A new kernel-based approach to system identification with quantized output data

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the so-called Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectation-maximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the state-of-the-art kernel-based methods when these are employed in system identification with quantized data.Comment: 10 pages, 4 figure

Fixed-order FIR approximation of linear systems from quantized input and output data

Abstract The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example

On the Equivalence between Integer-and Fractional Order-Models of Continuous-Time and Discrete-Time ARMA Systems

Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.The equivalence of continuous-/discrete-time autoregressive-moving average (ARMA) systems is considered in this paper. For the integer-order cases, the interrelations between systems defined by continuous-time (CT) differential and discrete-time (DT) difference equations are found, leading to formulae relating partial fractions of the continuous and discrete transfer functions. Simple transformations are presented to allow interconversions between both systems, recovering formulae obtained with the impulse invariant method. These transformations are also used to formulate a covariance equivalence. The spectral correspondence implied by the bilinear (Tustin) transformation is used to study the equivalence between the two types of systems. The general fractional CT/DT ARMA systems are also studied by considering two DT differential fractional autoregressive-moving average (FARMA) systems based on the nabla/delta and bilinear derivatives. The interrelations CT/DT are also considered, paying special attention to the systems defined by the bilinear derivatives.publishersversionpublishe

Variability analysis of engine idle vibration

Vibration in motor vehicles is largely influenced by the engine and thus has become the focus of much automotive testing. Engine idle vibration is focused on since deviations in the vibration signature are prevalent at this operating condition. The objective of this thesis was to derive a best-practice method for the analysis of engine idle vibration. Variability of the engine vibration signatures was calculated through the implementation of multiple analysis techniques. These methods included: angle domain analysis, the fast Fourier transform, the discrete cosine transform, the moving average model, and the auto-regressive moving average model. Also included in the investigation were examinations of data normalization, detrending, and filtration. The results of the analyses were then evaluated with reference to the correlation between similar engines and the identification of outliers. It was found that the fast Fourier transform analysis technique provided the best overall results. The moving average model and the auto-regressive moving average models were also identified as methods that have great potential in vibration analysis but are limited by their computational intensity

Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system

An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts’ Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies