Showing papers on "System identification published in 2015"
Book•
01 Mar 2015
TL;DR: This valuable volume offers a systematic approach to flight vehicle system identification and covers exhaustively the time-domain methodology and addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis.
Abstract: This valuable volume offers a systematic approach to flight vehicle system identification and covers exhaustively the time-domain methodology. It addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis. A pragmatic and balanced account of pros and cons in each case are provided. The book also presents data gathering and model validation and covers both large-scale systems and high-fidelity modeling.
Real world problems dealing with a variety of flight vehicle applications are addressed and solutions are provided. Examples encompass such problems as estimation of aerodynamics, stability, and control derivatives from flight data, flight path reconstruction, nonlinearities in control surface effectiveness, stall hysteresis, unstable aircraft, and other critical considerations.
Beginners, as well as practicing researchers, engineers, and working professionals who wish to refresh or broaden their knowledge of flight vehicle system identification, will find this book highly beneficial. Based on years of experience, the book also provides recommendations for overcoming problems likely to be faced in developing complex nonlinear and high-fidelity models and can help the novice negotiate the challenges of developing highly accurate mathematical models and aerodynamic databases from experimental flight data.
Software that runs under MATLAB® and sample flight data are provided to assist the reader in reworking the examples presented in the text. The software can also be adapted to the reader’s own interests.
435 citations
26 May 2015
TL;DR: Three baseline models are described and it is shown that they are significantly outperformed by the ReLU Network Model in experiments on real data, indicating the power of the model to capture useful structure in system dynamics across a rich array of aerobatic maneuvers.
Abstract: We consider the problem of system identification of helicopter dynamics. Helicopters are complex systems, coupling rigid body dynamics with aerodynamics, engine dynamics, vibration, and other phenomena. Resultantly, they pose a challenging system identification problem, especially when considering non-stationary flight regimes. We pose the dynamics modeling problem as direct high-dimensional regression, and take inspiration from recent results in Deep Learning to represent the helicopter dynamics with a Rectified Linear Unit (ReLU) Network Model, a hierarchical neural network model. We provide a simple method for initializing the parameters of the model, and optimization details for training. We describe three baseline models and show that they are significantly outperformed by the ReLU Network Model in experiments on real data, indicating the power of the model to capture useful structure in system dynamics across a rich array of aerobatic maneuvers. Specifically, the ReLU Network Model improves 58% overall in RMS acceleration prediction over state-of-the-art methods. Predicting acceleration along the helicopter's up-down axis is empirically found to be the most difficult, and the ReLU Network Model improves by 60% over the prior state-of-the-art. We discuss explanations of these performance gains, and also investigate the impact of hyperparameters in the novel model.
175 citations
TL;DR: The sample complexity of various constrained control problems is derived, showing the key role played by the binomial distribution and related tail inequalities, and providing the sample complexity which guarantees that the solutions obtained with SPV algorithms meet some pre-specified probabilistic accuracy and confidence.
162 citations
TL;DR: In this paper, the authors present an integrated framework and software platform that enables the use of model-based tools in design, operational optimisation and advanced control studies for process systems engineering applications.
124 citations
TL;DR: A novel Bayesian real-time system identification algorithm using response measurement is proposed for dynamical systems and is applicable to simultaneous model class selection and parametric identification in the real- time manner.
Abstract: In this article, a novel Bayesian real-time system identification algorithm using response measurement is proposed for dynamical systems. In contrast to most existing structural identification methods which focus solely on parametric identification, the proposed algorithm emphasizes also model class selection. By embedding the novel model class selection component into the extended Kalman filter, the proposed algorithm is applicable to simultaneous model class selection and parametric identification in the real-time manner. Furthermore, parametric identification using the proposed algorithm is based on multiple model classes. Examples are presented with application to damage detection for degrading structures using noisy dynamic response measurement.
111 citations
TL;DR: In this article, an artificial neural network (ANN) model-based system identification method was proposed to model multi-zone buildings, considering the energy input from mechanical cooling, ventilation, weathher change, and convective heat transfer between the adjacent zones.
108 citations
TL;DR: An attempt has been made to model a nonlinear system using a Hammerstein model, which has been trained using a cuckoo search algorithm, which is a recently proposed stochastic algorithm.
