H. Hjalmarsson

Latest

• Linear Prediction Error Methods for Stochastic Nonlinear Models
• Bayesian nonparametric estimation of Wiener systems
• Identification of linear models from quantized data: a midpoint-projection approach
• Learning robust LQ-controllers using application oriented exploration
• Modeling and identification of uncertain-input systems
• Nonlinear System Identification Using Optimal Estimating Functions
• Parametric Identification Using Weighted Null-Space Fitting
• Performance analysis of Iterative Feedback Tuning
• The Weighted Null-Space Fitting Method for Identification of Multivariate Model Structures
• A Least Squares Method for Identification of Feedback Cascade Systems
• A Sequential Least Squares Algorithm for ARMAX Dynamic Network Identification
• A benchmark for data-based office modeling: challenges related to CO$_2$ dynamics
• A graph theoretical approach to input design for identification of nonlinear dynamical models
• A kernel-based approach to Hammerstein system identification
• A new kernel-based approach to system identification with quantized output data
• A weighted least-squares method for parameter estimation in structured models
• Advanced Autonomous Model-Based Operation of Industrial Process Systems (AutoProfit): Technological Developments and Future Perspectives
• Analysis of two methods for nonparametric Frequency Response Function identification
• Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem
• Application-Oriented Input Design in System Identification Optimal input design for control
• Applications Oriented Input Design for Closed-Loop System Identification: a Graph-Theory Approach
• Approximate Maximum-Likelihood Identification of Linear Systems from Quantized Measurements
• Bayesian kernel-based system identification with quantized output data
• Blind identification strategies for room occupancy estimation
• Blind system identification using kernel-based methods
• Blind system identification using kernel-based methods
• EM-based Hyperparameter Optimization for Regularized Volterra Kernel Estimation
• Identification of a Class of Nonlinear Dynamical Networks
• Nonlinear FIR Identification with Model Order Reduction Steiglitz-McBride
• On the Effect of Noise Correlation in Parameter Identification of SIMO Systems
• On the variance of identified SIMO systems with spatially correlated output noise
• Uncertainty in system identification: learning from the theory of risk
• Variance Analysis of Linear SIMO Models with Spatially Correlated Noise
• Weighted Null-Space Fitting for Identification of Cascade Networks
• A simulated annealing approach to exact experiment design for dynamical systems
• Application Set Approximation in Optimal Input Design for Model Predictive Control
• Applications Oriented Input Design in Time-Domain Through Cyclic Methods
• Experiment design for parameter estimation in nonlinear systems based on multilevel excitation
• Input Signal Generation for Constrained Multiple-Input Multple-Output Systems
• Iterative Data-Driven $H_ınfty$ Norm Estimation of Multivariable Systems with Application to Robust Active Vibration Isolation
• Optimal Experiment Design in Closed Loop with Unknown Nonlinear or Implicit Controllers using Stealth Identification
• Outlier robust system identification: a Bayesian kernel-based approach
• Variance Results for Parallel Cascade Serial Systems
• Analysis of TCP/IP over WCDMA Wireless Systems under Power Control, MAI and Link Level Error Recovery
• Applications of mixed $mathcal H_ınfty$ and $mathcal H_2$ input design in identification
• Validation of stability for an induction machine drive using power iterations
• From experiments to closed loop control
• Randomized Iterative Feedback Tuning
• Tuning for robustness and performance using Iterative Feedback Tuning
• Convergence and Asymptotic Efficiency of the Box-Jenkins Stegilitz McBride Method: The Open Loop Case
• Distributed Signal Recovery Via Piecewise Orthogonal Matching Pursuit and Finite-Time Consensus
• Least Costly Closed-loop Performance Diagnosis and Plant Re-identication
• Least Squares End Performance Experiment Design in Multicarrier Systems: The Sparse Preamble Case
• Piecewise Toeplitz Matrices-based Sensing for Rank Minimization
• Training Sequence Design for MIMO Channels: An Application-Oriented Approach
• Identification for Control of Multivariable Systems: Controller Validation and Experiment Design via LMIs
• A General Framework for Iterative Learning Control
• Accurate quantification of variance error
• Closed loop identification of unstable poles and non-minimum phase zeros
• Exact Quantification of variance error
• From open loop learning to closed loop control
• Identification of non-minimum phase zeros in open and closed loop
• Identification of performance limitations using ARX models
• Identification of performance limitations using general SISO structures
• Input design for identification of zeros
• Model Based Design Variables for Iterative Learning Control of Nonlinear Systems
• On direct estimation of physical parameters in nonlinear models
• On explicit characterisation of reproducing kernels with applications in estimation theory
