Identification of an Induction Motor by a Microcomputer based Structure

Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity

Mechatronic systems identification using an impulse response recursive algorithm

MIMO system identification is a fundamental concern in a variety of applications. Various iterative and recursive MIMO system identification algorithms exist in the literature. The iterative algorithms suffer from high computational cost due to large matrix computations and recursive algorithms suffer from slow convergence speed. This paper proposes a fast recursive exact least squares algorithm for MIMO system identification with fast convergence speed and low computational cost. The recursions of the algorithm give rise to a lattice structure. The lattice structure is less sensitive to round-off errors, coefficients variations and can also be used for model order reduction. Although in this work we estimate an FIR (finite impulse response) model of the MIMO system, the framework can also be used for IIR (infinite impulse response) models, and for estimating linear periodically time varying (LPTV) and multivariable systems. The theory proposed is validated using simulation results.

A recursive algorithm for sampling properties of physical clusters such as size distribution and percolation is presented. The approach can be applied to any system with periodic boundary conditions, given a spatial definition of a cluster. We also introduce some modifications in the algorithm that increases the efficiency considerably if one is only interested in percolation analysis. The algorithm is implemented in Fortran 90 and is compared with a number of iterative algorithms. The recursive cluster identification algorithm is somewhat slower than the iterative methods at low volume fraction but is at least as fast at high densities. The percolation analysis, however, is considerably faster using recursion, for all systems studied. We also note that the CPU time using recursion is independent on the static allocation of arrays, whereas the iterative method strongly depends on the size of the initially allocated arrays.

Recursive prediction error identification and scaling of non-linear state space models using a restricted black box parameterization

The matrix inversion lemma gives an explicit formula of the inverse of a positive-definite matrix A added to a block of dyads (represented as BBH). It is well-known in the literature that this formula is very useful to develop a block-based recursive least-squares algorithm for the block-based recursive identification of linear systems or the design of adaptive filters. We already extended this result to the case when the matrix A is singular, and presented the matrix pseudo-inversion lemma. Such a singular case may occur in a situation where a given problem is overdetermined in the sense that it has more equations than unknowns. In this paper, based on these results, we propose a block-based adaptive multichannel super-exponential deflation algorithm. We present simulation results for the performance of the block-based algorithm in order to show the usefulness of the matrix pseudo-inversion lemma.

In adaptive filters, several recursive algorithms have been used to track state-space model vectors in nonstationary environments. So far, kernel recursive algorithms showed the best results in this regard. With this letter, we aim to propose an algorithm based on a nonlinear function of the error, motivated by the extended recursive least-squares algorithm. Simulations were performed on the problem of tracking a nonlinear Rayleigh fading multipath channel and on a system identification. The results showed that the proposed algorithm can overcome the extended kernel version ones.

Microcomputer based data acquisition systems for centrifuge modeling : Shen, C K; Li, X S; Kim, Y S Geotech Test JV7, N4, Dec 1984, P200–204

Investigation of High Impedance Faults has long held a high priority at Pennsylvania Power & Light Company (PP& L). The Ratio Ground Relay has been developed in conjunction with Westinghouse Electric Corporation's Advanced Systems Technology and Relay Instrument Divisions under PP&L sponsorship. As part of the research, six feeders were digitally modeled [1], one feeder was modeled on an analog computer [2], and staged fault tests [3] were conducted to gain data and observe relay operations under controlled conditions. Thorough development and analysis of the applicability of a new relaying technique requires long-term field testing. To ensure that the devices are performing according to design specifications, they must be monitored. This paper describes the analysis of four such monitoring schemes and discusses the unique application of commercially available home microcomputer equipment within the substation environment to automatically sample, record and transmit circuit data to a host microcomputer. It also describes the computer configuration, programming, and data transfer to the corporate mainframe computer. The Ratio Ground Relay and a conventional Ground Overcurrent Relay have been installed on one feeder from each of six substations to evaluate their performance during normal distribution system daily operation. The development of this unique microcomputer application is discussed in detail.