Abstract: A novel nonlinear system identification scheme is proposed.A Hammerstein model has been trained using cuckoo search algorithm.The model is a cascade of a FLANN and an adaptive IIR filter.Simulation study shows enhanced modeling capacity of the proposed scheme.The new schemes offers lesser computational time over other methods studied. An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results.
103 citations
TL;DR: A variable projection algorithm is proposed to estimate the model parameters more efficiently by eliminating the linear parameters through the orthogonal projection of RBF-ARX model by substantially reducing the dimension of parameter space.
Abstract: The radial basis function network-based autoregressive with exogenous inputs (RBF-ARX) models have much more linear parameters than nonlinear parameters. Taking advantage of this special structure, a variable projection algorithm is proposed to estimate the model parameters more efficiently by eliminating the linear parameters through the orthogonal projection. The proposed method not only substantially reduces the dimension of parameter space of RBF-ARX model but also results in a better-conditioned problem. In this paper, both the full Jacobian matrix of Golub and Pereyra and the Kaufman’s simplification are used to test the performance of the algorithm. An example of chaotic time series modeling is presented for the numerical comparison. It clearly demonstrates that the proposed approach is computationally more efficient than the previous structured nonlinear parameter optimization method and the conventional Levenberg–Marquardt algorithm without the parameters separated. Finally, the proposed method is also applied to a simulated nonlinear single-input single-output process, a time-varying nonlinear process and a real multiinput multioutput nonlinear industrial process to illustrate its usefulness.
95 citations
TL;DR: The paper shows that, contrary to apparently widely held beliefs, the iterative RIV algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of Box-Jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood, prediction error minimization and instrumental variable terms.
94 citations
TL;DR: The aim of this work is to provide new insights on the stable spline estimator equipped with ML estimation of hyperparameters, and to derive the notion of excess degrees of freedom, which measures the additional complexity to be assigned to an estimator which is also required to determinehyperparameters from data.
93 citations
TL;DR: In this paper, a new procedure to tune a feedforward controller based on measured data obtained in finite time tasks was developed, where a suitable feedforward parametrization was introduced that provided good extrapolation properties for a class of reference signals.
TL;DR: Results show that the O-ESN outperforms the classical feature selection method, least angle regression (LAR) method in that its architecture is simpler than that of LAR.
Abstract: The echo state network (ESN) is a novel and powerful method for the temporal processing of recurrent neural networks. It has tremendous potential for solving a variety of problems, especially real-valued, time-series modeling tasks. However, its complicated topologies and random reservoirs are difficult to implement in practice. For instance, the reservoir must be large enough to capture all data features given that the reservoir is generated randomly. To reduce network complexity and to improve generalization ability, we present a novel optimized ESN (O-ESN) based on binary particle swarm optimization (BPSO). Because the optimization of output weights connection structures is a feature selection problem and PSO has been used as a promising method for feature selection problems, BPSO is employed to determine the optimal connection structures for output weights in the O-ESN. First, we establish and train an ESN with sufficient internal units using training data. The connection structure of output weights, i.e., connection or disconnection, is then optimized through BPSO with validation data. Finally, the performance of the O-ESN is evaluated through test data. This performance is demonstrated in three different types of problems, namely, a system identification and two time-series benchmark tasks. Results show that the O-ESN outperforms the classical feature selection method, least angle regression (LAR) method in that its architecture is simpler than that of LAR.
TL;DR: In this article, an online steady state load identification method is proposed to solve the problems related to frequency drift, system robustness deterioration, difficulties in controller design due to the uncertainties in load and mutual inductance variations of an inductive power transfer (IPT) system.
Abstract: An online steady-state load identification method is proposed to solve the problems related to frequency drift, system robustness deterioration, difficulties in controller design due to the uncertainties in load and mutual inductance variations of an inductive power transfer (IPT) system. Take a Series-Series-type IPT system as an example, an additional capacitor is added into the system to make the system work in two operating modes, and a mathematical model is established according to the two modes for the system identification. Simulation and experimental results have verified the proposed online load identification method. It has demonstrated that the method is accurate and reliable for identifying uncertain loads and magnetic coupling variations if other system parameters are known. The method can be used to improve the system performance with precise control.
TL;DR: In this article, the authors discuss the importance and relevance of direct continuous-time system identification and how this relates to the solution for model identification problems in practical applications and discuss the software tools available and illustrate their advantages via simulated and real data examples.
