques with the simplicity of identification approaches. Following the asymptotic homogenization scheme, the overall behaviour is defined via perturbation functions, which (differently from the asymptotic scheme) are evaluated on a finite domain obtained as the periodic repetition of cells and subject to quadratic displacement boundary conditions. As a consequence, the periodicity of the perturbation function is satisfied only in an approximate sense, nevertheless results from the proposed identification algorithm are shown to be reasonably accurate.

An Approximate Algorithm for Quantum Hamiltonian Identification with Complexity Analysis

Identification of the Hamiltonian is vital for characterizing the dynamical evolution of a quantum system. The dimension of a multi-qubit system increases exponentially with the qubit number, which usually leads to daunting computational complexity for general Hamiltonian identification algorithms. In this paper, we design an efficient quantum Hamiltonian identification method based on periodical sampling. The computational complexity is O(M2 + MN2), where M is the number of unknown parameters to be identified in the Hamiltonian and N is the length of the sampling data. Numerical results with different data lengths demonstrate the effectiveness of the proposed identification algorithm.

A Novel Vector Hysteresis Model Using Anisotropic Vector Play Model Taking Into Account Rotating Magnetic Fields

This paper presents an improved identification algorithm of the scalar play model, and suggests a novel vector play model to describe the vector hysteretic behavior of a nonoriented electrical steel sheet (ESS) under rotating magnetic fields. The proposed identification algorithm gives much better numerical accuracy than a conventional one by assigning more play operators than the measured asymmetric minor loops. The vector-shape function of the suggested vector play model consists of two scalar shape functions, which are independently identified by using measured hysteresis loops under alternating magnetic fields along rolling and transverse direction of ESS, respectively. The vector play model, furthermore, employs a nonlinear coefficient, which is a function of the magnitude and direction of magnetic flux density, to increase the numerical accuracy. The validity of the suggested vector play model is verified by comparing it with experimental results under various magnetic-field conditions.

Dynamic System Transfer Function Identification Based on the Experimental Results

The paper deals with identifying linear dynamical systems from the experimental data obtained through applying the test signals to the system. The paper objective is to determine both the form and the coefficients of the transfer function retrieved from the hodograph samples experimentally at bench test. The order of the frequency transfer function of the system being identified was assumed to be unknown. It was expected that in obtaining the frequency characteristics of a real system there would be noise during the experiment as a result of which the points of the experimentally obtained hodograph would be randomly shifted. As a model, a certain transfer function of the system was adopted. The authors proposed to find a solution of the identification problem in the class of hodographs specified by the model of the system. The search for unknown coefficients of the transfer function of the system model is carried out by minimizing a proximity criterion (measure) - described and published earlier by one of the authors - between the experimentally received system hodograph and the system model on an entire set of the experimental points of the system hodograph and the hodograph of the system model. The solution of linear dynamic system identification from the frequency hodograph was reduced to solving a system of equations of the system model frequency transfer function that is linear with respect to unknown parameters. The proposed identification algorithm allows us to determine the order of the frequency transfer function of the identified system from the experimentally obtained samples of the frequency hodograph of the system. For dynamic systems of the fifth order at most there is software developed to simulate the process providing the pseudo-experimental data with random errors and determining the parameters of such systems. A computational experiment has been carried out to evaluate the error with which the proposed algorithm determines the parameter values of the system to be identified. The illustrative computational experiment has shown that using the proposed algorithm for identifying a linear dynamic system from the frequency hodograph the error in determining the coefficient values of the frequency transfer function of the system is comparable with a range of measuring error in the experimental samples of the hodograph of this system. In known sources on identification of linear dynamic systems there is no method of identification this publication describes. This identification method of linear dynamic systems can find application in experimental testing, verification tests in situ and iron bird tests for vehicles of various purposes.

Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB) electric model by using a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD) of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.