• On frequency Domain Accuracy of Closed Loop Estimates
• On methods for gradient estimation in IFT for MIMO systems
• Optimal experiment design in closed loop
• The Analysis of Variance Error Part I: Fundamental Principles
• The Analysis of Variance Error Part II: Accurate Quantification
• Unbiased bandwidth estimation in communication protocols
• Using a sufficient condition to analyze the interplay between identification and control
• Using local and global information in identification for control
• Efficient Tuning of Linear Multivariable Controllers Using Iterative Feedback Tuning
• Model-free Tuning of a Robust Regulator for a Flexible Transmission System
• Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting
• Stability and Performance Analysis of Control Based on Incomplete Models
• Toward Tractable Global Solutions to Maximum-Likelihood Estimation Problems via Sparse Sum-of-Squares Relaxations
• Analysis of averages over distributions of Markov processes
• Approximate Maximum-Likelihood Identification of Linear Systems Weighted Null-Space Fitting for Cascade Networks with Arbitrary Location of Sensors and Excitation Signals
• Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors
• Outlier-robust estimation of uncertain-input systems with applications to nonparametric FIR and Hammerstein models
• Semi-parametric kernel-based identification of Wiener systems
• Outlier-robust estimation of uncertain-input systems with applications to nonparametric FIR and Hammerstein models
• An empirical Bayes approach to identification of modules in dynamic networks
• Open-loop asymptotically efficient model reduction with the Steiglitz-McBride method
• Optimal identification experiment design for the interconnection of locally controlled systems
• Hierarchical Robust Analysis for Identified Systems in Network
• Recursive Identification Based on Weighted Null-Space Fitting
• A nonparametric kernel-based approach to Hammerstein system identification
• On anti-aliasing filtering and oversampling scheme in system identification
• Approximate inference of nonparametric Hammerstein models
• Incorporating noise modeling in dynamic networks using non-parametric models
• On maximum likelihood identification of errors-in-variables models
• Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models
• Variational Bayes identification of acyclic dynamic networks
• Adaptive Experiment Design for LTI systems
• Covariance Analysis in SISO Linear Systems Identification
• Cost function shaping of the output error criterion
• A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems
• A Weighted Least Squares Method for Estimation of Unstable Systems
• Identification of Modules in Dynamic Networks: An Empirical Bayes Approach
• Kernel-Based System Identification from Noisy and Incomplete Input-Output Data
• The Transient Impulse Response Modeling Method for Non-parametric System Identification
• An application-oriented approach to dual control with excitation for closed-loop identification
• Robust EM kernel-based methods for linear system identification
• Generation of signals with specified seond order properties for constrained systems
• Piecewise sparse signal recovery via piecewise orthogonal matching pursuit
• The Box-Jenkins Steiglitz McBride Method
• A new kernel-based approach for overparameterized Hammerstein system identification
• Multi-room occupancy estimation through adaptive gray-box models
• On Estimating Initial Conditions in Unstructured Models
• On the Variance Analysis of identified Linear MIMO Models
• On the estimation of initial conditions in kernel-based system identification
• Outlier robust kernel-based system identification using l1-Laplace techniques
• A Multi-Time-Scale Generalization of Recursive Identification Algorithm for ARMAX Systems
• Almost sure stability and stabilization of discrete-time stochastic systems
• Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer
• The conservation of information, towards an axiomatized modular modeling approach to congestion control
• Re-weighting, Regularization Selection, and Transient in Nuclear Norm based Identification
• Sparse estimation of polynomial and rational dynamical models
• Variance Analysis of Identified Linear MISO Models Having Spatially Correlated Inputs, With Application to Parallel Hammerstein Models
• Input design as a tool to improve the convergence of PEM
• A Note on the SPICE Method
• Frequency smoothing gains in preamble-based channel estimation for multicarrier systems
• Application-Oriented Least Squares Experiment Design in Multicarrier Communication Systems
• A Geometric Approach to Variance Analysis of Cascaded Systems
• A Sparse Estimation Technique for General Model Structures
• Generation of excitation signals with prescribed autocorrelation for input and output constrained systems
• Iteratively Learning the $H_ınfty$-Norm of Multivariable Systems Applied to Model-Error-Modeling of a Vibration Isolation System
• Model predictive control with integrated experiment design for Output Error Systems
• Optimal input design for non-linear dynamic systems: a graph theory approach
• Providing improvements in relation to a model of a process
• Robust and Adaptive Excitation Signal Generation for Input and Output Constrained Systems