TL;DR: An adaptive control algorithm is presented for nonlinear vibration control of large structures subjected to dynamic loading based on integration of a self-constructing wavelet neural network developed specifically for structural system identification with an adaptive fuzzy sliding mode control approach.
TL;DR: In this paper, a new modelling methodology was developed to combine the fidelity of CFD models with the computational attractiveness of BEM-type models, which can give representative linear models, or be extended into the nonlinear domain, as desired.
TL;DR: In this article, a novel outlier-resistant extended Kalman filter (OR-EKF) is proposed for outlier detection and robust online structural parametric identification using dynamic response data possibly contaminated with outliers.
Abstract: Structural health monitoring (SHM) using dynamic response measurement has received tremendous attention over the last decades. In practical circumstances, outliers may exist in the measurements that lead to undesirable identification results. Therefore, detection and special treatment of outliers are important. Unfortunately, this issue has rarely been taken into systematic consideration in SHM. In this paper, a novel outlier-resistant extended Kalman filter (OR-EKF) is proposed for outlier detection and robust online structural parametric identification using dynamic response data possibly contaminated with outliers. Instead of definite judgment on the outlierness of a data point, the proposed OR-EKF provides the probability of outlier for the measurement at each time step. By excluding the identified outliers, the OR-EKF ensures the stability and reliability of the estimation. In the illustrative examples, the OR-EKF is applied to parametric identification for structural systems with time-varyin...
TL;DR: A new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed, to insert a penalty of block-sparsity, which is a mixed l2, 0 norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm.
Abstract: In order to improve the performance of least mean square (LMS)-based adaptive filtering for identifying block-sparse systems, a new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed in this paper. The basis of the proposed algorithm is to insert a penalty of block-sparsity, which is a mixed l2, 0 norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm. To describe a block-sparse system response, we first propose a Markov-Gaussian model, which can generate a kind of system responses of arbitrary average sparsity and arbitrary average block length using given parameters. Then we present theoretical expressions of the steady-state misadjustment and transient convergence behavior of BS-LMS with an appropriate group partition size for white Gaussian input data. Based on the above results, we theoretically demonstrate that BS-LMS has much better convergence behavior than l0-LMS with the same small level of misadjustment. Finally, numerical experiments verify that all of the theoretical analysis agrees well with simulation results in a large range of parameters.
18 Dec 2015
TL;DR: An overview of the state of the art of technical means to achieve camera model identification can be found in this article, where an introduction to forensic source identification is presented. And the role of model-specific characteristics in device-level identification is discussed.
Abstract: This chapter presents an overview of the state of the art of technical means to achieve camera model identification. It starts with an introduction to forensic source identification. The chapter deals with a specific case of digital camera model identification. It comments on suitable image datasets for setting up practical algorithms, the foundations, and focuses on problems that arise in identification scenarios with unknown camera models. The chapter explains the connections between camera model identification and device identification. It talks about the open set camera model identification that poses an inherent challenge to the widely used multi-class support vector machines (SVMs), as they always assign a test sample to one of the trained classes. The role of model-specific characteristics in device-level identification is discussed. As forensic camera model identification finds applications in practical investigations, a more explicit treatment of the different types of artifact interdependencies will gain more relevance.
TL;DR: The authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty.
Abstract: In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories.
TL;DR: This work details the Bayesian identification of a nonlinear dynamical system using a novel MCMC algorithm: ‘Data Annealing’, which allows the Markov chain to easily clear ‘local traps’ in the target distribution.
TL;DR: The identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the network is considered, and a flexible choice of which internal variables need to be measured in order to identify the module of interest is considered.
TL;DR: Simulation studies and comprehensive comparisons are conducted, and demonstrate that the GEBF-FNN-based model of tanker motion dynamics achieves superior performance in terms of both approximation and prediction.
Abstract: In this paper, the motion dynamics of a large tanker is modeled by the generalized ellipsoidal function-based fuzzy neural network (GEBF-FNN). The reference model of tanker motion dynamics in the form of nonlinear difference equations is established to generate training data samples for the GEBF-FNN algorithm which begins with no hidden neuron. In the sequel, fuzzy rules associated with the GEBF-FNN-based model can be online self-constructed by generation criteria and parameter estimation, and can dynamically capture essential motion dynamics of the large tanker with high prediction accuracy. Simulation studies and comprehensive comparisons are conducted on typical zig-zag maneuvers with moderate and extreme steering, and demonstrate that the GEBF-FNN-based model of tanker motion dynamics achieves superior performance in terms of both approximation and prediction.