Combined wavelet–Hilbert transform-based modal identification of road bridge using vehicular excitation

In this paper, continuous wavelet transform (CWT)-based filtering strategy is utilized along with Hilbert transform (HT) for modal identification of reinforced concrete road bridge. Although, wavelet-based pre-filtering prior to Hilbert–Huang transform is published in the literature, the advantage and uniqueness of the proposed method over traditional HHT or WT-HHT lies in its ability to bypass the time-consuming evaluation of intrinsic mode functions (IMFs) that often produces spurious modes and mode-mixing. This is achieved using a modified form of Littlewood–Paley basis function whose compact support in frequency domain helps to filter the signal in leakage-free frequency bands. It is followed by HT for modal parameter estimation. The proposed identification algorithm is first tested with synthetic records having closely spaced strong and weak modes to validate its efficiency and accuracy. Then, a full-scale reinforced concrete road bridge is considered for experimental validation. Results presented in this work clearly show that it can effectively evaluate significant number of modes which are helpful in finite element model updating of large structures. Finally, the advantage of the proposed method is demonstrated by comparing its performance with other methods available in the literature.

Application of System Identification for Modeling the Dynamic Behavior of Axial Flow Compressor Dynamics

Identification of a one-stage axial compressor system is addressed. In particular, we investigate the underlying dynamics of tip air injection and throttle activation to the overall compressor dynamics and the dynamics around the tip of the compressor blades. A proposed subspace system identification algorithm is used to extract three mathematical models: relating the tip air injection to the overall dynamics of the compressor and to the flow dynamics at the tip of the compressor blade and relating the movement of the throttle to the overall compressor dynamics. As the system identification relays on experimental data, concerns about the noise level and unmodeled system dynamics are addressed by experimenting with two model structures. The identification algorithm entails a heuristic optimization that allows for inspection of the results with respect to unmodeled system dynamics. The results of the proposed system identification algorithm show that the assumed model structure for the system identification algorithm takes on an important role in defining the coupling characteristics. A new measure for the flow state in the blade passage is proposed and used in characterizing the dynamics at the tip of the compressor blade, which allows for the inspection of the limits for the utilized actuation.

Identification of non-uniformly sampled-data systems with asynchronous input and output data

This paper considers the identification problem of non-uniformly sampled-data (NUSD) systems with asynchronous input and output data. By using the lifting technique, the lifted transfer function (L-TF) model of the asynchronous NUSD systems is derived. Furthermore, an auxiliary model based recursive least squares (AM-RLS) algorithm is developed to directly identify the L-TF model. In order to avoid the causality constraint problem and improve the computational efficiency, a coupled AM-RLS algorithm is proposed to identify the subsystems of the L-TF model. The effectiveness of the proposed identification algorithms is validated by two simulation examples.

On the Identification of SM and Alamouti-Coded SC-FDMA Signals: A Statistical-Based Approach

Signal identification represents the task of a receiver to identify the signal type and its parameters, with applications to both military and commercial communications. In this paper, we investigate the identification of spatial multiplexing (SM) and Alamouti (AL) space-time block code (STBC) with single carrier frequency division multiple access (SC-FDMA) signals, when the receiver is equipped with a single antenna. We develop a discriminating feature based on a fourth-order statistic of the received signal, as well as a constant false alarm rate decision criterion which relies on the statistical properties of the feature estimate. Furthermore, we present the theoretical performance analysis of the proposed identification algorithm. The algorithm does not require channel or noise power estimation, modulation classification, and block synchronization. Simulation results show the validity of the proposed algorithm, as well as a very good agreement with the theoretical analysis.

Wideband Compensation of RF Vector Multiplier for RF Predistortion Systems

This brief presents a wideband compensation method that addresses the hardware imperfections exhibited in an RF vector multiplier (RF-VM) used for RF predistortion systems. A wideband RF-VM model is first developed to embed the various sources of imperfections in an RF-VM. A compensation method is then presented, followed by a newly proposed identification algorithm to determine the parameters of the compensation modules. Experimental results indicated that the use of the proposed compensation method can lead to an improved error vector magnitude of up to 10 dB, when the RF-VM generates a synthesized predistortion signal of 150-MHz modulation bandwidth. Furthermore, an improvement up to 3.9 dB in the adjacent channel leakage ratio was obtained when using the compensated RF-VM to linearize solid-state power amplifiers driven by wideband communication signals.

