Browsing by Subject "System identification"
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Item Open Access Application of Gauss-Seidel method and singular value decomposition techniques to recursive least squares algorithm(Bilkent University, 1991) Malaş, AtillaSystem identification algorithms are utilized in many practical and theoretical applications such as parameter estimation of sj'stems, adaptive control and signal processing . Least squares algorithm is one of the most popular algorithms in system identification, but it has some drawbacks such as large time consumption and small convergence rates. In this thesis, Gauss-Seidel method is implemented on recursive least squares algorithm and convergence behaviors of the resultant algorithms are analyzed. .Also in standard recursive least squares algorithm the excitation of modes are monitored using data matrices and this algorithm is accordingly altered. A parallel scheme is proposed in this analysis for efficient computation of the modes. The simulation results are also presented.Item Open Access Comparative study of an EKF-based parameter estimation and a nonlinear optimization-based estimation on PMSM system identification(MDPI AG, 2021-09-25) Sel, Artun; Sel, Bilgehan; Coskun, Umit; Kasnakoğlu, CoskuIn this study, two different parameter estimation algorithms are studied and compared. Iterated EKF and a nonlinear optimization algorithm based on on-line search methods are imple mented to estimate parameters of a given permanent magnet synchronous motor whose dynamics are assumed to be known and nonlinear. In addition to parameters, initial conditions of the dynami cal system are also considered to be unknown, and that comprises one of the differences of those two algorithms. The implementation of those algorithms for the problem and adaptations of the methods are detailed for some other variations of the problem that are reported in the literature. As for the computational aspect of the study, a convexity study is conducted to obtain the spherical neighborhood of the unknown terms around their correct values in the space. To obtain such a range is important to determine convexity properties of the optimization problem given in the estimation problem. In this study, an EKF-based parameter estimation algorithm and an optimization-based method are designed for a given nonlinear dynamical system. The design steps are detailed, and the efficacies and shortcomings of both algorithms are discussed regarding the numerical simulations.Item Open Access Exact blind channel estimator(Bilkent University, 1998) Özdemir, Ahmet KemalRecently blind identification of single-input multiple-output (SIMO) FIR channels has received considerable attention. The obtained exact identification approaches place over-restrictive constraints on the channels. In this thesis least set of constraints on the channels are placed and the noise-free blind channel identification problem is solved in two stages: The identification of the uncommon zeros followed by the identification of the common zeros of the channels. The minimum number of samples required to identify the uncommon zeros is specified, and closed form solutions are obtained. Also a binary-tree algorithm is proposed for the computation of the uncommon zeros efficiently. Then the common zeros of the channels are identified by a novel pruning algorithm. Finally a simulation example is presented to illustrate these ideas.Item Open Access Frequency-domain subspace ıdentification of linear time periodic (LTP) systems(Institute of Electrical and Electronics Engineers, 2019) Uyanik, I.; Saranli, U.; Ankaralı, M.; Cowan, N. J.; Morgül, ÖmerThis note proposes a new methodology for subspace-based state-space identification for linear time-periodic (LTP) systems. Since LTP systems can be lifted to equivalent linear time-invariant (LTI) systems, we first lift input-output data from the unknown LTP system as if it was collected from an equivalent LTI system. Then, we use frequency-domain subspace identification methods to find an LTI system estimate. Subsequently, we propose a novel method to obtain a time-periodic realization for the estimated lifted LTI system by exploiting the specific parametric structure of Fourier series coefficients of the frequency-domain lifting method. Our method can be used to both obtain state-space estimates for unknown LTP systems as well as to obtain Floquet transforms for known LTP systems. IEEEItem Open Access Identification of a vertical hopping robot model via harmonic transfer functions(Sage Publications Ltd., 2016) Uyanık, İ.; Ankaralı, M. M.; Cowan, N. J.; Saranlı U.; Morgül, Ö.A common approach to understanding and controlling robotic legged locomotion is the construction and analysis of simplified mathematical models that capture essential features of locomotor behaviours. However, the representational power