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System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.


Papers
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Journal ArticleDOI
TL;DR: The estimated ARX model parameters are shown to converge exponentially to their true values under a suitable persistence of excitation condition on a projection of the embedded input/output data.

128 citations

Dissertation
01 Jan 2003
TL;DR: In this article, a neural network-based predictive controller is proposed for non-linear control of a coupled tank system and an inverse pendulum system, where the controller is trained on simulation runs of the plant.
Abstract: This thesis addresses two neural network based control systems. The first is a neural network based predictive controller. System identification and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two different plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant. The controller design includes the GPC parameters, but prediction is done explicitly by using a neural network model of the plant. System identification is discussed. Two control systems are constructed for two different plants: A coupled tank system and an inverse pendulum. This shows how implementation aspects such as plant excitation during system identification are handled. Limitations of the controller type are discussed and shown on the two implementations. In the second part of this thesis, the direct neural network controller is discussed. An output feedback controller is constructed around a neural network. Controller parameters are determined using system simulations. The control system is applied as a single-step ahead controller to two different plants. One of them is a path-following problem in connection with a reversing trailer truck. This system illustrates an approach with step-wise increasing controller complexity to handle the unstable control object. The second plant is a coupled tank system. Comparison is made with the first controller. Both controllers are shown to work. But for the neural network based predictive controller, construction of a neural network model of high accuracy is critical especially when long prediction horizons are needed. This limits application to plants that can be modelled to sufficient accuracy. The direct neural network controller does not need a model. Instead the controller is trained on simulation runs of the plant. This requires careful selection of training scenarios, as these scenarios have impact on the performance of the controller.

127 citations

Posted Content
TL;DR: This work presents a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models and places a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena.
Abstract: State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

127 citations

Journal ArticleDOI
TL;DR: This paper introduces a new ANC algorithm suitable for single-tone noises as well as some specific narrowband noises that does not require the identification of the secondary path, though its convergence can be very slow in some special cases.
Abstract: Active noise control (ANC) has been widely applied in industry to reduce environmental noise and equipment vibrations. Most available control algorithms require the identification of the secondary path, which increases the control system complexity, contributes to an increased residual noise power, and can even cause the control system to fail if the identified secondary path is not sufficiently close to the actual path. In this paper, based on the geometric analysis and the strict positive real (SPR) property of the filtered-x LMS algorithm, we introduce a new ANC algorithm suitable for single-tone noises as well as some specific narrowband noises that does not require the identification of the secondary path, though its convergence can be very slow in some special cases. We are able to extend the developed ANC algorithm to the case of active control of broadband noises through our use of a subband implementation of the ANC algorithm. Compared to other available control algorithms that do not require secondary path identification, our developed method is simple to implement, yields good performance, and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm

127 citations

DissertationDOI
01 Jan 1978
TL;DR: In this article, the problem of determining linear models of structures from seismic response data is studied using ideas from the theory of system identification, in which optimal estimates of the model parameters are obtained by minimizing a selected measure-of-fit between the responses of the structure and the model.
Abstract: The problem of determining linear models of structures from seismic response data is studied using ideas from the theory of system identification. The investigation employs a general formulation called the output-error approach, in which optimal estimates of the model parameters are obtained by minimizing a selected measure-of-fit between the responses of the structure and the model. The question of whether the parameters can be determined uniquely and reliably in this way is studied for a general class of linear structural models. Because earthquake records are normally available from only a small number of locations in a structure, and because of measurement noise, it is shown that it is necessary in practice to estimate parameters of the dominant modes in the records, rather than the stiffness and damping matrices. Two output-error techniques are investigated. Tests of the first, an optimal filter method, show that its advantages are offset by weaknesses which make it unsatisfactory for application to seismic response. A new technique, called the modal minimization method, is developed to overcome these difficulties. It is a reliable and efficient method to determine the optimal estimates of modal parameters for linear structural models. The modal minimization method is applied to two multi- story buildings that experienced the 1971 San Fernando earthquake. New information is obtained concerning the properties of the higher modes of the taller building and more reliable estimates of the properties of the fundamental modes of both structures are found. The time-varying character of the equivalent linear parameters is also studied for both buildings. It is shown for the two buildings examined that the optimal, time-invariant, linear models with a small number of modes can reproduce the strong-motion records much better than had been supposed from previous work using less systematic techniques.

127 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023177
2022361
2021646
2020813
2019804
2018862