Keywords: Data-driven Control, Regularization and Kernel Methods, Other Applications
Abstract: Data-driven control that circumvents the process of system identification by providing optimal control inputs directly from system data has attracted renewed attention in recent years. In this paper, we focus on understanding the effects of the regularization on the data-enabled predictive control (DeePC) algorithm. We provide theoretical motivation and interpretation for including a quadratic regularization term. Our analysis shows that the quadratic regularization term leads to robust and optimal solutions with regards to disturbances affecting the data. Moreover, when the input/output constraints are inactive, the quadratic regularization leads to a closed-form solution of the DeePC algorithm and thus enables fast calculations. On this basis, we propose a framework for data-driven synchronization and power regulations of power converters, which is tested by high-fidelity simulations and experiments.

Keywords: Subspace Methods, Data-driven Control, Filtering and Smoothing
Abstract: The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise-free matrix. In this paper, recent results in matrix denoising by singular value shrinkage are reviewed. A novel approach is proposed to solve the low-rank Hankel matrix denoising problem by using an iterative algorithm in structured low-rank approximation modified with data-driven singular value shrinkage. It is shown numerically in both the input-output trajectory denoising and the impulse response denoising problems, that the proposed method performs the best in terms of estimating the noise-free matrix among existing algorithms of low-rank matrix approximation and denoising.

Keywords: Data-driven Control, Dynamic Network Identification, Identification for Control
Abstract: The data-driven synthesis of a distributed controller in the presence of noise is considered, via the distributed virtual reference feedback tuning (DVRFT) framework. The analysis is performed for a linear interconnected system on an arbitrary graph that is subject to unmeasured exogenous inputs. By solving a dynamic network identification problem with prediction-error filtering and a tailor-made noise model, we show that the distributed model-reference control problem can be solved directly from data. Sufficient conditions are provided for which the local controller estimates are consistent. Moreover, it is shown how the method can be applied in the single-input-single-output case, leading to consistent estimates with standard virtual reference feedback tuning as well. The effectiveness of the method is demonstrated via a small network example with two interconnected systems.

Keywords: Data-driven Control, Continuous Time System Estimation
Abstract: We investigate stability analysis and controller design of unknown continuous-time systems under state-feedback with aperiodic sampling, using only noisy data but no model knowledge. We first derive a novel data-dependent parametrization of all linear time-invariant continuous-time systems which are consistent with the measured data and the assumed noise bound. Based on this parametrization and by combining tools from robust control theory and the time-delay approach to sampled-data control, we compute lower bounds on the maximum sampling interval (MSI) for closed-loop stability under a given state-feedback gain, and beyond that, we design controllers which exhibit a possibly large MSI. Our methods guarantee the stability properties robustly for all systems consistent with the measured data. As a technical contribution, the proposed approach embeds existing methods for sampled-data control into a general robust control framework, which can be directly extended to model-based robust controller design for uncertain time-delay systems under general uncertainty descriptions.

Keywords: Data-driven Control, Nonparametric Methods, Frequency Domain Identification
Abstract: Data-driven model reference control allows for the design of a controller from input and output data when a parametric model of the system is not available. In this work we propose to use advanced nonparametric frequency response function estimation methods to aid in the model reference control task. This allows for a convenient way to extend model reference control to continuous-time systems. Moreover, we also outline a procedure that implements frequency weighing to achieve the Cramér-Rao lower bound in the case that the ideal controller is realizable and in the case that the input is also perturbed by noise. The proposed methods are used to design an analog controller for a continuous-time system.

Keywords: Data-driven Control, Machine Learning and Data Mining, Process Control
Abstract: The rationale of shifting towards green energy, along with the cost reduction and the increasing capacity of lithium-ion batteries, has motivated the end-users to go for energy storage systems integrated with solar technology solutions. Such systems provide the end-users with greater flexibility, thereby enhancing their role as prosumers in a range of grid-management programs. In this regard, we consider a residential household equipped with a battery and photovoltaic panels, collectively known as the photovoltaic-battery (PV-B) system. We further learn (off-line) a deterministic sub-optimal policy for charging/discharging of the residential battery using an actor-critic reinforcement learning based method. Such proposed approach, named polynomial deterministic policy gradient (PDPG), does not require any model of the system and uses polynomials as function approximator, as opposed to conventional neural networks. The usefulness of the proposed approach is tested on real power data (demand and PV generation) of a residential household in Australia. Numerical simulations indicate that the proposed PDPG algorithm outperforms the OFFON control approach in terms of electricity bill savings and the model-based receding horizon control in terms of computation time.