of such simple mathematical models is inevitably limited due to the non-linear and complex nature of biological locomotor systems. Attempting to identify and explicitly incorporate key non-linearities into the model is challenging, increases complexity, and decreases the analytic utility of the resulting models. In this paper, we adopt a data-driven approach, with the goal of furnishing an input–output representation of a locomotor system. Our method is based on approximating the hybrid dynamics of a legged locomotion model around its limit cycle as a Linear Time Periodic (LTP) system. Perturbing inputs to the locomotor system with small chirp signals yield the input–output data necessary for the application of LTP system identification techniques, allowing us to estimate harmonic transfer functions (HTFs) associated with the local LTP approximation to the system dynamics around the limit cycle. We compare actual system responses with responses predicted by the HTF, providing evidence that data-driven system identification methods can be used to construct models for locomotor behaviours.Item Open Access Independent estimation of input and measurement delays for a hybrid vertical spring-mass-damper via harmonic transfer functions(IFAC, 2015-06) Uyanık, İsmail; Ankaralı, M. M.; Cowan, N. J.; Saranlı, U.; Morgül, Ömer; Özbay, HitaySystem identification of rhythmic locomotor systems is challenging due to the time-varying nature of their dynamics. Even though important aspects of these systems can be captured via explicit mechanics-based models, it is unclear how accurate such models can be while still being analytically tractable. An alternative approach for rhythmic locomotor systems is the use of data-driven system identification in the frequency domain via harmonic transfer functions (HTFs). To this end, the input-output dynamics of a locomotor behavior can be linearized around a stable limit cycle, yielding a linear, time-periodic system. However, few if any model-based or data-driven identification methods for time-periodic systems address the problem of input and measurement delays in the system. In this paper, we focus on data-driven system identification for a simple mechanical system and analyze its dynamics in the presence of input and measurement delays using HTFs. By exploiting the way input delays are modulated by the periodic dynamics, our results enable the separate, independent estimation of input and measurement delays, which would be indistinguishable were the system linear and time invariant. © 2015, IFAG.Item Open Access Nonlinear identification and optimal feedforward friction compensation for a motion platform(Elsevier, 2020) Güç, Ahmet Furkan; Yumrukçal, Z.; Özcan, OnurIn this study, we present a method of nonlinear identification and optimal feedforward friction compensation for an industrial single degree of freedom motion platform. The platform has precise reference tracking requirements while suffering from nonlinear dynamic effects, such as friction and backlash in the driveline. To eliminate nonlinear dynamic effects and achieve precise reference tracking, we first identified the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function (HOSIDF) based system identification. Next, we present optimal feedforward compensation design to improve reference tracking performance. We modeled the friction using the Stribeck model and identified its parameters through a procedure including a special reference signal and the Nelder–Mead algorithm. Our results show that the RMS trajectory tracking error decreased from 0.0431 deg/s to 0.0117 deg/s when the proposed nonlinear identification and friction compensation method is utilized.Item Open Access Parameter optimized controller design based on frequency domain identification(Bilkent University, 1995) Köroğlu, HakanRecently, there has been a great tendency towards the development of iterative design methodologies combining identification with control in a mutually supportive fashion. In this thesis, we develop such an algorithm utilizing nonparametric frequency domain identification methods in order to realize the online iterative design of parameter optimized controllers for a system of unknown dynamics. The control design is based on the minimization of LQG (Linear Quadratic Gaussian) cost criterion with a two-degree of freedom control system. This is achieved by the approximation of an optimality relation, which is derived for a particular parametrization of one of the controllers, using the frequency domain transfer function estimates and application of this together with a numerical optimization algorithm. It is shown that, if the first controller is a FIR filter of length greater than or equal to two times the number of frequencies present in the reference input, the designed control system is optimal independent of the stabilizing second controller.