TL;DR: This paper brings several representative algorithms together, developed by the authors and their colleagues, to form an easily referenced archive for promotion of the awareness, tutorial, applications, and even further research expansion.
Abstract: This paper is a summary of the research development in the rational total nonlinear dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined as those where the model parameters and input controller outputs are subject to nonlinear to the output. Previously, this class of models has been known as rational models, which is a model that can be considered to belong to the nonlinear autoregressive moving average with exogenous input NARMAX model subset and is an extension of the well-known polynomial NARMAX model. The justification for using the rational model is that it provides a very concise and parsimonious representation for highly complex nonlinear dynamic systems and has excellent interpolatory and extrapolatory properties. However, model identification and controller design are much more challenging compared to the polynomial models. This has been a new and fascinating research trend in the area of mathematical modelling, control, and applications, but still within a limited research community. This paper brings several representative algorithms together, developed by the authors and their colleagues, to form an easily referenced archive for promotion of the awareness, tutorial, applications, and even further research expansion.
TL;DR: To guarantee the convergence and to speed up the process of the on-line training algorithm, the optimal learning rate (OLR) is introduced based on Lyapunov stability theory and the modifications allow the SLFRWNN to be much faster than FWNN and hence it is more appropriate in real-time applications.
TL;DR: The TMCMC method is a Bayesian model updating technique which not only finds the most plausible model parameters but also estimates the probability distribution of those parameters given the data measured at the laboratory.
TL;DR: It is indicated that Bayesian model class selection may lead to over-confidence in certain model classes, resulting in biased extrapolation, in terms of parameter-identification robustness and extrapolation accuracy.
26 May 2015
TL;DR: A data-driven mixture-of-experts learning approach using Gaussian processes that accurately predicts the joint torques resulting from contact forces, is robust to changes in the environment and outperforms existing dynamic models that use of force/ torque sensor data.
Abstract: In whole-body control, joint torques and external forces need to be estimated accurately. In principle, this can be done through pervasive joint-torque sensing and accurate system identification. However, these sensors are expensive and may not be integrated in all links. Moreover, the exact position of the contact must be known for a precise estimation. If contacts occur on the whole body, tactile sensors can estimate the contact location, but this requires a kinematic spatial calibration, which is prone to errors. Accumulating errors may have dramatic effects on the system identification. As an alternative to classical model-based approaches we propose a data-driven mixture-of-experts learning approach using Gaussian processes. This model predicts joint torques directly from raw data of tactile and force/torque sensors. We compare our approach to an analytic model-based approach on real world data recorded from the humanoid iCub. We show that the learned model accurately predicts the joint torques resulting from contact forces, is robust to changes in the environment and outperforms existing dynamic models that use of force/ torque sensor data.
TL;DR: An important contribution of this paper is the fact that, although the model is strictly linear, it can change its parameters as a function of the operation point of the vehicle to represent the engine's and the transmission's nonlinear behaviors.
Abstract: This paper presents the model identification and the velocity control of an autonomous car. The control system was designed so that the car is controlled at low speeds, where the main applications for the vehicle's autonomous operations include parking and urban adaptive cruise control. A longitudinal model of the car was used in the control loop to compensate the nonlinear behavior of its dynamics. Since the determination of the vehicle's model is a difficult step in the design of model-based controllers, the main contribution of this paper is the use of an empirically determined model to this end. In this paper, the structure of the model was conceived from the car's physics equations, but its parameters were estimated using data-based identification techniques. An important contribution of this paper is the fact that, although the model is strictly linear, we can change its parameters as a function of the operation point of the vehicle to represent the engine's and the transmission's nonlinear behaviors. Moreover, in this paper, we propose a way to include changes in the longitudinal dynamics caused by the automatic gear shifting. The validation of the proposed controller was conducted by computer simulations and real-world experiments.
TL;DR: In this paper, a Lyapunov-based EMPC (LEMPC) is designed with a linear empirical model that allows for closed-loop stability guarantees in the context of nonlinear chemical processes.
Abstract: Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based EMPC (LEMPC) is designed with a linear empirical model that allows for closed-loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed-loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J, 61: 816–830, 2015