Keywords: Process Control, Machine Learning and Data Mining, Regularization and Kernel Methods

Keywords: Machine Learning and Data Mining, Process Control, Algorithms
Abstract: Accurate modeling of industrial processes is an important topic in process systems engineering for further anomaly detection and fault diagnosis. Dynamics is inevitable in these processes, and several dynamic variants were proposed in the literature to extract both cross-correlations and auto-correlations between process variables and quality variables. However, all of them focus on the auto-correlations in process variables only, while the valuable auto-regressive information between collected quality variables is ignored. In this paper, a new dynamic auto-regressive partial least squares (DAPLS) method is proposed to capture the auto-correlations of both process and quality variables as well as the cross-correlations between them. In DAPLS, quality-relevant dynamics are exploited by maximizing the covariance between current quality sample and the weighted combinations of both past process and quality samples. Its inner modeling objective is also explicit and consistent with its outer model. The case studies with the numerical simulations and the Tennessee Eastman process have demonstrated the effectiveness of the proposed model.

Keywords: Data-driven Control, Machine Learning and Data Mining, Biological Systems
Abstract: This paper studies the robustness of reinforcement learning for discrete-time linear stochastic systems with multiplicative noise evolving in continuous state and action spaces. As one of the popular methods in reinforcement learning, the robustness of policy iteration is a longstanding open issue for the stochastic linear quadratic regulator (LQR) problem with multiplicative noise. A solution in the spirit of small-disturbance input-to-state stability is given, guaranteeing that the solutions of the policy iteration algorithm are bounded and enter a small neighborhood of the optimal solution, whenever the error in each iteration is bounded and small. In addition, a novel off-policy multiple-trajectory optimistic least-squares policy iteration algorithm is proposed, to learn a near-optimal solution of the stochastic LQR problem directly from online input/state data, without explicitly identifying the system matrices. The efficacy of the proposed algorithm is supported by rigorous convergence analysis and numerical results on a second-order example.

Keywords: Identification for Control, Data-driven Control, Nonparametric Methods
Abstract: There is a steadily increasing demand for full and partial autonomous operation of systems. One way to achieve autonomy for systems is the fusion of classical control approaches with methods from machine learning and articial intelligence. We consider machine learning approaches to learn unknown or partially known references to increase the autonomy and performance of control systems for reference trajectory tracking. To improve learning and provide guarantees, we incorporate system properties such as constraints and the system dynamics in the learning algorithm. In particular, Gaussian processes are used to support a model predictive control scheme that exploits the predicted -learned- reference. Recursive feasibility and stability is established, and improved performance is illustrated considering a chemical process and asymptotically constant references.

Keywords: Machine Learning and Data Mining, Automotive Systems
Abstract: Traffic data are a key element for setting up scenarios for Advanced Driver Assistant Systems (ADAS) safety and performance testing. Testing will thus reflect in some way the data used. However, there is no clear understanding in which way and how to choose the data so that the evaluation results are reliable and comprehensive. Therefore, the important scenarios in a traffic data set in view of safety analysis have to be determined. The paper presents a method with which traffic situations from a given data set are classified into different safety classes according to easily measurable features. It is shown that taking the Time To Collision (TTC) as a measure of safety and a linear Support Vector Machine (SVM) as a classifier, 64.7% of traffic situations of a validation data set were classified to the correct safety class considering only three measurable features. Thus, traffic situations from a data set can be classified fast into different safety categories, providing information to the ADAS tester if the developed device has been tested in a safe or unsafe environment.

