H.~Hjalmarsson

Latest

• Identication and Control: Joint Input Design and $H_ınfty$ State Feedback with Ellipsoidal Parametric Uncertainty via LMIs
• Identication and Control: Joint Input Design and $H_ınfty$ State Feedback with Ellipsoidal Parametric Uncertainty via LMIs
• Closed loop experiment design for linear time invariant dynamical systems via LMIs
• Closed loop experiment design for linear time invariant dynamical systems via LMIs
• Least-squares estimation of a class of frequency functions: A finite sample variance expression
• Variance Analysis in SISO Linear Systems Identification
• $L_2$ and $L_ınfty$ methods in identification
• Finite sample input design for linearly parameterized models subject to frequency domain constraints
• Making retransmission delays in wireless links friendlier to TCP
• Round trip time estimation in communication networks using adpative Kalman filtering
• Experiment design and identification for control
• A Geometric Approach to Variance Analysis in System Identification
• How to make bias and variance errors insensitive to system and model complexity in identification
• D3.1. Methodologies for identification and model management of large-scale systems
• Variance error quantifications for identified poles and zeros
• Finite Model Order Optimal Input Design for Minimum Variance Control
• Identification of ARX systems with non-stationary inputs - asymptotic analysis with application to adaptive input design
• A Geometric Approach to Variance Analysis in System Identification
• Variance Analysis in SISO Linear Systems Identification
• The cost of complexity in identification of FIR systems
• Adaptive input design for ARX systems
• Optimal Input Design for Identification of Non-Linear Systems: Learning from the Linear Case
• Closed Loop Aspects of Fluid Flow Model Identification in Congestion Control
• Gain Estimation for Hammerstein Systems
• Mixed $H_2$ and $H_ınfty$ Input Design for Multivariable Systems
• On Optimal Input Design in System Identification
• On Some Robustness Issues in Input Design
• Robust Input Design Using Sum of Squares Constraints
• Towards accurate congestion control models: Validation and stability analysis
• Adaptive input design in system identification
• Exact quantification of the variance of estimated zeros
• Identification and Control: Joint Input Design and $H_ınfty$ State Feedback with Ellipsoidal Parametric Uncertainty
• Local analysis of structural limitations of network congestion control
• From experiment design to closed loop control
• Analysis of the Variability of Joint Input-Output Estimation Methods
• On the Frequency Domain Accuracy of Closed Loop Estimates
• An exact finite sample variance expression for a class of frequency function estimates
• Convex computation of worst-case criteria with applications in identification and control
• Making retransmission delays in wireless links friendlier to TCP
• Mixed $mathcal H_ınfty$ and $mathcal H_2$ input design
• A framework for mixed $mathcal H_ınfty$ and $mathcal H_2$ input design
• Some Modeling and Estimation Issues in Control of Heterogeneous Networks
• Gradient approximations in Iterative Feedback Tuning of Multivariable Processes
• Identification of performance limitations using general SISO structures
• The Effect of Regularisation on Variance Error
• Variance Error Quantifications that are Exact for Finite Model Order
• Special section on algorithms and applications of Iterative Feedback Tuning
• Identification for control: $L_2$ and $L_ınfty$ methods
• Identification for control: Adaptive input design using convex optimization
• Iterative learning control of nonlinear non-minimum phase systems and its application to system and model inversion
• Control Relevant Identification of Nonlinear Systems using Linear Models
• Higher Order Cumulant based Parameter Estimation in Nonlinear Time Series Models
• Inversion of Nonlinear Stochastic Models for the Purpose of Parameter Estimation
• Inversion of nonlinear stochastic models for parameter estimation
• Spectral based parameter estimation in nonlinear stochastic models
• Parameter Estimation in Partitioned Nonlinear Stochastic Models
• Assessing Model Quality from Data