Iterative method for frequency domain identification of continuous processes with delay time

AbstractDelay time that affects the performances of many control synthesis techniques in controlled systems is common in most chemical industries. Estimating the delay time is also a difficult problem in identification fields. In this paper, aiming at continuous processes with delay time, an iterative least square identification method is proposed in the frequency domain. By introducing a truncated first‐order Taylor expansion, the delay time term is linearized. Linear regression equation for least squares (LS) is directly derived from the transfer function whose nonlinear term is replaced by a linear one. For reducing the linear approximation error and getting more accurate estimations of the model parameters, an iterative algorithm is developed based on the LS. Moreover, the proposed method can be easily extended to a closed‐loop system without increasing the order of the identified model. Simulations verify the effectiveness, fast convergence rates, and robustness of the proposed identification algorithm.

Broadband PLC‐channel equalisation in the frequency domain based on complementary sequences

Multipath channels, like power lines, have a periodic time-variant response that impacts on data transmission. Power Line Communications (PLC) performance, in the context of a cyclic-prefix single carrier modulation scheme, can benefit from frequency domain equalisation techniques. This study proposes a frequency-domain dynamic characterisation and equalisation algorithm based on the properties of complementary sequences (CSs). This proposal takes advantage of CS properties to reduce the complexity of the algorithm by performing all the operations in the frequency domain, and without the necessity of noise/variance estimators. The proposal is compared to some well-known methods like zero forcing and minimum mean-square error (MMSE) through the transmission of data under different PLC channels. Bit error rate (BER) is also measured using Middleton Class A impulse noise (IN) model and the performance of all methods is finally evaluated under a time-variant PLC channel model showing the importance of dynamic equalisation on PLC systems. Reported computational resources and simulations show that the proposal is four time faster than MMSE and improves the BER performance by up to 4 dB. In addition, the proposed identification algorithms shows to work properly even in PLC channels with attenuations higher than 40 dB and severe IN scenarios.

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites

This paper considers the identification algorithm of parameters included in a parabolic law that is often used to predict the time dependence of the thickness of the interfacial layers in the structure of composite materials based on a metal matrix. The incubation period of the process and the speed of reaction and pressure are taken into account. The proposed algorithm of identification is based on the introduction of a minimized objective function of a special kind. The problem of identification of unknown parameters in the parabolic law is formulated in a variational form. The authors of the paper have determined the desired parameters, under which the objective function has a minimum value. It is shown that on the basis of four known experimental values of the interfacial layer thickness, corresponding to different values of temperature, pressure and the time of the interfacial layer growth, it is possible to identified four model parameters. They are the activation energy, a pre-exponential parameter, the delay time of the start of the interfacial layer formation, and the parameter determining the pressure effect on the rate of interfacial layer growth. The stability of the proposed identification algorithm is also studied.

Blind multivariable ARMA subspace identification

In this paper, we study the deterministic blind identification of multiple channel state-space models having a common unknown input using measured output signals that are perturbed by additive white noise sequences. Different from traditional blind identification problems, the considered system is an autoregressive system rather than an FIR system; hence, the concerned identification problem is more challenging but possibly having a wider scope of application. Two blind identification methods are presented for multi-channel autoregressive systems. A cross-relation identification method is developed by exploiting the mutual references among different channels. It requires at least three channel systems with square and stably invertible transfer matrices. Moreover, a general subspace identification method is developed for which two channel systems are sufficient for the blind identification; however, it requires the additive noises to have identical variances and the transfer matrices having no transmission zeros. Finally, numerical simulations are carried out to demonstrate the performance of the proposed identification algorithms.