• Analyzing Iterations in Identification with Application to Nonparametric $H_ınfty$-norm Estimation
• Finite model order accuracy in Hammerstein model estimation
• Recursive estimators with Markovian jumps
• Accuracy of Linear multiple-input multiple-output (MIMO) Models obtained by Maximum Likelihood Estimation
• On the Performance of Optimal Input Signals for Frequency Response Estimation
• A Chernoff Relaxation on the Problem of Application-Oriented Finite Sample Experiment Design
• A Mathematica Toolbox for Signals, Systems and Identification
• A Tutorial on Applications-Oriented Optimal Experiment Design
• A Unified Experiment Design Framework for Detection and Identification in Closed-Loop Performance Diagnosis
• Adaptive experiment design for ARMAX systems
• Application-Oriented Finite Sample Experiment Design: A Semidefinite Relaxation Approach
• Correlation of Distortion Noise Between the Branches of MIMO Transmit Antennas
• Identification for Automotive Systems
• Identification of Box-Jenkins models using structured ARX models and nuclear norm relaxation
• Mean-squared error experiment design for linear regression models
• Non-parametric Frequency Function Estimation using Transient Impulse Response Modelling
• On the convergence of the prediction error method to its global minimum
• Order and Structural Dependence Selection of LPV-ARX Models Revisited
• Preface to System identification: A Wiener-Hammerstein benchmark
• Robust Experiment Design for System Identification via Semi-Infinite Programming Techniques
• Sparse Estimation Techniques for Basis Function Selection in Wideband System Identification
• Sparse Estimation of Rational Dynamical Models
• System Identification for Automotive Systems: Opportunities and Challenges
• The Transient Impulse Response Modeling Method and the Local Polynomial Method for nonparametric system identification
• A Design Algorithm using External Perturbation to Improve Iterative Feedback Tuning Convergence
• On the accuracy in errors-in-variables identification compared to prediction-error identification
• The Cost of Complexity in System Identification: The Output Error Case
• Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response
• An adaptive method for consistent estimation of real-valued non-minimum zeros in stable LTI systems
• Conditions when minimum variance control is the optimal experiment for identifying a minimum variance controller
• A Least Squares Approach to Direct Frequency Response Estimation
• An axiomatic fluid-flow model for congestion control analysis
• Analyzing Iterations in Identification with Application to Nonparametric $mathcalH_infty$-norm Estimation
• Cascade and multibatch subspace system identification for multivariate vacuum-plasma response characterisation
• Chance Constrained Input Design
• Four encounters with System identification
• Input design using cylindrical algebraic decomposition
• MPC oriented experiment design
• On Optimal Input Design for Model Predictive Control
• On estimation of the gain of a dynamical system
• Optimal experiment design for hypothesis testing applied to functional magnetic resonance imaging
• Sparse estimation based on a validation criterion
• Queue Dynamics with Window Flow Control
• System identification of complex and structured systems. Parts I and II
• Identification for robust $H_2$ deconvolution filtering
• Closed-Loop MIMO ARX Estimation of Concurrent External Plasma Response Eigenmodes in Magnetic Confinement Fusion
• Identification of Nonlinear Systems Using Misspecified Predictors
• MPC oriented experiment design
• Non-parametric methods for $L_2$-Gain Estimation using Iterative Experiments
• Nonlinear State-Dependent Delay Modeling and Stability Analysis of Internet Congestion Control
• On Optimal Input Design for Nonlinear FIR-Type Systems
• The Cost of Complexity in System Identification: Frequency Function Estimation of Finite Impulse Response Systems
• A System, Signals and Identification Toolbox in Mathematica with Symbolic Capabilities
• Consistent estimation of real NMP zeros in stable LTI systems of arbitrary complexity
• Data-Driven Methods for $L_2$-Gain Estimation
• Optimal input design for robust $H_2$ deconvolution filtering
• Tuning of dissolved oxygen and pH PID control parameters in large scale bioreactor by lag control
• An Improved Link Model for Window Flow Control and Its Application to FAST TCP
• Improving TCP Performance During the LTE Handover
• Input Design for Asymptotic Robust $H_2$-Filtering
• MIMO Experiment Design based on Asymptotic Model Order Theory
• On Performance Limitations of Congestion Control
• System identification of complex and structured systems
• System identification of complex and structured systems
• Variance Results for Identification of Cascade Systems
• Vector dither experiment design and direct parametric identification of reversed-field pinch normal modes
• Variance Analysis of Identification of Cascade Systems
• ACK-Clocking Dynamics: Modeling the Interaction between ACK-Clock and Network
• On identification of cascade systems
• Window flow control: Macroscopic properties from microscopic factors