Keywords: Machine Learning and Data Mining, Automotive Systems
Abstract: Models of traction batteries are an essential tool throughout the development of automotive drivetrains. Surprisingly, today's massively collected battery data is not yet used for more accurate and reliable simulations. Primarily, the non-uniform excitation during regular battery operations prevent a consequent utilization of such measurements. Hence, there is a need for methods which enable robust models based on large datasets. For that reason, a data-driven error model is introduced enhancing an existing physically motivated model. A neural network compensates the existing dynamic error and is further limited based on a description of the underlying data. This paper tries to verify the effectiveness and robustness of the general setup and additionally evaluates a one-class support vector machine as the proposed model for the training data distribution. For validation purposes, five datasets at the boundary of the priorly known input data space are selected. Based on a five datasets it is shown, that gradually limiting the data-driven error compensation outside the boundary leads to a similar improvement and an increased overall robustness.

Keywords: Fault Detection and Diagnosis, Neural Networks, Machine Learning and Data Mining
Abstract: The purpose of this paper is to evaluate the virtue of deep neural networks in detecting incipient failures of transformers, in particular windings looseness, via vibration data analysis. The transformer vibration technique is a non-invasive method to monitor winding looseness. It is based on the analysis of vibration spectra measured by sensors located on the transformer tank. In this paper, we rely on measurements that have been made in a dedicated lab under two different conditions: in presence or in absence of the clamping pressure on the windings. The analysis of data, oriented to fault detection, is performed by feedforward neural networks which, by experimental results, proved effective for a reliable prediction. Special emphasis is given to the robustness of the prediction to sensor misplacement and various techniques are carried out to evaluate and to enforce generalization to out-of-sample-data for the obtained classifier.

Keywords: Fault Detection and Diagnosis, Continuous Time System Estimation, Process Control
Abstract: A model-based method for the detection and estimation of faults in dynamic systems is proposed. The method is based on the combination of the parity space approach and the modulating function framework for estimation. The parity space method is employed as an efficient geometric procedure determining null subspaces for annihilating unknown terms and formulating residuals. With the modulating functions technique the dynamic relation from output differentiation is reformulated as an algebraic expression. This substantially reduces the noise sensitivity of the output derivatives required. The design allows for the robust fault detection and isolation also for some nonlinear systems. The robustness of the approach is demonstrated on a nonlinear model of a four-tank process.

Keywords: Fault Detection and Diagnosis, Biomedical Systems, Time Series
Abstract: Continuous Glucose Monitoring (CGM) sensors are key components in an artificial pancreas, an emerging tool for type 1 diabetes treatment. Malfunctioning of this component might reduce the efficacy of glucose control achieved by the system and even pose the safety of the patient at risk. Therefore, accurate and prompt detection of these anomalies is an important problem. This paper investigates a model-based method to detect CGM failures. Based on an individualized linear model of the subject, identified on hystorical data, the method predicts future glucose concentration through a one-step ahead Kalman predictor. The correct functioning of the system is then monitored using two different criteria: the first checks the magnitude of prediction residuals. The second checks the whiteness of the residuals through a correlogram test. The effectiveness of the two criteria is investigated and compared by performing tests on an in-silico dataset obtained by means of UVA/Padova Type 1 Diabetes simulator, accepted by the US Food and Drug Administration as a substitute of animal testing prior to artificial pancreas clinical trials on humans.

Keywords: Fault Detection and Diagnosis, Parameter Estimation, Nonlinear System Identification
Abstract: This paper addresses the problem of prognostics based on Joint Estimation of States and Parameters (JESP) using a zonotopic Linear Parameter-Varying (LPV) set-membership approach. The aim of the prognostics is to forecast the Remaining Useful Life (RUL) of systems with degraded components. Thus, this paper suggests transforming such systems with nonlinear dynamical models into an LPV representation. Hence, a Zonotopic Set-Membership (ZSM) observer has been proposed to carry out the JESP for degradation assessment with a considerable focus on multiple-output systems. Despite the existence of different geometrical shapes, zonotopes have gained recently a lot of popularity due to their low computational complexity, in addition to the possibility of relating them with stochastic approaches. Moreover, an optimal correcting gain has been designed based on Linear Matrix Inequality (LMI) for robust observer tuning. Additionally, the LMI-based tuning can be solved offline with reduced computational effort while coping with a polytopic LPV representation of the model. Consequently, a Recursive ZSM (RZSM) approach has been employed for degradation prediction and guaranteed zonotopic RUL forecasting. Finally, the proposed approach is assessed using a degraded power electronics system, and the results are illustrated.