This paper will discuss a portable microcomputer based data acquisition system for toxicology research. The data acquisition system and the companion data base were designed and built by a team of individuals at Battelle, which consisted of Barry Brownstein, Jack Webb, Robert Weiner, and myself. The remainder of this paper will describe the factors which affected the design of the system, as well as details regarding the system's implementation. We will first examine the motivation for such a system.

This paper presents a microcomputer based data acquisition system and an analysis of pressure fluctuations in the range of weak hydraulic jumps. Statistical analysis of data shows that the distribution of pressure fluctuations follows the Gaussian probability density function. The maximum pressure fluctuation amplitude is found to be in the range of 2.5 to 3.5 times the standard deviation. The spectral analysis of data indicates that large eddies in the low frequency range of 0 to 20 Hz are predominant over the small eddies in the high frequencies. This result is also predicted by the empirical correlation coefficient. Based on cross correlation analysis, the relationship between areal pressure fluctuations and point pressure fluctuations is developed and can be used to prevent over‐designing the strength of the stilling basins. It shows that the areal pressure fluctuations decrease as the jump longitudinal dimension increases.

A very flexible Levinson-type recursion for a class of non-Toeplitz systems of linear equations is demonstrated. A complete solution is expressed as a linear combination of a partial solution and three auxiliary solutions. The class of systems possesses a special structure in that the coefficient matrices can be partitioned into four block Toeplitz submatrices. The number of multiplications and additions required to compute an n-dimensional solution is O(n/sup 2/). The recursion is then applied to multichannel IIR filtering. Specifically, a lattice structure is established for linear minimum mean square error predictors having independently and arbitrarily specified numbers of poles and zeros. Next the recursion is used to develop a fast time and order recursive least-squares algorithm for ARX system identification. The novelty of the algorithm is that it can be used to efficiently determine parameter estimates of a family of ARX models. >

A recursive algorithm for identification of nonlinear dynamic systems with backlash is proposed in this paper. In this method, the backlash, which is a non‐smooth function, is decomposed into a combination of a group of piecewise linearized models so that all the parameters of the backlash can be estimated separately. Moreover, the model of the backlash is embedded into a Hammerstein‐type model. Thus, a pseudo‐Hammerstein model with backlash is constructed. The estimation of the parameters for such a non‐smooth nonlinear system can be implemented through a so‐called recursive general identification algorithm (RGIA). Then, the corresponding convergence analysis of the RGIA for the model with backlash is also investigated. After that, two examples are presented to show the performance of the proposed method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

The recursive stochastic algorithms for estimating the parameters of linear discrete-time dynamic systems in the presence of disturbance uncertainty has been considered in the paper. Problems related to the construction of min-max optimal recursive algorithms are demonstrated. In addition, the robustness of the proposed algorithms has been addressed. Since the min-max optimal solution cannot be achieved in practice, an approximate optimal solution based on a recursive stochastic Newton-Raphson type procedure is suggested. The convergence of the proposed practically applicable robustified recursive algorithm is established theoretically using the martingale theory. Both theoretical and experimental analysis related to the practical robustness of the proposed algorithm are also included. .

The paper presents a new recursive prediction error algorithm for joint identification of a time invariant nonlinear state space model and a delay. The parametrization of the state space model is polynomial. The delay parameter is embedded in the output equation of the continuous time model. This allows the gradient to be computed directly by differentiation, which leads to a recursive algorithm that exploits an integrated recursive correlation for identification of the delay. Multiple dynamic models and gradients, for a selected range of integer delays, are applied for delay discretization. Interpolation between the integer delay models then enables identification of the fractional part of the delay. The algorithm is shown to provide highly accurate results in a numerical example when processing a relatively small data set. The general motivation for the work is the emerging wireless 5G standards, whose latencies are specified to enable new networked feedback control and virtual reality based applications using so called critical machine type communication. To support this development there is a need for nonlinear system identification methods that are able to compensate for the often still significant network delays.