• A Geometric Approach to Variance Analysis in System Identification: Linear Time-Invariant Systems
• A Geometric Approach to Variance Analysis in System Identification: Theory and Nonlinear Systems
• Prediction of Engine Noise using Parameterized Combustion Pressure Curves
• ACK-clock Dynamics in Network Congestion Control -- An Inner Feedback Loop with Implications on Inelastic Flow Impact
• A control perspective on optimal input design in system identification
• Conclusions of the ARTIST2 Roadmap on Control of Computing Systems
• Conclusions from the European Roadmap on Control of Computing Systems
• Input Design via LMIs Admitting Frequency-wise Model Specifications in Confidence Regions
• Numerical Conditioning
• Variance Error, Reproducing Kernels and Orhonormal Bases
• Application of mixed $mathcal H_ınfty$ and $mathcal H_2$ input design to identification for control
• Variance Error Quantifications that are Exact for Finite Model Order
• Relay auto-tuning of PID controllers using iterative feedback tuning
• Iterative Feedback Tuning --An overview
• On Router Control for Congestion Avoidance
• Randomization methods in optimization and adaptive control
• Signalteori
• Model Structure and Numerical Properties of Normal Equations
• Identification of Performance Limitations in Control
• Robust loopshaping using Iterative Feedback Tuning
• Spectral Matching for parameter estimation in nonlinear input-output models
• Improved and quantified accuracy for linear spectral estimates
• Optimal Input Design Using Linear Matrix Inequalities
• The fundamental role of general orthonormal bases in system identification
• Asymptotic Variance Expressions for Output Error Model Structures
• Estimation Variance is not Model Structure Independent
• Iterative Feedback Tuning of controllers in cold rolling mills
• Maximum Likelihood Estimation of Models with Unstable Dynamics and Non-minimum Phase Noise Zeros
• Model Structure and Numerical Properties of Normal Equations
• Signal Spectra and Conditioning when using Orthonormal Parametrisation
• Tuning of controllers and generalized hold functions in sampled-data systems using Iterative Feedback Tuning
• Generalised Fourier and Toeplitz results for rational orthonormal bases
• Iterative Feedback Tuning: theory and applications
• Optimally Robust System Identification of Systems Subject to Amplitude Bounded Stochastic Disturbances
• Control of nonlinear systems using Iterative Feedback Tuning
• Fast non-iterative estimation of hidden Markov models
• Generalized Fourier and Toeplitz results for rational orthonormal bases.
• Identification in closed loop: Asymptotic high order variance for restricted complexity models
• Iterative Feedback Tuning
• Iterative Feedback Tuning of linear time-invariant MIMO systems
• Frequency domain expressions of the accuracy of a model free control design scheme
• Iterative Feedback Tuning: theory and applications in chemical process control
• Significance Regression: A Statistical Approach to Partial Least Squares
• For model based control design criteria, closed loop identification gives better performance
• On the choice of norms in system identification
• Modelling of Random Processes using Orthonormal Bases
• On Neural Network Model Structures in System Identification
• Optimal robust system identification: Bounded stochastic disturbances
• 21 ML estimators for model selection
• Composite Modeling of Transfer Functions
• Composite Modeling of Transfer Functions
• Model-Free Tuning of Controllers: Experience with Time-Varying Linear Systems
• Model-Free Tuning of a Robust Regulator for a Flexible Transmission System
• Nonlinear Black-Box Models in System Identification: Mathematical Foundations
• Nonlinear Black-Box Models in System Identification: a Unified Overview
• Optimality and Sub-optimality of Iterative Identification and Control Design Schemes
• System Identification through the eyes of Model Validation
• A Convergent Iterative Restricted Complexity Control Design Scheme
• A Unifying View of Disturbances in Identification
• Identification for control: Closing the loop gives more accurate controllers
• Neural Networks in System Identification
• Non-Vanishing Model Errors
• On the choice of norms i system identification
• The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
• The least-squares identification of FIR systems subject to worst-case noise
• A Discussion of "Unknown-but-Bounded" Disturbances in System Identification
• A Model Variance Estimator
• Aspects on Incomplete Modeling in System Identification
• Asymptotic Correct Correlation Tests in Model Validation
• Detecting Asymptotically Non-Vanishing Model Uncertainty
• Nineteen ML Estimators for Model Structure Selection
• Estimating Model Variance in the Case of Undermodeling
• An Invariance Principle for "reverse" Mixingales
• Asymptotic Relations Between Non-Weighted and Exponentially Weighted Series: A Functional Limit Approach
• Some Reflections on Control Design Based on Experimental Data
• Estimation of the variability of time-varying systems
• Model Quality: The Role of Prior Knowledge and Data Information
• How to estimate model uncertainty in the case of under-modelling
• On Estimation of Model Quality in System Identification