Keywords: Fault Detection and Diagnosis, Time Series, Mechanical and Aerospace
Abstract: Early signs of mechanical characteristic changes are essential for structural health monitoring (SHM). Due to the complexity of civil, mechanical or aeronautical structures, SHM is often faced with high dimensional mechanical characteristics together with limited sensor instrumentation. In this paper, Lasso regression is applied to address this complexity issue, based on its ability for solving large regression problems. The mechanical vibration model is first appropriately transformed into a linear regression model, with its parameters corresponding to small changes in the monitored mechanical characteristics, then these parameters are estimated from mechanical sensor signals under the assumption that most of the parameters are zeros. The performance of the proposed method is illustrated with a simulated truss structure.

Keywords: Fault Detection and Diagnosis, Filtering and Smoothing, Automotive Systems
Abstract: This paper deals with a sensor fault detection approach using the Optimal Upper Bounded Interval Kalman Filter (OUBIKF) and an adaptive degrees of freedom Chi^2-statistics method. It is devoted to discrete time linear model subjected to mixed uncertainties in terms of observations and noises. Mixed uncertainties mean both bounded and stochastic uncertainties. The degrees of freedom of this Chi^2 hypothesis test method are adaptively chosen thanks to amplifier coefficients improving the detection of the sensor faults. The proposed approach is an extension of a result developped in Lu et al. in 2019. Application on a vehicle bicycle model highlights the efficiency of the proposed approach. Comparisons with other efficient estimation and fault detection strategies are provided to discuss the accuracy of the obtained results.

Keywords: Errors in Variables Identification, Parameter Estimation, Bounded Error Identification
Abstract: Sparse linear system identification can be performed through convex optimization, by the minimization of an l1-norm functional. If an errors-in-variables model is considered, the problem is more challenging as inherently non-convex. The l1-norm approach for the errors-in-variables model is studied in recent literature. In this work, we propose to replace the l1-norm functional by a concave functional. Concave functionals have been shown to improve the performance in practical experiments of sparse linear regression; nevertheless, theoretical analyses of this improvement are missing in the errors-in-variables setting. The goal of this paper is to fill this gap, by studying conditions that guarantee that the concave approach is variable selection consistent. Moreover, we illustrate how to implement it through l1 reweighting techniques, and we present numerical simulations that show its effectiveness.

Keywords: Parameter Estimation, Model Validation, Monitoring
Abstract: The paper presents a combined state and parameter estimation for a landslide model using a Kalman filter. The model under investigation is based on underlying mechanics that depicts a landslide behavior. This system is described by an Ordinary Differential Equation (ODE) with displacement as a state and landslide geometrical and material properties as parameters. The Kalman filter approach is utilized on a simplified model equation for state and parameter estimation. Finally, the presented approach is validated by two illustrative examples, the first one a synthetic case study and the second one on Super-Sauze landslide data taken from the literature.

Keywords: Parameter Estimation, Grey Box Modelling, Mechanical and Aerospace
Abstract: This paper addresses the parameter estimation issue on mobile robots. A comparison between the state-of-art Least-Squares Technique and the potentially useful Instrumental Variable Method is carried out. With that objective, the whole process of kinematic and dynamic modeling, exciting trajectory design, simulation, parameter identification and cross-validation is done. The results are exhibit in simulation of a Differential Drive Mobile Robot to show that against possible noises and perturbations on mobile robotics, due to effects as slipping and not perfect rolling, Instrumental Variable is more suitable. As a use case, a mobile robot with an unbalanced mass distribution is modeled, identified and validated in simulation.

Keywords: Identifiability, Parameter Estimation
Abstract: This paper deals with identification of noise covariance matrices of a dynamic system described by a linear discrete-in-time time-invariant stochastic state-space model. In particular, the parametric identifiability of the correlations methods is analysed and explicit relations for determination of a number of identifiable noise covariance matrices parameters are stated. The theoretical results are thoroughly discussed and illustrated.

Keywords: Basis Functions, Maximum Likelihood Methods
Abstract: This article presents a discrete-time identification algorithm able to characterize multiscale systems. It belongs to the pseudo-linear regression class, and uses a model parameterization established on generalized bases of orthonormal transfer functions. An analysis of the estimated parameters accuracy is provided, and in order to tend toward statistical efficiency, an iterative procedure of the basis poles selection is proposed. Simulation examples show the interest of the approach.

Keywords: Identification for Control, Parameter Estimation, Biomedical Systems
Abstract: Anemia is the condition in which patients suffer from the deficiency of the red blood cells to carry the oxygen in the body. One of the many causes of anemia is chronic kidney disease (CKD). CKD is a disease in which kidneys are partially or completely damaged, which results in a deficiency of the oxygen-carrying red blood cells. CKD is common in older people, and external human recombinant erythropoietin (EPO) is required to maintain healthy levels of hemoglobin (Hb). In order to effectively address the impact of inter and intra-individual variability in dose-response characteristics in CKD patients, individualized patient-specific models are required instead of traditional population-based models. In this research, individualized patient models are developed by using patient-specific time-domain data with robust system identification techniques. For control-oriented system identification, two robust identification techniques are investigated: (1) l1 robust identification considering zero initial conditions and (2) semi-blind robust system identification considering non-zero initial conditions. The performance of these two techniques is compared and it is shown that the semi-blind robust identification technique gives better results as compared to l1 robust identification.

Keywords: Filtering and Smoothing, Recursive Identification
Abstract: The boundedness of the Kalman filter, as the first cornerstone of its stability analysis, has been proved in the classical literature through upper bounds of non-recursive filters in the sense of the trace of the state estimation error covariance. In this paper, an upper bound of the Kalman filter prediction error covariance is established in the sense of matrix positive definiteness, based on a bounded recursive non-optimal filter. The boundedness of the error covariance is a prerequisite for the definition of a Lyapunov function involved in the state estimation error dynamics stability analysis.

Keywords: Continuous Time System Estimation, Recursive Identification
Abstract: In this paper, we propose an adaptive identification method for continuous-time systems with slowly time-varying parameters subject to infrequent abrupt changes using sampled data in the presence of measurement noise. The proposed estimator consists of two steps. First, the time-derivatives of the input and output signals are approximated through a combination of a Kalman filter and fixed-interval smoother. The parameters of the time-varying system are then estimated using the approximate time-derivatives by two Kalman filters running forward and backward in time to track the abrupt changes. The performance of the proposed estimator is verified through simulation studies where it is shown to perform better than an existing direct continuous-time estimator.

Keywords: Recursive Identification, Multivariable System Identification
Abstract: Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

Keywords: Recursive Identification, Basis Functions, Other Applications
Abstract: Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state of the art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application - identification of fast time-varying acoustic channels used in underwater communication.

Keywords: Recursive Identification, Multivariable System Identification, Nonparametric Methods
Abstract: This work develops a recursive algorithm to estimate a given size sequence of Markov parameters for linear discrete-time systems, which is related to FIR models estimation. The discussion on FIR models in identification literature tends to be brief due to its poor prediction error for low order models, although Markov parameter sequence of shorter length can be used, e.g., as the input for data-driven MPC based on FIR models and for system identification combined with realization theory. Estimation of Markov parameters sequence of larger length can also be used in applications in which the prediction itself is not relevant, such as stability assessment or norm computations. The formulation is derived for SISO systems and then we extended it to the MIMO case. An analysis of the overall truncation and bias errors is also developed and illustrative examples are given to highlight the method's performance. In the examples we also further illustrate the difference in estimation results for different inputs, since the input choice is affected by the identification method utilised.

Keywords: Errors in Variables Identification, Recursive Identification, Algorithms
Abstract: The identification of Errors-in-variables (EIV) models refers to systems where the available measurements of their inputs and outputs are corrupted by additive noise. A large variety of solutions are available when dealing with this estimation problem, in particular when the corrupting noises are white processes. However, the number of available solutions decreases when the output noise is assumed as a colored process, which is a case of great practical interest. On the other hand, many applications require estimation algorithms to work on-line, tracking a dynamical system behavior for control, signal processing, or diagnosis. In many cases, they even have to take into account computational constraints. In this paper, we propose an estimation method that is able to both lay out an algorithm to solve the identification problem of EIV systems with arbitrarily correlated output noise and also provide an efficient recursive version that does not make use of variable size matrix inversions.

Keywords: Toolboxes, Algorithms, Machine Learning and Data Mining
Abstract: The R2021b release of the System Identification Toolbox™ for MATLAB® contains new features that enable the use of machine learning techniques for nonlinear system identification. With this release it is possible to build nonlinear ARX models with regression tree ensemble and Gaussian process regression mapping functions. The release contains several other enhancements including, but not limited to, (a) online state estimation using the extended Kalman filter and the unscented Kalman filter with code generation capability; (b) improved handling of initial conditions for transfer functions and polynomial models; (c) a new architecture of nonlinear black-box models that streamlines regressor handling, reduces memory footprint and improves numerical accuracy; and (d) easy incorporation of identification apps in teaching tools and interactive examples by leveraging the Live Editor tasks of MATLAB.

Keywords: Nonlinear System Identification, Toolboxes
Abstract: This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a wide range of nonlinear systems. An illustration is provided on experimental data of an electrical system mimicking the forced Duffing oscillator, and on numerical data of a nonlinear fluid dynamics problem.

Keywords: Nonlinear System Identification, Toolboxes, Multivariable System Identification
Abstract: Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

Keywords: Nonlinear System Identification, Toolboxes
Abstract: This paper describes the LPVcore software package for MATLAB developed to model, simulate, estimate and control systems via linear parameter-varying (LPV) input-output (IO), state-space (SS) and linear fractional (LFR) representations. In the LPVcore toolbox, basis affine parameter-varying matrix functions are implemented to enable users to represent LPV systems in a global setting, i.e., for time-varying scheduling trajectories. This is a key difference compared to other software suites that use a grid or only LFR-based representations. The paper contains an overview of functions in the toolbox to simulate and identify IO, SS and LFR representations. Based on various prediction-error minimization methods, a comprehensive example is given on the identification of a DC motor with an unbalanced disc, demonstrating the capabilities of the toolbox. The software and examples are available on www.lpvcore.net.

Keywords: Toolboxes, Parameter Estimation, Model Validation
Abstract: This paper describes new additions to the Rapid Parameter Identification Toolbox (RaPId), which is an open-source MATLAB toolbox for parameter estimation using models developed with the Modelica language and exported with the functional mock-up interface (FMI) Standard. These additions include an updated graphical user interface (GUI), an optimization method utilizing multiple starting points for a gradient descent optimization, and examples for different cyber-physical system applications such as the Duffing-Holmes equation modeling in a form of electrical circuit and a hydroelectric power plant modeling.

Keywords: Toolboxes, Continuous Time System Estimation
Abstract: The main purpose of this contribution is to describe the new features of the latest version 7.4 of the CONtinuous-Time System IDentification (CONTSID) toolbox for Matlab. The main addition is a new Graphical User Interface (GUI), which allows the user in a friendly and easy way to perform data analysis, model parameter estimation as well as model validation. The recent additions for MISO time-delay transfer function model identification are also briefly introduced.

Keywords: Toolboxes, Frequency Domain Identification, Subspace Methods
Abstract: An open source toolbox, FSID, implemented in the Python, Julia and Matlab programming languages is described. The toolbox provides scripts which estimates linear multi-input multi-output state-space models from sample data using frequency-domain subspace algorithms. Algorithms which estimate models based on samples of the transfer function matrix as well as frequency domain input and output vectors are provided. The algorithms can be used for discrete-time models, continuous-time models as well as for approximation of rational matrices from samples corresponding to arbitrary points in the complex plane. The algorithms can handle frequency dependent weighting which enable to obtain approximate BLUE estimates. To reduce the computational complexity for the estimation algorithms, an accelerated algorithm is provided which evaluate the state-space realization of the transfer function matrix at arbitrary points.

Keywords: Toolboxes, Algorithms, Biological Systems
Abstract: Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze time-series data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling,implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.